<h3>- Handles various calculations such as division column (vertical) calculations and decimals. 
Additional functions planned to include scoring and grade aggregation functions to contribute to reducing teachers' workload and enhancing educational administrative work - </h3>Tokyo, Japan - January 21, 2025 - Recursive Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter, "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is pleased to announce that it has developed an AI model that automatically evaluates whether the answers handwritten by children in their math workbooks are correct. This AI model was developed for the leading Japanese publisher of educational materials for elementary and middle schools, Kyoikudojinsha Co., Ltd. (hereafter "Kyoikudojinsha") as part of the joint efforts of Recursive and Kyoikudojinsha to contribute to the digitalization within the education and reduce teachers' workload.　When children write their answers by hand in the paper-based math workbooks for elementary schools provided by Kyoikudojinsha and then take a photo of their answers using their tablet cameras, the AI model evaluates whether the answers are correct or not. The AI model supports math problems from first to sixth grade of elementary school and can handle not only linear (horizontal) calculations, but also column (vertical) calculations and calculations involving decimals, which have conventionally been more technically challenging for AI to read and recognize.Checking the answers for paper-based educational materials has traditionally been time-consuming and labor-intensive. To address this, Kyoikudojinsha and Recursive are leveraging AI to reduce the workload, improve accuracy, digitize students' learning records, and make it easier for teachers to track students’ learning progress. As part of the first phase of this initiative, Kyoikudojinsha and Recursive have successfully developed a model that automatically evaluates whether students' answers are correct.As a result, students can check their own answers regardless of grade level, reducing the workload for teachers. This also allows teachers to focus on tasks that only they can perform, such as enriching the educational experience and providing more personalized guidance for each student, ultimately contributing to an improvement in the quality of education. Additionally, by transitioning paper-based materials to digital platforms, Kyoikudojinsha and Recursive aim to contribute to the effective use of educational data within Japan's education system.This AI model is scheduled for trial implementation at select public elementary schools in the first half of 2025, in cooperation with Kyoikudojinsha. The model was trademarked under the AI engine name "MITORU" on December 27, 2024, by Kyoikudojinsha. It is also currently under patent application by Kyoikudojinsha. With the goal of enhancing this technology and expanding its application, Recursive will continue to collaborate with Kyoikudojinsha to further develop and improve the functionality of this AI model.Background and current challenges
The education landscape in Japan has become increasingly complex and labor-intensive in recent years, with the demands on teachers changing significantly. As a result, long working hours for teachers have become the norm and have evolved into a serious social issue. In response, the Ministry of Education, Culture, Sports, Science and Technology (MEXT) is promoting reforms to improve teachers' work styles, and aiming to advance digital transformation (DX) in education. Specifically, the "GIGA School Program" (*1), Japan’s national policy aimed at improving ICT environments in schools, is progressing in its second phase, focusing on not only strengthening the infrastructure of devices and networks but also promoting the widespread use of learning support tools and digital teaching materials, which is expected to further enhance personalized learning. At the same time, the demand for paper-based educational materials remains high in Japan, particularly in elementary schools, where paper-based materials are often better suited for many subjects. As a result, teachers continue to spend significant time on grading and evaluating answers.Against this backdrop, Recursive was recognized by Kyoikudojinsha for its expertise in custom AI development and its alignment with the company's vision for advancing sustainable educational practices, which led to its selection as a development partner. Resonating with Kyoikudojinsha's commitment to not only publishing educational materials but also flexibly responding to the evolving needs of the educational landscape and supporting teachers in various ways, Recursive has embarked on developing an AI model that bridges paper-based materials with digital platforms.Overview of the project and technology
In light of this background, Recursive began customizing its in-house developed AI tools—“FindFlow” for document analysis and “PixelFlow” for analyzing and generating media like images—to develop an AI model that can automatically evaluate the correctness of students’ answers to math problems, as well as score and aggregate grades for elementary school students, specifically for Kyoikudojinsha. As the first step, Recursive developed a system that automatically evaluates whether students' handwritten answers are correct.In this AI model, when students take a photo of their handwritten answers on paper-based materials using a smartphone or tablet camera, the AI automatically recognizes the answers. Utilizing advanced object detection and OCR (optical character recognition) technology, the system converts handwritten numbers and symbols into text data, compares them against the correct answer data, and provides a rapid evaluation of whether they are correct.The AI model’s training leveraged a vast amount of educational material data accumulated by Kyoikudojinsha, enabling extensive learning to enhance its versatility. As a result, the model can now provide highly accurate and flexible evaluations, capable of assessing not only linear (horizontal) calculations and whole number calculations but also column (vertical) calculations and decimal calculations; areas that have traditionally posed significant technical challenges.Planned Future Enhancements
Recursive is also planning to support Kyoikudojinsha to expand the AI model’s evaluation scope to cover a wider range of elementary school math, such as incorporating an automatic grading aggregation feature that scores and grades paper-based materials, and connecting that to personalized learning as well as enabling the utilization of educational data in classrooms.Teachers play a crucial role in the growth of the next generation. Recursive will continue to contribute to the reform of teachers’ workstyles and the creation of a sustainable educational environment for both teachers and students through the development of this AI model and supporting its implementation in educational settings.<img src=/content/images/image_82fd04c5572b91b5eeb68ac050f4e6e1.png>Image of evaluation of the answers when capturing handwritten answers using a web application (1). <img src=/content/images/image_c62d7b26091a3b411cd89a7043509e9e.png>Image of evaluation of the answers when capturing handwritten answers using a web application (2). <img src=/content/images/image_4ed469825ae0ca3579b6d1ec48d3e58c.png>Image of evaluation of the answers when capturing handwritten answers using a web application (3). *1 GIGA School Initiative: GIGA stands for Global and Innovation Gateway for All. The initiative aims to establish an educational ICT environment that is fair, individually optimized, and ensures the development of students' abilities and potential, including those requiring special support. This is achieved through providing one device for each student and  building a high-speed, high-capacity communication network, ensuring that no child is left behind. The goal is to create an education system where all children, regardless of their needs, can thrive. (Source: Ministry of Education, Culture, Sports, Science and Technology (MEXT), "Overview and Issues Related to Digital Learning Infrastructure" (Draft), September 30, 2024).[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of Members: 47
Website:  https://recursiveai.co.jp/en/　[Media inquiries] 
Recursive Inc. 
Marika Kishimoto (Public Relations)
Email: marika.kishimoto@recursiveai.co.jp
Phone: +81-90-9847-7832

【Press Release】AI startup, Recursive, develops an AI model for Kyoikudojinsha, a Japanese publisher of educational materials: enables automatic evaluation of whether answers handwritten by children are correct 

<h3>- To first focus on Saudi Arabia, United Arab Emirates, Thailand, and Singapore as initial phase of expansion - </h3>TOKYO, Japan - December 12, 2024 - Recursive Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter referred to as "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is thrilled to announce its expansion into international markets, leveraging its cutting-edge AI technology to drive sustainable growth on a global scale. The initial phase of this expansion will focus on the Middle East and Southeast Asia, with a particular focus on Saudi Arabia, United Arab Emirates (UAE), Thailand, and Singapore. To lead its international efforts, Recursive has appointed Taylor Scobbie, an entrepreneur known for founding social impact startups that have served some of the world’s most influential organizations and academic institutions, as Director of Overseas. Under Taylor’s leadership, Recursive plans to accelerate its global growth while supporting key markets in their transition to more sustainable economies, with a specific focus on applications in the healthcare, energy, and logistics sectors. Recursive’s efforts to increase its international presence underscores its dedication to establish itself as a global leader in sustainable innovation.　　Background:
Since its founding in 2020, Recursive has built a strong foundation in Japan as it drove its mission to build a fairer, more sustainable society through innovative AI technology. Its state-of-the-art AI solutions have already been supporting some of Japan’s largest companies to advance sustainability initiatives, drive business transformation, and strengthen their business models. Its AI-powered solutions are already assisting some of Japan's largest companies in advancing sustainability initiatives, driving business transformation, and strengthening their business models.As Recursive approaches its fifth anniversary in 2025, it is set for international growth to amplify its impact worldwide. Saudi Arabia, UAE, Thailand, and Singapore have been strategically chosen as entry points for this expansion, as these nations are actively leveraging AI and other advanced technologies through ambitious national growth initiatives —such as Saudi Arabia’s Vision 2030, the UAE National Strategy for Artificial Intelligence 2031, Thailand 4.0 and AI Thailand, and Singapore’s National AI Strategy 2.0 (NAIS 2.0)— to drive sustainability, foster economic diversification, and build resilient digital economies.　Strategy:
Recursive will predominantly focus on supporting the healthcare, energy, and logistics sectors in Saudi Arabia, UAE, Thailand, and Singapore, where the company sees significant opportunities to drive innovation and sustainability. Leveraging its four technology platforms, Findflow (AI model for text generation, search, and analysis), PixelFlow (an AI model for media generation and analysis), Borealis (an AI that can generate highly precise models of the environment), and Zenith (an AI engine for optimization, forecasting, and recommendation) to build bespoke/semi-bespoke enterprise solutions, Recursive aims to accelerate innovation, enhance operational efficiency, and drive sustainable transformation. This involves supporting nations in transitioning away from a fossil fuel-dependent economy, assisting businesses in reducing carbon dioxide emissions, and ultimately improving citizens' quality of life by enabling cleaner, more efficient services while enhancing access to essential resources, including healthcare.Recursive aims to leverage the extensive experience and expertise gained from developing AI solutions across diverse industries in Japan such as environment, energy, healthcare, pharmaceuticals, food, and retail.　Profile of Taylor Scobbie
Taylor brings deep expertise in social impact and entrepreneurship to Recursive’s global expansion. After leading Trigger’s news aggregator app to #1 on the Apple USA App Store as Vice President of Marketing, he founded the social startup, IMPCT, during his MBA in 2014. As CEO, he used coffee brand profits to support children’s education in the developing world, earning IMPCT the $1M Hult Prize in 2015 as the world’s best new social enterprise. He later founded +Purpose, a platform that turns customers into impact investors through shopping, and served as its CEO for three years. Taylor joined Recursive in 2024 to lead its international growth.　　[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of Members: 47
Website:  https://recursiveai.co.jp/en/　[Media inquiries] 
Recursive Inc. Marika Kishimoto
Email: marika.kishimoto@recursiveai.co.jp
Phone: +81-90-9847-7832

【Press Release】AI Startup, Recursive, Expands into Overseas Markets,  Appoints Social Entrepreneur Taylor Scobbie as Director of Overseas

Recursive's blog has been featured on an online Japanese trade media for IT engineers and freelancers called Levtech Freelance.In the article titled "AI・データサイエンスを学ぶ！おすすめの厳選サイトまとめ” (Learn AI & Data Science! A List of Carefully Selected Recommended Sites), Levtech Freelance highlighted how Recursive's blog provides the latest insights on AI and sustainability, and how it offers valuable insights into shaping a future-oriented career.You can read the feature from below:■ Coverage details:
● Name of the publication: Levtech Freelance
● Publication date: December 6, 2024
● Article link: <a href=https://freelance.levtech.jp/guide/detail/61867/>https://freelance.levtech.jp/guide/detail/61867/</a> (Japanese only)

Recursive's Blog Featured in Online Japanese Trade Media for IT Engineers, Levtech Freelance

Recursive engaged in a discussion with the Japanese consulting firm Rise Consulting Group, Inc., on the topic of AI utilization and its future in the healthcare sector. The insights from this conversation have been featured on Rise Consulting's owned media platform, DIALOGUE.The development of specialty care pharmaceuticals, including treatments for cancer, rare diseases, and intractable illnesses, continues to pose significant challenges for Japan's pharmaceutical industry. However, advancements in AI and digital technologies offer promising solutions to address these hurdles. Recursive is also exploring the potential of AI applications within the healthcare sector.Tiago Ramalho, Co-founder and CEO of Recursive, and Takemasa Yuyama, Sales Director of Recursive, participated in the discussion with Osamu Takeguchi, Partner at Rise Consulting Group, and Jun Tsukahara, Manager at the same firm. The conversation covered a wide range of topics, including enhancing and streamlining drug development, utilizing AI in pharmaceutical sales and marketing activities, leveraging big data such as real-world data, and exploring AI's role in various healthcare applications.Tiago enthusiastically also spoke about the potential of Recursive's AI technology in providing personalized medicine and assisting with clinical trial documentation, emphasizing the feasibility and impact of these innovations. The discussion served as a platform to explore the possibilities of AI-driven innovations in the healthcare industry and shared a collective vision for the future.For more details, please visit Rise Consulting's website.▶ RISE TALK 3: [Recursive Inc.] The Future and AI Utilization in the Healthcare Sector
November 22, 2024
<a href=https://www.rise-cg.co.jp/digital-site/dialogue/risetalk_22/>https://www.rise-cg.co.jp/digital-site/dialogue/risetalk_22/</a> (Japanese only)

Recursive was Featured on Rise Consulting’s Website, Discussing the Utilization of AI in the Healthcare Sector

(Left to right) Ms. Hala Alshammari (Business Strategy Manager of SBI Middle East), Prof. Ahmed Al-Askar (Executive Director of KAIMRC), Katsutoshi Yamada (Co-Founder and COO of Recursive Inc.), Mr. Shiran Dias (CEO of SBI Middle East), Mr. Mohammed Abu Alola (Executive Director of Investment Advisory, SBI Middle East), Dr. Abdelali Haoudi (Director of the Biotechnology Park of KAIMRC)　<h3>- Aims to enhance screening and detection capabilities of TB by leveraging AI -</h3>　RIYADH, Saudi Arabia / TOKYO, Japan - November 28, 2024 - Recursive Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter referred to as "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is pleased to announce the signing of a Memorandum of Understanding (MoU) with King Abdullah International Medical Research Center (KAIMRC) — the largest medical research institution in the Kingdom of Saudi Arabia, a part of the Saud Bin Abdulaziz University for Health Sciences (KSAU-HS) and connected to Ministry of National Guard-Health Affairs, a nation-wide healthcare system with specialized hospitals and transplant centers and a network of primary to tertiary care hospitals across Saudi Arabia, — to jointly develop an advanced AI system for the early screening of tuberculosis (hereinafter referred to as “TB”; *1) .This partnership, formalized on November 11, 2024, during the Riyadh Global Medical Biotechnology Summit (#RGMBS2024) organized by the Ministry of National Guard with a strategic partnership of the Saudi Ministry of Investment, combines Recursive’s expertise and commitment to leverage AI to facilitate sustainability with KAIMRC’s commitment to innovative research to tackle infectious diseases. Recursive will support KAIMRC in developing an AI system that enhances the early detection of TB, improves the accuracy and speed of diagnosis, and accelerates research and prediction capabilities. Together, both organizations aim to contribute significantly to Saudi Arabia’s Vision 2030, which includes transforming the nation’s healthcare system and improving the quality of life for its citizens. This partnership also aligns with the United Nations Sustainable Development Goals (SDGs), particularly Goal 3, which focuses on ensuring healthy lives and promoting well-being for all at all ages.

BackgroundIn 2023, TB surged as the world’s top infectious disease killer, surpassing COVID-19. Globally, TB caused approximately 1.25 million deaths, and about 10.8 million people worldwide fell ill with TB, including 8.2 million people who were newly diagnosed with the disease. Among the 400,000 people estimated to have developed multidrug-resistant or rifampicin-resistant TB (MDR/RR-TB; *2); in 2023, only 44% were diagnosed and treated in 2023. Although TB is preventable and curable — with a treatment success rate of 68% for MDR/RR-TB patients (*3) — without timely medical intervention, it remains a fatal disease. As a result, ending the TB epidemic by 2030 has become a critical component of the United Nations Sustainable Development Goals under Goal 3: Ensure healthy lives and promote well-being for all at all ages.In addition, the Kingdom of Saudi Arabia End-TB strategy by 2035, part of the National Tuberculosis Program (NTP) in the Kingdom of Saudi Arabia, aims to decrease TB mortality and incidence rates by 95% and 90%, respectively, compared to 2015 levels (*4). Saudi Arabia is also pursuing an ambitious agenda for transforming the nation’s healthcare infrastructure through its Vision 2030 initiatives.　

Anticipated impact and visionThrough co-development efforts between KAIMRC scientists, Ministry of National Guard Health Affairs (MNGHA) physicians, and Recursive, chest X-ray imaging data will be utilized to enhance early screening and detection of TB. This collaborative approach aims to facilitate timely diagnosis and treatment, reducing the risk of mortality and preventing further transmission.This collaboration aims not only to enhance TB diagnosis within Saudi Arabia but also to create a scalable model that can be deployed in other regions with high TB burdens. Furthermore, these collaborative efforts aim to expand this AI-powered and data science approaches to address other infectious diseases, fostering innovation that strengthens global healthcare systems and drives broader public health advancements.“We are truly honored to partner with KAIMRC on this groundbreaking initiative,” said Tiago Ramalho, Co-Founder and CEO of Recursive Inc. “This collaboration embodies Recursive’s commitment to fostering a sustainable future by harnessing the power of cutting-edge AI technology. By combining KAIMRC’s pioneering medical research with our AI expertise, we are confident we can make a meaningful impact, not only in Saudi Arabia but also in regions worldwide that face the increasing challenge of TB and other infectious diseases.”Recursive will continue to develop and leverage AI technologies to tackle global challenges, driving innovation and advancing efforts to create a more sustainable world.
　
　
 *1 Tuberculosis: An infectious disease caused by bacteria that commonly affects the lungs and sometimes other organs such as the brain. Tuberculosis is spread through the air when people who have developed the disease cough, sneeze or spit.*2 Multidrug-resistant or rifampicin-resistant TB (MDR/RR-TB): Rifampicin is the most potent first-line anti-TB drug. MDR-TB is a more severe form of TB that is resistant to rifampicin and isoniazid.*3 All statistics from the Global Tuberculosis Report 2024, issued by the World Health Organization on October 29, 2024. <a href=https://iris.who.int/bitstream/handle/10665/379339/9789240101531-eng.pdf>https://iris.who.int/bitstream/handle/10665/379339/9789240101531-eng.pdf</a>*4 Based on the “National Tuberculosis Program Manual 2021” issued by the Ministry of Health of Kingdom of Saudi Arabia: <a href=https://www.moh.gov.sa/Documents/National-TB-program.pdf>https://www.moh.gov.sa/Documents/National-TB-program.pdf</a>
 
 [About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarters: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 44
Website: <a href=https://recursiveai.co.jp/en/>https://recursiveai.co.jp/en/</a>
 
[Media inquiries]
Recursive Inc.
Marika Kishimoto
Email: marika.kishimoto@recursiveai.co.jp
Phone: +81-90-9847-7832 (Direct number to Kishimoto)

【Press Release】AI Startup, Recursive, Signs Memorandum of Understanding with  Saudi Arabia’s Largest Medical Research Institution,  King Abdullah International Medical Research Center, to Develop AI and Data Science Solution for Tackling Tuberculosis

Tiago Ramalho, Co-Founder and CEO of Recursive, joined a panel of industry leaders at the annual Southeast Asia Trade and Development Forum hosted by the International Institute for Trade and Development (ITD). This year’s forum, “Trade Thrives, AI Drives,” took place in Bangkok, uniting top innovators to discuss AI's transformative effects on Southeast Asia’s economic landscape.During the panel discussion, Tiago emphasized AI’s role as a powerful tool for enhancing business efficiency, reducing environmental impact, and mitigating climate risk. “AI is not about replacing business processes but making them more efficient—less energy, fewer CO₂ emissions, better climate risk mitigation,” Tiago explained.<img src=/content/images/image_fc7d4c08fc2b70b7afaaaa56d38e0cbe.jpg>Tiago also discussed Recursive’s latest innovations, focusing on Borealis, an advanced physical systems modeling solution that serves as a “digital twin” of nature to deliver hyper-local forecasts. He shared an example of Borealis in action, where it accurately forecasts water levels in Indonesia’s forests, helping efficiently manage land irrigation.Discussing the global implications of AI-driven businesses, Tiago emphasized, “If you want to have a successful operation, it has to be global.” He explained that Recursive’s strategy combines high standards of quality with solutions tailored to local market needs, positioning Recursive as an adaptable, globally-minded innovator.<img src=/content/images/image_53977fcf008e6cae35e462659c6cb2dc.jpg>Looking to the future, Tiago urged companies to plan beyond immediate gains, drawing an analogy to the rapid evolution of the computing industry in the 1990s. Just as the first video game developers had to anticipate the rapid hardware advancements, companies today should focus on building AI strategies for the advancements of the next 5-10 years. This forward-looking approach entails digitizing operations, creating seamless data flows, and securing data ownership to fully harness AI’s future potential. Interested in how Recursive’s solutions can benefit your business? Contact us at <a href=mailto:sbdm@recursiveai.co.jp>sbdm@recursiveai.co.jp</a> for a consultation on leveraging AI to drive efficiency, sustainability, and growth.

【Event】Recursive Co-Founder and CEO, Tiago Ramalho, Speaks at the Southeast Asia Trade and Development Forum

Katsutoshi Yamada, Co-Founder and COO of Recursive, was invited to be a guest lecturer for the University of Miyazaki’s Graduate School of Engineering. He delivered an online lecture for the newly introduced course, “Advanced Information Theory to Meet Societal Needs,” which began in October 2024. The course focuses on the social implementation of data science and AI technologies, allowing students to explore successful examples of digital transformation (DX) and the latest technological trends. In his lecture to approximately 60 students, Yamada introduced Recursive’s business and discussed case studies of various sustainability initiatives that utilize AI, as well as basic knowledge of sustainability and its latest trends.
 During the lecture, Yamada also passionately told the students, “While it is wonderful to thoroughly pursue technology, it is also important to think about why you are doing it. There are various issues in the world, so I encourage everyone to think about what you can achieve through technology and innovation in addressing these issues." In addition, Yamada also shared his experience as a serial entrepreneur, and stressed, "Solving social problems and working for sustainability is not everything. What matters is that you focus on what you are passionate about and hold onto that passion. You never know when an opportunity will arise."<img src=/content/images/image_9bbaf8d9b94539f50e61d9cf73b04d57.png>During the Q&A session that followed his lecture, there was strong interest from students regarding the use of AI in the education sector, as well as its business models. A question was also raised about developing AIs that can resonate with human emotions. The session saw impressive engagement from students, leading to a lively exchange of ideas.
 In addition to developing and providing solutions that contribute to sustainability and the resolution of social issues through AI, Recursive will also continue its commitment to empowering AI communities worldwide, driving efforts toward a sustainable future.

【Event】Recursive’s COO, Yamada, Gives Lecture at University of Miyazaki’s Graduate School as Guest Lecturer

Recursive’s Co-Founder and CEO, Tiago Ramalho, and Project Manager, Quentin Siutkowski, were recently interviewed by GIS NEXT, a leading magazine specializing in geographic information systems (GIS). The feature provides an in-depth look at Recursive’s high-precision environmental modeling platform, Borealis. As demand grows for more refined methods in environmental observation, analysis, and forecasting, the reporter was interested in Borealis as it has advanced capabilities in satellite and meteorological data processing to generate highly accurate environmental simulations.
 The article covers an overview of Borealis, its development journey, and various use cases. Tiago also shared his vision for Borealis as a “digital twin of the natural environment,” saying the company aims to be able to cover nationwide Japan by 2030 to develop and deliver solutions that can enhance everyone’s understanding of the overall environment and be of practical use to customers.
 We invite you to read the article and explore Borealis' potential.
Coverage details:
●　Name of the publication: GIS NEXT
●　Issue: No. 89 (October 28, 2024)
●　Purchase Link:<a href=http://www.nextpb.com/gisnext/backnumber/no89.html> GIS NEXT Issue No. 89</a>

Borealis Featured in Japanese Trade Magazine for GIS Technology, GIS NEXT

Recursive has been honored with the "TRIPLE" ranking in the "Growth Companies Selection 2024," by SalesNow DB, a database service operated by SalesNow Inc. (Headquarters: Shibuya, Tokyo; CEO: Atsunori Muraoka; hereinafter, “SalesNow”). This recognition is awarded to companies in the SMB category with an employee growth rate in the top 5%.The selection is based on independent research and analysis conducted by SalesNow, covering the employee growth rate data of approximately 5.4 million companies in Japan between January to December 2023. According to their research, Recursive achieved an impressive employee growth rate of 44.44% and was recognized as one of the companies with excellent workforce expansion in Japan.SalesNow DB’s 2024 Growth Companies Selection divides businesses into the following categories, based on employee numbers as of January 2023, and ranks them by their growth rates from January to December 2023:
■ Company Categories:<ul><li>Enterprise: 1,000+ employees</li><li>Mid Market: 100 - 999 employees</li><li>SMB: 10 - 99 employees
(*Excludes companies established within the past two years and inactive businesses)</li></ul>■ Growth Rate Categories:<ul><li>TRIPLE: Top 5%</li><li>DOUBLE: Top 10%</li><li>SINGLE: Top 30%</li></ul>Recursive provides cutting-edge AI technology development and consulting services to businesses worldwide. While the importance and potential of AI as a solution for sustainable business transformation has been attracting attention in recent years, the needs of society and companies are becoming increasingly diverse and complex.To address these evolving needs, Recursive has actively strengthened its team by recruiting professionals with a variety of specialized expertise globally. This approach enables the company to remain flexible in meeting diverse client requirements and accelerate business innovation.Recursive continues to seek talented individuals who can contribute to its mission of not only improving operational efficiency but also fostering creativity, collaboration, and sustainable societal progress through AI adoption and support. If you are interested in joining Recursive, please visit our careers page below:
▶<a href=https://recursiveai.co.jp/en/careers/> Recursive Careers Page</a>[Reference]
Dedicated Page for Recursive's Selection:<a href=https://salesnow.jp/db/companies/7011001135182/growth-selection> https://salesnow.jp/db/companies/7011001135182/growth-selection</a>[Source]
SalesNow DB (<a href=https://salesnow.jp/db>https://salesnow.jp/db</a>)

Recursive Selected for "Growth Companies Selection 2024" by SalesNow

On September 24, 2024, the Park Hyatt Bangkok hosted the Japan External Trade Organization (JETRO) Bangkok’s 70th Anniversary Forum, a milestone event celebrating decades of Japan-Thailand cooperation. The forum brought together industry leaders, government representatives, and innovative startups to explore future collaborations in sustainable development.Taylor Scobbie, Director of Overseas at Recursive, participated in the forum to share insights on how AI technology can contribute to achieving carbon reduction goals and strengthening economic ties between Japan and Thailand.Key Takeaways: <ul><li>The Growing Importance of AI: Taylor highlighted AI’s transformative potential in reshaping industries, citing McKinsey's estimate that AI could generate approximately 4 trillion USD in economic value. He remarked, "AI is an adopt-or-lose proposition," emphasizing the critical need for businesses to adopt AI-driven solutions to stay competitive.</li><li>Recursive AI Carbon Management: Taylor introduced Recursive’s AI and physics-based modeling solution, which analyzes climate, geological, satellite, and ground data to accurately estimate carbon storage potential across various landscapes and ecosystems.</li><li>Recursive AI for a More Sustainable Thailand: Taylor highlighted how Recursive AI Carbon Management could help Thai landowners boost carbon sequestration and access valuable carbon credits. This initiative aligns with Thailand’s sustainability goals, supporting global carbon reduction efforts while offering economic opportunities to local stakeholders.</li></ul><img src=/content/images/image_282a70f3a8f451048ceb76bb3ea4c22d.jpg>Taylor concluded his presentation by emphasizing the importance of collaboration between Japan and Thailand, stating, “By combining AI innovation with localized solutions, and through the collective efforts of Japan and Thailand, we can address global challenges, creating a sustainable future for generations to come.”Interested in learning more about Recursive’s AI-driven solutions? Contact us at <a href=mailto:sbdm@recursiveai.co.jp>sbdm@recursiveai.co.jp</a>.

【Event】Recursive’s Director of Overseas, Taylor Scobbie, Speaks at JETRO Bangkok’s 70th Anniversary Forum

- Pitched a solution to support reskilling of workers by utilizing an educational platform developed by Recursive - Tokyo, Japan - September 25, 2024 - Recursive Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter, "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is thrilled to announce that it won the Innovation Award at the Meta Llama Ideathon in Japan, hosted by Meta’s Japanese subsidiary, Facebook Japan, on September 6, 2024. The Meta Llama Ideathon is a competition in which participants pitch projects that will bring economic and social impact by utilizing Llama, a next-generation, open-source large-scale language model developed by Meta. Recursive was one of the six teams that passed a rigorous selection process and made it to the finals of the Japan competition, where they took the stage to pitch a solution to help workers reskill using FindFlow Education, a comprehensive education platform implemented on Llama and developed in-house. Recursive pitched a solution to support reskilling and was highly acclaimed. In today's rapidly evolving world of technological innovation, many workers are under increasing pressure to acquire new skills. The shortage of teachers and trainers, along with the significant labor involved in creating educational content, is becoming an escalating challenge. Additionally, much of the available educational material is predominantly in English, which underscores the urgent need to make content available in multiple languages. To address these bottlenecks, Recursive proposed a solution that contributes to the efficient reskilling of workers by utilizing FindFlow Education, an efficient and comprehensive education platform that leverages AI to automatically generate course materials and exams from any document in multiple languages and automatically grades them. FindFlow Education utilizes Retrieval-Augmented Generation (RAG) to automatically generate systematic course materials based on existing databases, enabling learners to efficiently and effectively reskill themselves in a self-guided learning environment rather than answering individual questions as they come. By doing so, learners can reskill in a way that guides their own learning. In addition, companies across various industries can reduce the labor cost and time required to create course materials, allowing employees to learn more proactively, efficiently, and effectively. Recursive has a mission to build a fairer, more sustainable society by utilizing AI. As part of this mission, one of Recursive’s goals for 2030 is to provide AI-enabled education to 100 million people. Recursive will continue to develop innovative solutions using AI technology to enhance its efforts to close the global skills gap by providing high-quality education and job opportunities. <Reference><ul><li>For more details on the results of the Meta Llama Ideathon, please refer to Meta’s press release (Japanese only):
<a href=https://about.fb.com/ja/news/2024/09/llama-ideathon/>https://about.fb.com/ja/news/2024/09/llama-ideathon/</a></li></ul>　　[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 59 (including full-time employees, outsourced workers, and interns; as of July 2024)
Website: <a href=https://recursiveai.co.jp/en/> https://recursiveai.co.jp/en/</a>[Media inquiries]
Recursive Inc.
Kishimoto (PR & Branding)
Email: info@recursiveai.co.jp

【Press Release】AI Startup, Recursive, Wins Innovation Award at Meta Llama Ideathon Hosted by Meta

Tiago Ramalho, Co-founder and CEO of Recursive, spoke at the Entrepreneurship Development Training for Ukrainian Women and Youth in Japan hosted by United Nations Industrial Development Organization’s Investment and Technology Promotion Office (UNIDO ITPO) in Tokyo. The event was held in cooperation with UNIDO ITPO Bahrain from September 9 to 13, 2024 at the United Nations University in Tokyo.　 The event aimed to support Ukrainian people, especially evacuees and university students residing in Japan to nurture self-reliance and entrepreneurship. As one of the speakers, Tiago spoke about his experience as an entrepreneur in Japan and explained about Recursive and its business.　　In order to leave a better global environment and society for future generations, including solutions to the refugee crisis, Recursive will continue to promote and develop AI as well as providing sustainability-related solutions.　　　　For more information about this event, please also check out the event report by UNIDO:
<a href=https://www.unido.or.jp/en/outcome/seminars_events/12444/>https://www.unido.or.jp/en/outcome/seminars_events/12444/</a>

【Event】Recursive’s CEO, Tiago, Speaks at UNIDO’s Ukrainian Women and Youth in Japan Event

- The three firms to discuss under the topic, “The added value of AI: Governance and Innovation -　Tokyo, Japan - September 12, 2024 - Recursive Inc. (Headquarters: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter, "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is thrilled to announce that it will present at a seminar at Japan's largest open innovation trade show, "Japan Open Innovation Fes 2024" (hereinafter, “JOIF2024”), which will be held in Minato City, Tokyo, on September 13, 2024 (Friday).　Details are as follows:　Event overview■Name of the event：Japan Open Innovation Fes 2024 (also known as JOIF2024)■Organizer: eiicon Co., Ltd■Date/time: September 13, 2024 (Friday) 10:00 AM - 8:00 PM■Venue: Akasaka Intercity Conference(1-8-1 Akasaka, Minato-ku, Tokyo, Akasaka Intercity AIR, 4th Floor)■Theme: "GENERATE NEW BUSINESS - A Conference to Generate Business"Overview of the seminar■Name of the seminar: Seminar Stage SESSION 5 "The Added Value of AI: Governance and Innovation"■Overview of the session: Together with KDDI and Sumitomo Forestry, the session will explore the added value AI brings.Why did KDDI choose to build its own AI platform? Why is Sumitomo Forestry considering AI for forest management?Recursive, which specializes in custom AI development for a sustainable future, will introduce some of the projects undertaken by these major companies.■Date/time: September 13, 2024 (Friday) 6:00 PM to 6:45 PM■Venue: "The Amphitheater" at Akasaka Intercity Conference■Access: Directly connected to Tameike-Sanno Station (Ginza Line and Namboku Line, Exit 14)■Speakers:<ul><li>Shun Wakabayashi (Executive Officer, Recursive Inc.)</li><li>Tomoko Aizawa (General Manager of Communication Design, Brand Communication Division, KDDI Corporation)</li><li>Tsuyoshi Kato (Director & Deputy General Manager, Resources & Environment Business Division, Sumitomo Forestry Co., Ltd.)</li></ul>■Registration: Please register through the following form: <a href=https://auba.eiicon.net/joif/apply>https://auba.eiicon.net/joif/apply</a>　　[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 59 (including full-time employees, outsourced workers, and interns; as of July 2024)
Website: <a href=https://recursiveai.co.jp/en/> https://recursiveai.co.jp/en/</a>[Media inquiries]
Recursive Inc.
Kishimoto (PR & Branding)
Email: info@recursiveai.co.jp

【Press Release】AI Startup, Recursive, to Speak at a Seminar with KDDI and Sumitomo Forestry at Japan’s Largest Open Innovation Trade Show, JOIF2024

Katsutoshi Yamada, Co-founder and COO of Recursive, and Director of the AI industry association Generative AI Japan (GenAI), joined three other experts from GenAI and its member companies to speak at the AI Expo as part of his role as Director of GenAI. The event took place in Tokyo over two days, from August 29 to 30, 2024. The title of the session was “Creating Business Value through Generative AI”, and four members from the board of Generative AI Japan and its member companies discussed how Japanese companies can win in using generative AI.
 
The venue was filled to capacity, with discussions spanning from the latest trends in generative AI technology to its use cases in business enterprises.●Session Title: “AI Expo” —- Creating Business Value through Generative AI: What is the Winning Strategy for Japanese Companies?●Date: August 29, 2024 (Thurs) from 12:20 PM●Speakers:
Shigeru Urushibara of UL Systems, Inc
Yuusuke Takagi of Automation co., ltd.
Katsutoshi Yamada of Recursive Inc
Yuriko Asahina of Persol Holdings Co., Ltd.<img src=/content/images/image_99fbfb0182ec4b79ede7382cd64d2a21.jpg>Recursive is participating as a corporate member of Generative AI Japan (commonly known as GenAI), a general incorporated association launched in January 2024. Yamada was appointed as a board member of GenAI, which aims to enhance the industrial competitiveness of Japan as a whole by providing education, career opportunities, collaboration, co-creation, rule making, and proposals in the use of generative AI under the cooperation of industry and academia.As a corporate board member, we aim to continue discussions on utilizing generative AI, establish industry standards based on user-aligned use cases, and work towards the dissemination of best practices moving forward.

▼Please check out the event report by GenAI (Japanese): 
<a href=https://generativeaijapan.or.jp/370/>https://generativeaijapan.or.jp/317/</a>

【Event】Recursive's COO, Yamada, Joins Three Other Experts from Generative AI Japan and its Member Companies to Speak at AI Expo

- Flow Benchmark Tools to set standardized criteria for RAG evaluation, contribute to effective deployment of RAGs for various industries -<ul><li>Recursive developed a standardized, comprehensive tool suite for benchmarking the different RAG pipelines with a focus on Japanese language performance</li><li>The results from the test benchmark conducted on the Tool shows Recursive’s originally-developed RAG technology, FindFlow, outperforms major RAG pipelines in the market in both Question Answering using RAG and Whole Document Understanding aspects</li></ul>Tokyo, Japan - August 6, 2024 - Recursive Inc., a developer of AI solutions that facilitate sustainable business transformation, is pleased to announce that today, it has launched its open-source benchmarking tool suite for retrieval-augmented generation (RAG; *1) systems, “Flow Benchmark Tools.” Aiming to serve as an industry standard for evaluating RAG systems, the Flow Benchmark Tools are designed to evaluate and optimize RAG systems by focusing on document-specific information retrieval and end-to-end document processing, closely mirroring real-world scenarios. Recursive’s Flow Benchmark Tools are now publicly available to all engineers globally as open-source code on GitHub.Background and current challenges of benchmarking RAG systems
While organizations across various industries are increasingly viewing RAG as transformative solution for their businesses, there is no standardized, comprehensive method for benchmarking the different RAG pipelines (*2) in the market. Without a reliable benchmark, organizations face a critical challenge in identifying and selecting the most effective RAG tool for their specific use cases; this not only delays RAG implementation but also increases the risks of deploying ineffective solutions and wasting valuable resources.Moreover, there is a lack of benchmarking methods for non-English RAG systems, particularly for Japanese language performance. This makes it even more challenging for Japanese businesses to effectively evaluate and implement RAG tools suited to their specific linguistic and cultural needs.Key features of the Flow Benchmark Tools
By addressing the complexities inherent in RAG pipelines such as semantic retrieval, query generation, and Large Language Models (LLM)-based answer generation, the Flow Benchmark Tools provide a nuanced, comprehensive evaluation framework.The key features of the Tool include:<ul><li>Multilingual capabilities, including Japanese language: With a focus on Japanese language performance in addition to English, the Flow Benchmark Tools are able to accurately measure RAG system performance in Japanese language.</li><li>Comprehensive evaluation of the entire RAG pipeline: By evaluating the entire pipeline from raw document processing to response generation, the Flow Benchmark Tools provide a more holistic and practical assessment of RAG system performance.</li><ul><li>To ensure the most objective and robust evaluation possible, mitigate potential biases, and provide a comprehensive assessment of performance, the Tool employs an automated evaluation system that leverages multiple state-of-the-art LLMs, including GPT-4, Claude 3, and Gemini.</li><li>The automated evaluation system outputs a mean opinion rating with values ranging from 0 (worst) to 10 (perfect).</li></ul><li>Open-source methodology: The Flow Benchmark Tools are developed with an open-source approach, fostering transparency and collaboration within the AI community to have them leverage and build upon Recursive’s work to customize to their organization’s needs.</li></ul>Findings from the test benchmark:
Recursive conducted a test benchmark using the Flow Benchmark Tools and compared Recursive’s original fully customizable LLM-based Generative AI assistant, FindFlow, with three state-of-the-art Generative AI solutions in the market and designed the test to be thorough, fair, and reflective of real-world scenarios. Conducted from June 7 to July 19, 2024, t     he benchmark evaluated each of the pipelines’ performance in two critical areas: Question Answering using RAG and Whole Document Understanding.The benchmarking process utilized a dataset of Japanese government documents with challenging questions.Recursive's FindFlow RAG applications have demonstrated superior performance in initial benchmarks:<ul><li>FindFlow SearchAI (feature within FindFlow that is optimized for discovering factual information from a large database of documents) achieved a mean rating of 8.42 in RAG question answering, outperforming leading competitors by 0.61 to 1.41 points.</li><li>FindFlow AnalysisAI (feature within FindFlow that is optimized for analyzing documents and communications in-depth) scored 8.90 in whole document analysis, surpassing other prominent AI solutions by 1.68 to 2.61 points.</li></ul>These results underscore Recursive's commitment to developing RAG technologies that excel in complex, document-rich scenarios."Our Flow Benchmark Tools represent a major leap forward in RAG system evaluation," said Tiago Ramalho, Co-founder and CEO of Recursive. "By open-sourcing these tools, we're inviting the global AI community to join us in pushing the boundaries of what's possible in AI-driven document analysis and retrieval."Recursive plans to continually improve and expand these tools, incorporating additional languages and refining methodologies based on community feedback.In addition, Recursive aims to continue pursuing our commitment to not only driving sustainable business innovations, but also advancing RAG technology and empowering the global AI community.


*1 Retrieval-augmented generation (RAG): RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process
*2 RAG pipeline: A piece of code that performs RAG


Reference:<ul><li>Information about the Flow Benchmark Tools and to access the open-source release: <a href=https://recursiveai.co.jp/en/blog/introducing-flow-benchmark-tools/>https://recursiveai.co.jp/en/blog/introducing-flow-benchmark-tools/</a></li><li>Information on FindFlow: <a href=https://www.findflow.ai/en/>https://www.findflow.ai/en/</a></li><li>Flow Benchmark Tools on GitHub: <a href=https://github.com/recursiveai/flow_benchmark_tools>https://github.com/recursiveai/flow_benchmark_tools</a></li></ul>　[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 59 (including full-time employees, outsourced workers, and interns; as of July 2024)
Website: <a href=https://recursiveai.co.jp/en/> https://recursiveai.co.jp/en/</a>[Media inquiries]
Recursive Inc.
Kishimoto (PR & Branding)
Email: info@recursiveai.co.jp
Phone: +81-90-9847-7832 (Direct number to Kishimoto)

【Press Release】AI startup, Recursive, Launches Open-Source Benchmarking Tools for RAG Technology Evaluation

Recursive's Co-founder and COO, Katsutoshi Yamada, who is also a board member of the AI industry association Generative AI Japan (hereafter referred to as “GenAI”), spoke at the first Member Companies Summit hosted by GenAI on July 16, 2024. Yamada participated as a panelist in a panel discussion on social changes through generative AI. Six experts engaged in a lively discussion about future business opportunities for Japanese companies and the country's direction. The memorable first Members Summit attracted over 120 members, making it a very exciting event.Recursive is participating as a corporate member of Generative AI Japan (commonly known as GenAI), a general incorporated association launched in January 2024. Yamada was appointed as a board member of GenAI, which aims to enhance the industrial competitiveness of Japan as a whole by providing education, career opportunities, collaboration, co-creation, rule making, and proposals in the use of generative AI under the cooperation of industry and academia.
As a corporate board member, we aim to continue discussions on utilizing generative AI, establish industry standards based on user-aligned use cases, and work towards the dissemination of best practices moving forward.

▼Refence material: Event report by GenAI (Japanese only)
<a href=https://generativeaijapan.or.jp/317/>https://generativeaijapan.or.jp/317/</a>

【Event】 COO Yamada Speaks at the First Member Companies Summit Hosted by Generative AI

<ul><li>Mikiko has a distinguished career that spans across project management, consulting, and promoting sustainability and has made significant contributions at The Boeing Company, Accenture Japan, and Google Japan.</li><li>Mikiko's arrival is set to greatly enhance Recursive's project framework and management structure. Her extensive management expertise is perfectly aligned with Recursive's mission to drive AI development projects that lead to sustainable business transformation. With Mikiko at the helm of project management and as an Executive Officer, Recursive is poised for an exciting phase of strengthened capabilities and enhanced strategic direction.</li><li>Her role at Recursive will involve leading the Project Management department, to steer projects that address and solve client challenges effectively.</li></ul>Recursive Inc. (Headquarter: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter referred to as "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, is thrilled to announce the appointment of Mikiko Ujihara as Recursive’s new Executive Officer, effective on July 16, 2024. Previously overseeing strategic partnerships and sustainability initiatives at Google Japan, Mikiko brings a unique wealth of expertise and understanding in not only technical aspects but also sales and consulting. As she has insights and the necessary know-hows to facilitate complex, multifaceted projects, Mikiko will further accelerate Recursive’s business as a project management leader. With Mikiko’s appointment, Recursive aims to further strengthen not only its management structure but also enhance its project management structure to better meet the increasingly diverse, complex needs of its clients.Recursive provides technical development, customization, and consulting of cutting-edge AI technologies for both Japanese and international companies. While the importance and potential of AI as a solution for sustainable business transformation has been attracting attention in recent years, the needs of society and companies are becoming increasingly diverse and complex. As an Executive Officer, Mikiko joins Recursive’s management team consisting of Tiago Ramalho, Co-Founder and CEO; Katsutoshi Yamada, Co-Founder and COO; and Shun Wakabayashi, Executive Officer. With Mikiko’s extensive management experience across various industries, Recursive aims to strengthen its management structure and its project management setup to enhance its capabilities to better achieve clients’ needs.“I am looking forward to collaborating with Recursive’s team, composed of members from diverse backgrounds and expertise, representing nationalities spanning 20 countries worldwide. Together, we aim to foster sustainable innovation across diverse industries and work toward Recursive's mission of achieving 'a fairer, more sustainable society,'” says Mikiko upon her appointment.Moving forward, Recursive will intensify its efforts to support diverse industries and firms in utilizing and adopting AI technologies. These efforts are not only to enhance business efficiency but also to foster creativity and collaboration that can ultimately contribute to building a sustainable society.<Background of Mikiko Ujihara>
Before joining Recursive, Mikiko spent nearly a decade at The Boeing Company's U.S. headquarters. There, she served as an Engineer on the International Space Station and the Research & Technology divisions, and later as a Regional Director for Airline Economics in the Commercial Airplanes Sales & Marketing division. Following this role, she worked for over four years as a Management Consulting Manager at Accenture K.K., the Japanese subsidiary of Accenture. During her time there, she provided consulting services across various domains, including digital transformation. Most recently, Mikiko spent over five years at Google Japan G.K. In addition to her role as a Strategic Partnership Manager in the Global Partnerships organization, , she has served as a Project Manager at Google's moonshot factory "X" (formerly known as Google X), and Lead for Google Japan’s Sustainability Team.<img src=/content/images/image_00ab8da19b077549c95b47e03cfaf86a.jpg>The management team of Recursive Inc. : (left to right) Katsutoshi Yamada, Co-Founder and COO; Mikiko Ujihara, the newly appointed Executive Officer; Tiago Ramalho, Co-Founder and CEO; and Shun Wakabayashi, Executive Officer[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of Members: 59 (including full-time employees, outsourced workers, and interns; as of July 2024)
Website:  https://recursiveai.co.jp/en/[Media inquiries]
Recursive Inc.
Kishimoto (PR & Branding)
Email: info@recursiveai.co.jp
Phone: +81-90-9847-7832 (Direct number to Kishimoto)

【Press Release】AI Startup, Recursive, Appoints Mikiko Ujihara, Former Member of Google Japan, as its Executive Officer

- Aims to improve online ad production efficiency and maximize effectiveness through AI trained on KDDI’s data -Recursive Inc. (Headquarter: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter referred to as "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, has officially been contracted by the major Japanese telecommunications firm, KDDI Corporation (hereinafter referred to as “KDDI”). With this, Recursive is pleased to announce that it has started developing a data-driven AI model to generate ad creatives for KDDI. In addition to considering the concepts and customer characteristics of KDDI Group and its affiliated brands, the AI model being developed by Recursive will also enable the production of various ad creatives based on predictions of  KPI performance. This capability will allow KDDI to produce ad creatives and image assets, such as banners, more efficiently and effectively. In general, research and development of generative AI for image editing and creation tend to lag behind advancements in text generation. However, this data-driven generative AI model based on the prediction of set KPIs is a groundbreaking initiative that will open up new possibilities for ad production and operation. The development for this AI model is expected to be completed by the fall of 2024.Background and current challengesWith the rapid growth of social media in recent years, online advertising has gained significant importance. Advertisers now need to produce ad designs and compositions flexibly and quickly, all the while considering the unique viewing behaviors that are different from traditional mass media, as well as the different characteristics of each platform.  However, crafting online advertisements that have market appeal while optimizing them for each platform demands more production time and effort than traditional ads, thereby posing a challenge in achieving efficient production methods. Moreover, there is a pressing need to swiftly cycle through the PDCA (Plan-Do-Check-Act) process to optimize and maximize the effectiveness of advertisements.KDDI also faces the dual challenge of improving work efficiency and accelerating the PDCA cycle within its ad creative production process. Additionally, since KDDI has various brands within its group, it must also consider the branding, target audiences, concept, specifications, messaging, and descriptions for each brand, product, and service. Therefore, the AI model is anticipated to integrate these considerations from the early stages of ad creative production, facilitating efficient optimization and enabling high-volume production of ad creatives.Overview of the projectAgainst this background, Recursive will develop a generative AI model that would be capable of performing the following two main things in order to achieve optimization, efficiency, and high-volume production for KDDI's online ad creatives:<ol type=1><li>Generation of ad materials and image assets in line with brand identity: Reflecting the characteristics and needs of each brand (branding, target customers, concept, specifications, messaging, description, etc.) from the early stages of production, the AI model will generate ad designs tailored to each of the platforms where the ad will be placed. This will reduce the time and effort of tasks that were conventionally done manually, and improve operational efficiency.</li><li>Prediction of KPIs and decision on ad creatives with maximum effectiveness: The AI model will predict KPI performance, allowing for the prioritization of designs based on these metrics. This approach will enable the production of "data-driven" ad creatives grounded in KPI data.</li></ol><img src=/content/images/image_9bab97bcaabeb3d86285380465d8f466.png>Image of how the ad creatives will be generated when using Recursive's AI modelThe KDDI Group plays a crucial role in providing telecommunications infrastructure while striving to reduce its environmental footprint in Japan and abroad. Through the development of this AI model, Recursive aims to support KDDI Group by helping its products and services reach more people through efficient and effective advertising campaigns.[About Recursive Inc.]
Recursive is a service provider that offers AI solutions for building a sustainable future. By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society behind for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 52 (including full-time employees, outsourced workers, and interns; as of May 2024)
Website:  https://recursiveai.co.jp/en/[Media inquiries]
Recursive Inc.
Kishimoto (PR & Branding)
Email: info@recursiveai.co.jp
Phone: +81-90-9847-7832 (Direct number to Kishimoto)

【Press Release】AI startup, Recursive, Starts the Development of KDDI’s Original Ad Creative Production Model, Utilizing Generative AI

ROHTO Pharmaceutical Co., Ltd. (Headquarter: Osaka, Japan; President and Chief Operating Officer: Masashi Sugimoto; hereinafter referred to as "Rohto") and Recursive Inc. (Headquarter: Shibuya-ku, Tokyo; CEO: Tiago Ramalho; hereinafter referred to as "Recursive"), a developer of AI solutions that facilitate sustainable business transformation, have entered into a capital and business alliance as of June 14.Background and purposeUnder the management vision of “Connect for Well-being” which strives to support the health and happiness of people worldwide, ROHTO has been working to promote well-being not only in the field of healthcare, but also in various other areas such as food, community, and health-oriented leadership.Recursive is a service provider that offers AI solutions for building a sustainable future, with a mission to "Build a fairer, more sustainable society through the use of technology by utilizing technology." By combining expertise in diverse industries such as environment, energy, healthcare, pharmaceuticals, food, and retail as well as advanced technological capabilities and specialized knowledge in sustainability, Recursive provides AI consulting services and technical development. In order to leave a better global environment and society for future generations, Recursive’s unparalleled professionals are leading the creation of a new society with world-class, cutting-edge technology.ROHTO and Recursive decided to collaborate through this capital and business alliance, because both parties wanted to leverage cutting-edge AI technology to increase value for customers. This will be achieved by building practical AI models using large-scale health data resources.  By doing so, ROHTO and Recursive aim to create various innovations and strive to increase well-being in our society through the utilization of AI.[About ROHTO Pharmaceutical Co., Ltd.]
Company name: ROHTO Pharmaceutical Co., Ltd.
Headquarter:  1-8-1 Tatsumi-nishi, Ikuno-ku, Osaka City, Osaka
Founded: February 22, 1899
President and Chief Operating Officer: Masahi Sugimoto
Incorporated: September 15, 1949
Number of employees (as of March 31, 2024): 1,687 (non-consolidated basis) / 7,259 (consolidated basis)
Main businesses: Manufacturing and marketing pharmaceutical products, cosmetics, and functional foods
Website: https://www.rohto.co.jp/global/[About Recursive Inc.]
Company name: Recursive Inc.
Headquarter: Shibuya S-6 Building 6F, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Founded: August 2020
Co-Founders: Tiago Ramalho, Katsutoshi Yamada
Business: Research and development of AI and provision of sustainability-related solutions
Number of employees: 52 (as of May 2024)
Website:  https://recursiveai.co.jp/en/[Media inquiries about Recursive]
Recursive Inc.
Kishimoto / Chiba (PR & Branding)
Email: info@recursiveai.co.jp
Phone: +81-90-9847-7832 (Direct number to Kishimoto)

【Press Release】ROHTO Pharmaceutical and AI startup, Recursive, Form a Capital and Business Alliance to Achieve a Well-being Society Using AI

In conjunction with World Refugee Day (June 20), the 3rd "<a href=https://prtimes.jp/main/html/rd/p/000000010.000123868.html>Welcome Japan Summit 2024</a>" was held in Tokyo on June 17, 2024, our COO Katsutoshi Yamada was invited to participate as a keynote panelist. The event was organized by Welcome Japan, a general incorporated association, to explore tips for promoting refugee inclusion in Japan.<img src=/content/images/image_d0779fd7e5f47b9633c79d94d82096d4.png>The panel discussion, titled "The Potential of DX for Refugee Inclusion," featured Yamada and two other experts and stakeholders, who discussed how companies can get involved in AI-based refugee inclusion, the impact of AI on the lives and careers of refugees, and the prospects for using AI for refugee inclusion.Recursive is a service provider that provides AI solutions to build a sustainable future with the mission "Build a fairer, more sustainable society through the use of technology. Our COO Yamada suggested that in the refugee issue, the evolution of AI and other technologies and their use as effective solutions can help solve the three disparities of (1) language disparity, (2) information disparity, and (3) education disparity.He also mentioned the possibility that the recent spread of generative AI, led by ChatGPT, could dramatically change the world in the future, and discussed how the full-scale introduction and utilization of AI for refugee assistance platforms is an important perspective to take.<img src=/content/images/image_3ad8962d000cddea9e938b201c70e663.png>In order to leave a better global environment and society for future generations, including solutions to the refugee problem, we will continue to promote AI research and development and the provision of sustainability-related solutions.

【Event】COO Yamada Speaks at "Welcome Japan Summit 2024" and Introduces the Use of AI for Refugee Inclusion

On June 6, 2024, Recursive’s Senior Machine Learning Engineer, Pablo Cervantes, presented online at Le Wagon Career Week in Tokyo, engaging directly with students and graduates of the data science bootcamp. His talk served as a deep dive into the evolving role of machine learning engineers at Recursive and the company’s strategic focus on leveraging AI for sustainability. Pablo detailed how engineers are integral to developing solutions that not only push the boundaries of technology but also address critical environmental issues.Pablo also shed light on the unique career prospects available at Recursive. He made it clear that working as a machine learning engineer at Recursive goes beyond traditional tech roles, offering the chance to contribute to projects with tangible, positive impacts on society. The culture of continuous learning and professional growth at Recursive ensures that its engineers are not just participants in the tech industry but leaders in marrying AI with sustainability efforts. Through his presentation, Pablo inspired attendees to envision a career where their technical expertise could play a crucial role in shaping a more sustainable future, highlighting the rewarding journey that lies ahead for engineers at Recursive.In addition, Recursive is looking for people with expertise and a passion for sustainability. Recursive gathers people who want to grow themselves without being bound by conventional business formats and work styles in the ever-changing social business environment. Please take a look the following link.<a href=https://recursiveai.co.jp/en/careers/>https://recursiveai.co.jp/en/careers/</a><img src=/content/images/image_2cdcc73289b4b53836148ca78916453d.png>

【Event】Pablo Speaks at the Career Week at Le Wagon in Tokyo for Students and Graduates of Data Science Bootcamp 

At the first-ever <a href=https://www.superai.com/>SuperAI Conference 2024</a>, held at the iconic Marina Bay Sands in Singapore, aimed at bringing together industry leaders, heads of state, entrepreneurs, renowned researchers, and curious minds alike who will define the future of artificial intelligence. The conference featured a compelling panel discussion that delved deep into the potential and challenges of Generative AI technologies. Among the distinguished panelists was Recursive’s Co-Founder and CEO, Tiago Ramalho, who shared the stage with Mohan Jayaraman, an Expert Partner from Bain and Company, and Chandra Khatri, the founder of Krutrim AI. The session was expertly moderated by Julia Gusakova, the ASEAN AI Lead of Customer Success at Microsoft, whose insightful questions steered the conversation towards the future of AI.<img src=/content/images/image_d48f4a45e528492eeb9e42a99bf44017.jpeg>The discussion titled "Unleashing the Power of Generative AI" explored the vast capabilities and the transformative impact of Generative AI across various sectors. Tiago Ramalho provided a comprehensive overview of how Recursive is leveraging Generative AI to provide catered solutions to solve complex problems for their Japanese clients. Tiago discussed how Recursive is enabling companies to integrate AI into their businesses, as well as how they are solving non-digital use cases, such as manufacturing and environmental management, which have more potential for long-term impact on sustainability.All the panelists provided unique insights and perspectives into the potential of Generative AI to reshape industries, economies, and societies. Tiago's insights, in particular, highlighted the critical role of leadership and vision in navigating the complexities of the AI landscape towards a more sustainable future. 

By speaking at this event, Recursive was able to provide insight into how much potential utilizing cutting-edge technology like AI has in order to achieve a sustainable society. Striving to achieve our mission of "building a fairer, more sustainable society by utilizing technology,” Recursive will continue to provide advanced AI technology to domestic and international companies to further drive sustainable change and innovation.

【Event】CEO Tiago Speaks at SuperAI, an Event to Promote High Tech to Achieve Sustainable Cities

Four board members of <a href=https://generativeaijapan.or.jp/>GenAI</a> spoke at the AI and Population Intelligence EXPO on May 22, 2024, on the theme "Business Value Creation Strategies with Generative AI" and use cases of generative AI in Japan and companies, They talked about the use of generative AI in Japan, corporate use cases, and strategies for business applications. The session was attended by a large audience of over 800 people.
　Recursive is participating as a corporate member of Generative AI Japan (abbreviated as GenAI), a general incorporated association launched in January 2024. Katsutoshi Yamada, President and COO of Recursive, has been appointed as a board member of GenAI Japan, which aims to enhance the industrial competitiveness of Japan as a whole by providing education, career opportunities, collaboration, co-creation, rule making, and proposals in the use of generative AI under the cooperation of industry and academia.
As a director in the future, under the cooperation of industry-academy, we will discuss the way of utilizing generative AI, establish industry standards from use cases that align with user realities, and work toward the dissemination of best practices.


▼Related our press release 
<a href=https://website-dev.recursivehq.com/en/news/20240117/>https://website-dev.recursivehq.com/en/news/20240117/</a>
▼ Introduction of other board members at this event
<a href=https://generativeaijapan.or.jp/274/>https://generativeaijapan.or.jp/274/</a>

【Event】COO Yamada Speaks at "Business Value Creation Strategy by Generative AI" at AI & Artificial Intelligence EXPO as Board Member of GenAI.

<img src=/content/images/image_8eca0f7c791546a78ff2ea6898e82f69.jpg>On May 16, 2024, Recursive’s Co-Founder and CEO, Tiago Ramalho, spoke at a panel discussion of <a href=https://sushitech-startup.metro.tokyo.lg.jp/en/>SusHi Tech Tokyo 2024</a>, an event held in Tokyo aiming to achieve sustainable cities through advanced technology. Also joining the panel were Ms. Shiho Watabe, CEO of Shibuya Startups, and Ms. Jen Carter, Global Head of Technology of Google.org. The topic of the panel discussion was "Discussing Future of Technology: How AI changes human lifestyles,” which led to a lively, insightful discussion.During the panel discussion, Tiago talked about how AI can bring and improve sustainable change, especially from the the four pillars of Recursive's business: accelerating innovation, increasing productivity, preparing for future risks, and deliver better work/education. He also touched upon future challenges and outlook that is being faced when further utilizing AI to advance sustainability, to which he emphasized Recursive's direction and vision of using AI as a tool to empower humans rather than replacing them.<img src=/content/images/image_7a6b4c22ea07c83f624241586dba72bc.jpg>By speaking at this event, Recursive was able to provide insight into how much potential utilizing cutting-edge technology like AI has in order to achieve a sustainable society. Striving to achieve our mission of "building a fairer, more sustainable society by utilizing technology,” Recursive will continue to provide advanced AI technology to domestic and international companies to further drive sustainable change and innovation.<img src=/content/images/image_cae638d2c76c917f7ff6c6d8618d1dca.jpg>

【Event】CEO Tiago Speaks at SusHi Tech Tokyo, an Event to Promote High Tech to Achieve Sustainable Cities

Tiago, the CEO and co-founder of Recursive, and Yamada, the COO, have been interviewed by the online business media for entrepreneurs “Founder's Notebook” and the article was published.
The article introduces the story behind the company's founding, their thoughts on starting a business, and their vision for realizing a sustainable society using AI. Please take a look.<a href=https://sogyotecho.jp/recursive-tiago-yamada-interview/>https://sogyotecho.jp/recursive-tiago-yamada-interview/</a>

【Media coverage 】Interview Article with Tiago and Yamada, Co-founders of Recursive, Published on “Founder's Notebook/創業手帳”

On March 20th 2024, in Bangkok, at <a href=https://www.seatcon.co/>SEA-T Conference 2024</a>, an event highlighting the intersection of technology and industry, Recursive’s CEO Tiago Ramalho gave a presentation. The conference, themed "Future of Everything" brought together top executives and experts from various fields to discuss the transformative power of technologies and innovations.Tiago's presentation, on the topic: "The Future of Work and Education", explored AI’s potential as a catalyst for building a more inclusive and progressive future. He focused on how AI can empower and support the learning process, having a more skilled and adaptable workforce.<img src=/content/images/image_9b75be925bb6e70b51e4c80b17968853.jpg>The SEA-T Conference 2024 covered a wide range of topics, including ClimateTech, Entertainment, Metaverse, AgriTech, TravelTech, FinTech, Venture Capital, HealthTech, SportTech, FoodTech, MarTech, SustainableTech, and Hardware-IOT. It served as a unique platform for attendees to connect, collaborate, and exchange information, offering unparalleled access to innovative content.Recursive's participation at the SEA-T Conference reaffirms our commitment to use AI for achieving inclusive innovation in work and education.

【Event】CEO Tiago Speaks at SEA-T Conference 2024 in Bangkok, an Event on the Convergence of Technology and Industries

March 13, 2024Recursive Inc. presented a paper at the 30th Annual Meeting of the Association for Natural Language Processing in 2024 (hereinafter referred to as "NLP2024").Paper Abstract<ul><li>Title: "Exploring Trends in Natural Language Processing with Chatbots Equipped with RAG: Consideration of Methods for Utilizing AI Assistants to Streamline Document Analysis Tasks"</li><li>Lead Author: Kangying Li (Ph.D. in Engineering, Ritsumeikan University)</li><li>Co-authors: Shuying Song, Tiago Ramalho</li><li>Website: <a href=https://www.anlp.jp/proceedings/annual_meeting/2024/>Conference Website</a>. See “P2:ポスター 3月12日（火） 11:15-12:45” section to read the paper</li></ul>This paper, authored by our engineer Kangying Li, explores methods for utilizing AI assistants equipped with search extension generation in the field of research practice, targeting general users and students.This paper was submitted to NLP2024 conference and presented during the conference as a poster. NLP2024 serves as a benchmark event for the field of natural language processing in Japan, providing a platform for research achievements nationwide and worldwide.Overview of NLP2024 Conference<ul><li>Host: The Association for Natural Language Processing</li><li>Date: March 11, 2024 (Monday) to March 15, 2024 (Friday)</li><li>Venue: Kobe International Conference Center, 6-9-1 Minatojima Nakamachi, Chuo-ku, Kobe City, Hyogo Prefecture, 650-0046</li></ul>Background of Recursive’s PaperWe, as a service provider offering AI solutions to build a sustainable future, have developed unique AI products, as a startup based in Japan. To constantly propose optimal solutions to the increasingly complex management challenges faced by our customers, we believe it is crucial to continuously learn about and engage with the latest trends and technologies in the field of AI, particularly in natural language processing. Through this paper, we aim to discuss the potential applications of commercial products like ours in academic fields with other researchers and students, striving for effective AI implementations towards achieving a sustainable society.In addition, <a href=https://prtimes.jp/main/html/rd/p/000000020.000078033.html>this year we joined Generative AI Japan as a corporate member</a>, strengthening industry-academia collaboration for the future. We will continue to deepen our cooperation with academia through joint research and technology development projects, aiming to improve practical problem-solving capabilities and facilitate the societal implementation of cutting-edge research achievements.Our Recruitment ActivitiesCurrently, we are recruiting members for various positions, including the AI Division, to expand our business. For more information, please visit <a href=https://recursiveai.co.jp/en/careers/>here</a>.Company Overview<img src=/content/images/image_1e54e28aa73ee460417e0293e0fbfcbb.png>We offer AI solutions to build a sustainable future. Combining insights from various industries from environment, energy, healthcare, pharmaceuticals, food, to retail with advanced technological expertise and specialized knowledge in sustainability, we provide AI system development and consulting services.Company Name: Recursive Inc.
Co-founders: Tiago Ramalho, Katsutoshi Yamada
Location: 6th Floor, Shibuya S-6 Building, 1-7-1 Shibuya, Shibuya-ku, Tokyo 150-0001, Japan
Established: August 2020
Business Activities: AI R&D and provision of solutions related to SDGs
Website: <a href=https://recursiveai.co.jp/>https://recursiveai.co.jp</a>

【Press Release】Recursive Presented a Research Paper on Improving Efficiency of AI Text Analysis at the 30th Annual Conference of the Association for Natural Language Processing (NLP2024)

On February 29, 2024 (Thursday), as part of our ongoing commitment to fostering innovation, our COO Yamada participated in the 'Sustainability × Tech Pitch' event, hosted by Mizuho Bank.The event, held in a hybrid format, attracted over 100 viewers. Alongside our company, which leverages AI to address sustainability challenges, four startups focusing on circular economy and Agritech showcased their solutions.During the event, we introduced our four AI platforms.<img src=/content/images/image_e9d94dd03c9a8b06b3eab77dc0bf4033.jpg>Recursive will continue to explore various opportunities to promote AI innovation and achieve better education and work.

【Event】COO Yamada Presents AI Application in Sustainability at Mizuho Bank Pitch Event

Tiago was presenting in front of the camera during an online seminar.（Credit: Fujitsu Learning Media）On February 9, 2024, Recursive participated as a keynote speaker in an online seminar hosted by Fujitsu Learning Media. Known for their expertise in talent development, Fujitsu Learning Media trains around 170.000 individuals annually with their online course. This seminar marked the continuation of our recent partnership, announced in a press release on January 9, 2024. You can read the press release <a href=https://recursiveai.co.jp/ja/news/20240109/>here</a>.Our CEO Tiago Ramalho delivered a presentation titled "AI Trends: Future AI Picture and Required Skills". The seminar was targeted towards machine learning engineers and business practitioners seeking to leverage the power of AI.With an audience of over 400 participants, Tiago shared his perspectives on AI trends and essential skills. The Q&A session that followed allowed participants to delve deeper, asking specific and insightful questions about AI use cases, upcoming AI challenges, and more.For more details about the seminar, visit <a href=https://www.knowledgewing.com/kw/event/2024/0209.html>here</a>.This webinar marks another milestone in Recursive's commitment to driving AI innovation and delivering better work and education.

【Seminar】Our CEO Tiago shared his insights about AI trends during a seminar hosted by Fujitsu Learning Media

Aiming to Promote the Use of Generative AI as an AI Startup in the Sustainability FieldRecursive Inc. (Headquarters: Shibuya-ku, Tokyo, Representative Director: Tiago Ramalho, hereinafter referred to as Recursive) has joined Generative AI Japan (abbreviated as "GenAI") as a corporate member. In addition, our Representative Director and COO, Katsutoshi Yamada, has been appointed as a director. This organization was launched in January 2024 by UL Systems Inc. and Benesse Corporation as joint founders. Under the collaboration of industry and academia, we aim to enhance the industrial competitiveness of Japan as a whole by conducting education, career opportunities, collaboration, co-creation, rule-making, and recommendations in the use of generative AI.URL <a href=https://generativeaijapan.or.jp/>https://generativeaijapan.or.jp</a><img src=/content/images/image_cb8ed25317e8ff10687e3ccda9a616d5.jpg>(At the inaugural press conference with board members. Our COO, Yamada is the second from the right in the upper row) 【Association Overview】<ul><li>Name: Generative AI Japan</li><li>Founded on: January 9, 2024</li><li>Representative Director: Hiroaki Miyata</li><li>Auditor: Masamitsu Harada</li><li>Location: 1 Chome-34 Ochiai, Tama City, Tokyo 206-0033</li><li>URL: <a href=https://generativeaijapan.or.jp/>https://generativeaijapan.or.jp/</a></li></ul><img src=/content/images/image_0fdaa72a06cd678a90db087dfc4aaa99.png>【Activities of the Association (Scheduled)】Companies and experts involved in the use of AI will participate, discuss the way of utilizing generative AI in Japan, establish industry standards from use cases in line with user realities, and aim to spread best practices. In addition, the maintenance of guidelines and policy proposals will also be carried out as an important part of the activities. We aim for the social implementation of safe, fair, and productivity-enhancing generative AI.【Director Appointee】Katsutoshi Yamada, Co-founder & COO of Recursive Inc.After obtaining his MBA, he was involved in launching ethical fashion and starting two companies. He served as Sales Director at the AI startup "Cogent Labs", where he was involved in the launch of AI-OCR "Tegaki" and implemented AI introduction consulting to more than 200 companies. After continuously starting AI-related companies in Japan, Singapore, and Vietnam from the fiscal year 2018, he co-founded Recursive Inc. in August 2020. He graduated from Deakin University Graduate School of Business.【Our Background in Participating】Our opportunity to participate came from an invitation by Benesse Corporation, the initiator of this organization. We were established in August 2020 as an AI startup with a focus on the field of sustainability. We are currently pushing our business forward, consolidating a global perspective from our multinational members across 18 countries. In the process, we naturally see the preparation of environments for the use of generative AI overseas and the development speed of companies. In Japan too, constructing high fairness and safety for social implementation is an urgent issue. As a director in the future, under the cooperation of industry-academy, we will discuss the way of utilizing generative AI, establish industry standards from use cases that align with user realities, and work toward the dissemination of best practices.【Our Generative AI 】◼️Service: Enterprise AI Platform "FindFlow"This is a technology that applies generative AI to accelerate the utilization of documents, images, voices, and other data scattered throughout the company. FindFlow based AI solutions such as chatbots, business intelligence tools, and R&D assistants can achieve maximum efficiency by combining internal and external data through the analysis of these diverse data. In addition to a simple function of asking a question based on natural language and getting an answer, it also summarizes documents in a short time. For questions that cannot be answered with a single file, it investigates across multiple files, generates appropriate answers from there, and responds in natural language. It has also been developed to allow customization and the construction of your own models, and access control at the user level is possible within the company. It maintains data confidentiality, security, and compliance, and the information that in-house users have obtained using FindFlow is not reflected in external internet etc. Furthermore, the inference engine based on a large-scale language model supports both private models (such as GPT-4) and open-source models, and can be run on-premise to maximize data privacy.<a href=https://www.findflow.ai/en/>https://www.findflow.ai/en/</a>◼️Client Case Study: "Industry's First Double AI Simulation Service"Before the focus on generative AI in 2022, SBC Shonan Beauty Clinic and Recursive jointly developed. The actual case data of SBC Shonan Beauty Clinic were learned by an AI model applying the latest AI technology "GAN", realizing a high-precision double AI simulation service that can be commercially used.<a href=https://prtimes.jp/main/html/rd/p/000000069.000097803.html>https://prtimes.jp/main/html/rd/p/000000069.000097803.html</a>【About Recursive Co., Ltd.】<img src=/content/images/image_1e54e28aa73ee460417e0293e0fbfcbb.png>We are a service provider offering AI solutions to build a sustainable future. Combining insights from various industries such as environment, energy, healthcare, pharmaceuticals, food, retail, and advanced technological capabilities and expertise in sustainability business, we provide AI system development and consultation services. Unparalleled professionals lead the creation of a new society with world-standard technology in order to leave a better earthly environment and society for future generations.【Company Overview】Company Name: Recursive Inc.
Location: Shibuya S-6 Building, 6th Floor, 1-7-1 Shibuya, Shibuya-ku, Tokyo
Established: August 2020
Co-founders: Tiago Ramalho / Katsutoshi Yamada
Business Content:   Research and development of AI and provision of solutions related to sustainability
URL:         <a href=https://recursiveai.co.jp/>https://recursiveai.co.jp/</a>

【Press Release】Recursive, Appointed as a Director of "General Incorporated Association Generative AI Japan"

January 9, 2024
Recursive Inc.
Fujitsu Learning Media LimitedRecursive Inc, a company that provides AI research and development and solutions (Headquarters: Shibuya, Tokyo, Representative Director: Tiago Ramalho), and Fujitsu Learning Media Limited, a comprehensive talent development company (Headquarters: Kawasaki, Kanagawa, Representative Director: Masahiro Aoyama), today announced a business alliance to strengthen support for talent development and organizational development utilizing AI technology.【Background】
In recent years, AI technology has rapidly advanced, garnering attention as a technology that impacts society. The impact on business, such as improving productivity, creating new value, and solving social issues, is significant, and it is expected to be a technology that can be utilized regardless of industry.【Business alliance leveraging the strengths of the two companies】
This business alliance is aimed at providing AI utilization solutions in the education field for the first time by Recursive, which has various achievements in providing solutions related to AI research and development and sustainability, and Fujitsu Learning Media, which holds know-how on talent development and organizational development for about 170,000 people annually. We aim to provide rational learning environments that utilize AI technology and keep resources and costs down, provide equal learning opportunities regardless of region or affiliation, and provide new learning management services.【Free Seminar Event】<ul><li>Date and time: February 9, 2024 (Friday) 15:00-16:15</li><li>Seminar title: "AI Trends ~Future AI Picture, What Skills are Required?~" As the utilization of AI in business becomes more active, focusing on generative AI, Tiago Ramalho, who is leading the industry in the field of AI research and development as a senior research engineer at Google DeepMind, will talk about the current AI trends, future AI picture (future prediction), and the essential AI skills from a technical perspective.</li><li>Speaker: Tiago Ramalho, Co-founder and CEO of Recursive </li><li>Format: Online delivery via Zoom with simultaneous Japanese-English interpretation and Q&A</li><li>Target: Practitioners who want to use AI in business, engineers, promoters of talent development</li><li>Contact for media inquiries
Public Relations, Business Promotion Department <a href=mailto:flm-pr@cs.jp.fujitsu.com>flm-pr@cs.jp.fujitsu.com</a></li><li>Participation fee: Free
Registration URL: <a href=https://www.knowledgewing.com/kw/event/2024/0209.html>https://www.knowledgewing.com/kw/event/2024/0209.html</a></li></ul>【Future Development】
In this partnership, as a first step, we will conduct a PoC for a learning support tool using AI technology. The planned learning support tool will provide value to the learners by ensuring psychological safety because questions are not shared with other learners through one-on-one Q&A with AI. Effects such as habituating self-learning using dialogue type generative AI are also predicted. Furthermore, from the perspective of the management side, the use log of this tool will enable the listing of learners with high learning motivation and those who need support, and can provide new learning management information.<ul><li>Dialogic learner support</li><li>Evaluation of learner output (program)</li></ul>【Related WEB Sites】
Recursive Homepage: <a href=https://recursiveai.co.jp/en/>https://recursiveai.co.jp/en/</a>
Recursive Company Profile: <a href=https://recursiveai.co.jp/en/company/>https://recursiveai.co.jp/en/company/</a>【About Trademarks】
Product and service names mentioned are trademarks or registered trademarks of their respective companies.【About Recursive】
It is a service provider that provides AI solutions for building a sustainable future. By combining industry knowledge in diverse fields such as environment, energy, medical, pharmaceutical, food, and retail, with high technical capabilities and specialized knowledge in sustainability business, we provide AI system development and consultation services. Unparalleled professionals lead the creation of a new society with world-standard technology to leave a better global environment and society for future generations.【About Fujitsu Learning Media】
As the largest comprehensive talent development company in Japan, we provide total solutions for talent development. For companies, public institutions, and organizations of all industries, we offer about 2,930 open courses, ranging from "IT technical skills" to "human business skills", to about 170,000 students annually.
URL: <a href=https://www.fujitsu.com/jp/group/flm/>https://www.fujitsu.com/jp/group/flm/</a>

【Press Release】Collaboration for the demonstration of new services such as "Interactive Attendee Support" utilizing AI

Tiago, the CEO and co-founder of Recursive, and Yamada, the COO, have been interviewed by the economic media "Business Insider Japan", and the article has been published.Over the course of four installments, they introduce the background story of our established, each of personal history, and our outlook for the future. Please take a look.Published on 12/25/2023: A genius AI engineer from Google DeepMind teamed up with a Japanese serial entrepreneur. Creating a sustainable future with AI [Recursive1]<a href=https://www.businessinsider.jp/post-279907>https://www.businessinsider.jp/post-279907</a>Published on 12/26/2023: The aspiration towards the "father of artificial intelligence" led to joining Google DeepMind. The frustration held in Japan before starting an AI startup [Recursive2]<a href=https://www.businessinsider.jp/post-279909>https://www.businessinsider.jp/post-279909</a>Published on 12/27/2023: "I am a serial 'failure' entrepreneur". Social entrepreneurship and charity led to a period of unemployment and being a 'kept man' by my wife. Overcome to form the best buddy [Recursive3]<a href=https://www.businessinsider.jp/post-279911>https://www.businessinsider.jp/post-279911</a>Published on 12/28/2023: No external funding. The reason why the AI venture selected by "Google for startups" is committed to sustainability [Recursive4]<a href=https://www.businessinsider.jp/post-279913>https://www.businessinsider.jp/post-279913</a><img src=/content/images/image_54f18f0437f0eb7c52ac918816b3611d.png>

Media coverage for the interview article with Tiago and Yamada, the co-founders of Recursive, was published in four parts on "Mirai no Tsukurite" of "Business Insider Japan".

Recursive has been selected as a finalist in the "Japan Beauty and Fashion Tech Awards 2023" organized by Istyle Inc.<a href=https://prtimes.jp/main/html/rd/p/000000316.000005126.html>https://prtimes.jp/main/html/rd/p/000000316.000005126.html</a>This award is based on the "Japan Beauty and Fashion Tech Awards" established by ISTYLE under the theme of "Innovation that makes people happy," which shines a light on innovative efforts not only in the beauty and healthcare fields, but also in the fashion domain. This year, 13 companies including Recursive were selected as finalists out of 44 entries, and the awards ceremony was held on December 14, 2023.Recursive was recognized for its joint AI project "SBC AI" with SBC Medical Group, Inc.Recursive will continue to explore various opportunities to solve global problems and realize sustainable innovation.

Selected as a finalist in Japan Beauty and Fashion Tech Awards 2023

Shun Wakabayashi, our VP of Business, presented at the JAPAN GREEN INNOVATION CONFERENCE hosted by JETRO (Japan External Trade Organization) at COP28 (28th Conference of the Parties to the United Nations Framework Convention on Climate Change) being held in Dubai, United Arab Emirates (UAE). At the presentation, Shun has introduced the Sustainable Forest Management (SFM) project that Recursive is working on with Sumitomo Forestry and IHI to realize a decarbonized society. The presentation asked how this initiative can improve the management of peatlands around the world and promote forest conservation, which is the most important aspect of a decarbonized society.<img src=/content/images/image_dfe904a4540ccf8b8347c3da488411f7.jpg><img src=/content/images/image_2372a73a6981fa608dc22e630381cb4c.jpg>

VP Wakabayashi speaks at the JAPAN GREEN INNOVATION CONFERENCE hosted by JETRO (Japan External Trade Organization) during COP28

Recursive participated in "Climbers Startup JAPAN2023 - A Place to Meet and Accelerate the Startup Industry", a startup event organized by Sansan, and ran a booth at the event. The event was held at Tokyo Big Sight for two days on November 14 and 15, 2023. The event was attracted a total of 10,000 visitors.Hiroki Hasegawa, Business Development Manager of Recursive, took the stage at the event to introduce the use of AI in the area of sustainability.

Participated and exhibited at "Climbers Startup JAPAN2023" hosted by Sansan

COO Katsutoshi Yamada was a guest speaker at a research exchange meeting hosted by Nissan Chemical held in Nihonbashi, Tokyo on November 14, 2023. On the day, he introduced the current status of AI around the world, areas of AI application, and cases where AI has been introduced.

COO Yamada spoke at a research exchange meeting hosted by Nissan Chemical and introduced the use of AI.

Recursive has been selected as one of 10 Japanese startups for "Rock Thailand#5 Co-Creation of Japan-Thailand : Empowering Growth in ASEAN," a pitch event to create strategic partnerships between innovative Japanese startups and Thai conglomerates etc. The pitch event will be held on November 4 in Bangkok, Thailand.Rock Thailand is a pitch event co-hosted by the Embassy of Japan in Thailand, JETRO Bangkok Office, and CP Group, one of the largest conglomerates in Thailand.The event is expected to attract 100 participants onsite and 500 participants online, and is seen as an opportunity for both Japanese and Thai companies to create new business opportunities for sustainable economic development through pitches.For more information, please visit the following link.<a href=https://mediator.co.th/rock-thailand-5/>https://mediator.co.th/rock-thailand-5/</a>Recursive will continue to explore various opportunities not only in Japan but also overseas to realize Sustainable Innovation.

Recursive has been selected for "Rock Thailand" pitch event in Thailand

As indicated in the title, our CEO Tiago Ramalho and COO Katsutoshi Yamada will be giving a lecture at Chulalongkorn University in Bangkok, Thailand, on November 3.Chulalongkorn University is a prestigious national university with the longest history in the Kingdom of Thailand and is known for having the most difficult entrance examinations in the country.In their presentation titled "How to Develop AI Applications for Sustainable Business Transformation," Tiago and Yamada will present case studies and examples of technology applications related to AI and sustainability.They will give a lecture to 20 teachers from Chulalongkorn University, followed by a discussion.Recursive will continue to explore various opportunities not only in Japan but also overseas to realize Sustainable Innovation.

Our CEO Tiago and COO Yamada will give a lecture on AI x Sustainability at Chulalongkorn University in Thailand

We are pleased to announce Recursive's participation in Expand North Star 2023 held in Dubai, UAE, which is a dedicated startup event of GITEX, the largest IT exhibition in the Middle East and Africa (MENA) region.A Japan Pavilion will be set up at the event, and 18 Japanese companies will participate with the support of Japan External Trade Organization (JETRO).
Expand North Star is a event for startups that attracts a large number of government officials, regional heads of global companies, and conglomerates from the UAE and many other countries in the Middle East. This year, the event will be held at a new venue, Dubai Harbour, with an emphasis on providing networking opportunities among exhibitors and visitors.The official website is below:<a href=https://www.expandnorthstar.com/>https://www.expandnorthstar.com/</a>
We will report about the event later on on our website, so if you are interested in, please look forward to it.This will be Recursive's second visit to the UAE. We will take advantage of this opportunity and the support from various organizations to expand our business opportunities in the Middle East.

Recursive will participate in GITEX Expand North Star 2023, the largest startup event in the Middle East!

Our CEO, Tiago Ramalho, appeared in the video media “PIVOT”.PIVOT is a video media that delivers daily videos and articles free in order to enhance the mindset and skill sets of the new era. It delivers contents about entrepreneurs, businessperson, creators and others, who are the representatives of “Reiwa”, in a deep and fast manner.The number of subscribers to the Youtube channel has exceeded 1 million in just a little over a year since its launch, and it has attracted the attention of many businessman in Japan.
Tiago Ramalho, was featured in PIVOT, with title "[Cosmetic Surgery Also Changes With AI] Google’s AI group Google DeepMind engineer talks about future predictions/Taking away white-collar jobs/Importance of agile development [PIVOT TALK]".He talks extensively about the future of AI, Recursive's 4 winning AI strategies, the future of AI talent, AI development methodologies, and the future of Recursive.You can watch the video through the URL below.<a href=https://www.youtube.com/watch?v=uqTgyvm-cIw&t=9s>https://www.youtube.com/watch?v=uqTgyvm-cIw&t=9s</a>

Our CEO, Tiago Ramalho, appeared in the business video media “PIVOT”

We will hold a webinar to introduce the enterprise AI platform "FindFlow" on Tuesday, October 17th 

Plug and Play, a well-known startup support organization in Silicon Valley, is hosting the "Plug and Play Japan Summit2023" this month in Japan, where our CEO, Tiago Ramalho, will be speaking at the seminar.This event brings together various players in the startup ecosystem, including more than 70 domestic and international startups focused on the Summer/Fall 2023 Batch. It will be held on September 14th (Thursday) and 15th (Friday) at Toranomon Hills in Tokyo.The seminar in which Tiago will speak is titled "How Generative AI Will Change Society" and will be conducted with Jerry Chi, the representative of Stability AI, Inc.This is a discussion session with two leading companies about the impact of AI evolution, coexistence between humans and AI, new business models, and future possibilities. We will discuss the role of technology in the future society and how AI will affect creativity, industry, and social structures. The seminar will be conducted in English with consecutive interpretation in Japanese.If you are attending Plug and Play Japan Summit2023, please be sure to check out this seminar.Plug and Play Japan Summit2023 OverviewDate: September 14th (Thursday) to 15th (Friday), 2023Location: Toranomon Hills Forum 5th FloorWebsite: <a href=https://japan.plugandplaytechcenter.com/summit/summit-2023-sf/>https://japan.plugandplaytechcenter.com/summit/summit-2023-sf/</a>Seminar "How Generative AI Will Change Society" OverviewDate and Time: September 15th (Friday), 2023, 3:00 PMSpeakers: Jerry Chi, Stability AI, Inc. / Tiago Ramalho, Recursive, Inc.Website: <a href=https://japan.plugandplaytechcenter.com/summit/summit-2023-sf/#info-03>https://japan.plugandplaytechcenter.com/summit/summit-2023-sf/#info-03</a>

Our CEO, Tiago Ramalho, will be speaking at a seminar at the Plug and Play Japan Summit 2023.

Recursive will begin offering on-the-job training (hereinafter referred to as "OJT") services to develop in-house DX human resources into AI professionals. The program is designed to develop personnel who can plan and lead AI-related businesses within the company, highlighting the need for external resources to keep pace with the rapid progress of AI, but the resulting lack of personnel who can lead AI businesses within the company.Recursive's founder, CEO Tiago Ramalho, was once a senior researcher at Google DeepMind in the UK. Today, entrepreneurs from Google DeepMind continue to lead the AI industry by starting AI companies in Europe, the U.S., and other regions. Recursive's employees are from 18 countries (as of August 2023), and include AI engineers (machine learning engineers) and application engineers, Many of them have both global experience and understanding of the Japanese market.In order for companies to use AI not only to improve operational efficiency, but also to transform their business and increase its value, there is an urgent need to secure digital human resources within the company. One of the effective ways to develop human resources for in-house AI-related business is to cultivate various knowledge, skills, and perspectives to collaborate with engineers, project managers, and others. Recursive will provide on-the-job training to acquire knowledge and skills through actual work in an AI company to develop human resources who can lead AI businesses.This training program was established in response to requests from our clients, and since we are a company that develops original AI systems for companies, we can only offer this service to clients who have ordered AI systems from us. For each employee of the client, at least one Recursive engineer or other staff member is selected for on-the-job training to support the client's human resource development. The contract must be for the development of an AI project that spans a period of at least one year (both single and multiple projects are acceptable).In launching this OJT program, CEO Tiago Ramalho said, "Many Japanese companies understand the need for DX human resources because they are structured by business unit, but it is difficult to formulate company-wide DX strategies, and this is one of the reasons for the difference in speed between Japanese companies and Western companies, where DX departments are at the center of management. In addition, the evolution of AI, including generative AI, is extremely fast, so both speed and quality are needed in the training of human resources. We would like to support this training program as an opportunity for many Japanese companies to transform their businesses."Katsutoshi Yamada, co-founder of the program, also commented
As society changes, each employee must demonstrate his or her individual strengths to make management stronger. We have heard from many of our clients that they have outsourced their AI and DX projects to vendors and have not been able to accumulate the know-how within their companies.Recursive will continue to promote the compatibility of sustainability and business in society.

Launch of On-the-Job Training Service for DX Human Resource Development.

COO Katsutoshi Yamada will be speaking at the AI×Marketing conference titled "The Future of BtoB Marketing Collaborating with AI: Beyond Automation with ChatGPT/Generative AI" hosted by Excite Inc.The conference will be held online for two days on August 29th (Tue) and 30th (Wed), 2023, and will introduce concrete ways to utilize AI based on the latest trends in content generation using AI and successful case studies of content generation using AI that various companies have put into practice.Yamada, as a pro-consultant of Circulation Co., Ltd., will be speaking at the seminar "Ideas for New Businesses with AI-based Marketing" on August 30th (Wed), from 10:00.Overview of "The Future of BtoB Marketing Collaborating with AI: Beyond Automation with ChatGPT/Generative AI"Schedule: August 29th (Tue) - August 30th (Wed), 2023, 10:00-16:30Format: Online, free participationDetails and registration form: <a href=https://www.fangrowth.biz/20230829_30>https://www.fangrowth.biz/20230829_30</a>Overview of "Ideas for New Businesses with AI-based Marketing"Schedule: August 30th (Wed), 2023, 10:00-10:50Please join us.

Katsutoshi Yamada, COO of Recursive, will be speaking at the AI×Marketing conference, "The Future of BtoB Marketing Co-Created with AI - ChatGPT / Generative AI to Go Beyond 'Automation'" hosted by Excite Corporation.

Our CEO, Tiago Ramalho, will be speaking at a seminar during the Techsauce Global Summit 2023, one of the country's largest startup exhibitions, to be held in Bangkok, Thailand.The exhibition will be held from Wednesday, August 16 to Thursday, August 17 (local time), and is one of the largest exhibitions and summits in Southeast Asia, with approximately 150 exhibitors and visitors from over 30 countries expected.The seminar in which Tiago will speak is titled "Generative AI for Sustainability: Shaping a Greener and Smarter World," and will begin at 17:25 local time on Wednesday, August 16.The seminar will discuss how AI can be used to shape a sustainable society. If you are visiting the exhibition, please check out this seminar as well.Recursive staffs will also be on site, so if you would like to arrange a meeting, please contact us using this <a href=https://recursiveai.co.jp/en/contact/>contact form</a>.Techsauce Global Summit 2023 Overview.Dates: Wednesday, August 16 - Thursday, August 17, 2023 (local time)Location: Queen Sirikit National Convention Center (QSNCC)WEB: <a href=https://summit.techsauce.co/>https://summit.techsauce.co/</a>Seminar "Generative AI for Sustainability: Shaping a Greener and Smarter World" Overview.Date: Wednesday, August 16, 2023, 17:25-17:40 (local time)Location: Main StageWEB: <a href=https://summit.techsauce.co/agenda/talk?id=64ae4bc2b27bcf61877364f7>https://summit.techsauce.co/agenda/talk?id=64ae4bc2b27bcf61877364f7</a>

Our CEO Tiago Ramalho will be speaking at a talk session at Techsauce Global Summit 2023, a startup exhibition in Bangkok!

Recursive, Inc. announced that Katsutoshi Yamada, co-founder and COO of Recursive, had attended a panelist at the Tokyu Fudosan Holdings Corporation Group internal seminar. Here is a summary of the seminar.<img src=/content/images/image_e981e76ee7663165f45826c77cbc535a.jpg>From left to right: Mr. Aoki of Tokyu Fudosan Holdings Corporation, Mr. Yamada of Recursive, and Mr. Kuniyasu from alt Inc.The speakers were Mr. Takahiro Aoki ("Mr. Aoki"), who joined Tokyu Fudosan Corporation in 1994 and after planning asset development in the Urban Business Unit, is responsible for formulating overall DX strategies as General Manager of the Group DX Promotion Department of Tokyu Fudosan Corporation HD, and Mr. Kuniyasu from alt Inc., who develops and commercializes personal AI created by AI cloning technology. ("alt"), an AI DX consultant and business development manager, and Katsutoshi Yamada ("Mr. Yamada"), who founded Recursive with Tiago Ramalho in 2020. The talk session started.<h3>Q1.About the challenges of using generative AI internally and ideas that can be deployed in the real estate industry.</h3>Mr. Kuniyasu of alt Inc. mentioned that using the tool of AI is an important perspective, but what kind of value is created using that AI is even more important. He explained that he often hears that most employees are not utilizing ChatGPT even though it has been introduced in their companies, but this should be reversed as a way of thinking.He went on to talk about three perspectives based on his past experiences in the real estate industry. According to Kuniyasu, there are parts of the real estate industry that have not been digitized yet. For example, when sales know-how, which is difficult to be templated, is not being passed on to people, it is important to accumulate this know-how in the form of data and develop a system to automate it by learning natural language models. Next, he continues that it would be beneficial to collect a lot of information that exists in the real estate industry, such as location and pricing information, and have AI learn it.Finally, he explained that efficiency improvements, such as email automation, should also be considered, and by looking at these three improvements, the real estate industry will be able to make better use of digital technology.<h3>Q2. About the possibility of Tokyu Fudosan Holdings Corporation's utilization in the renewable energy business (solar and wind power).</h3>Mr. Yamada of Recursive started the discussion on the simulation of renewable energy generation using Recursive's product, Borealis ("Borealis"). He explained that renewable energy is a topic of interest not only in Japan but also worldwide, and that Borealis can simulate power generation using AI based on solar, wind, and other weather data, and by incorporating hazard maps and other data that take natural disasters into account, more precise simulations can be performed. Borealis can simulate power generation using AI based on meteorological data such as solar and wind power. He added that Borealis can support renewable energy projects on small sites because of the accuracy of its simulations, which can be done on a 100-meter scale.He also noted that Borealis can be used not only to simulate power generation, but also to solve climate-influenced issues such as demand forecasting, taking into account changes in human behavior due to climate effects. He said that in the future, he aims to use Borealis to solve a variety of climate-related issues.<h3>Q3. Ideas for using AI in the Shibuya area.</h3>Mr. Aoki of Tokyu Fudosan HD asked for opinions on how to utilize the large amount of information accumulated in an urban area such as Shibuya.Mr. Kuniyasu began by saying that by incorporating transfer information in addition to the wealth of information contained in human voices, the value of a place could be evaluated in a more multidimensional way. For example, he explained that this could be applied to marketing and urban planning, for example, by considering measures to increase marketing effectiveness for places with a positive image and measures to attract customers for places with a negative image.Mr. Yamada also mentioned the Shibuya Marui, which is scheduled to open in 2026 as Japan's first full-scale wooden commercial facility, and said that by proposing a sophisticated exterior design while taking sustainability into consideration, it may be possible to create new value for the Shibuya area. Specifically, he said that AI could propose multiple designs, select equipment and building materials that take CO2 emissions into account, and so on, with the aim of creating a sophisticated and attractive design. He added that he feels that these ideas could be brushed up by humans based on the AI's capabilities, creating new attractive spots in the Shibuya district that would attract attention from around the world.<h3>Q4. How to practically incorporate AI in the business developed by the Tokyu Fudosan Corporation HD Group.</h3>Recently, inbound demand has been increasing and the number of tourists from overseas has been growing, but in order to provide sufficient hospitality, there is a problem of a shortage of human resources at accommodation facilities, and we are trying to solve this problem by using AI to create shifts and optimize resources, etc. In the real estate brokerage business, we are also trying to solve the problem by using AI to improve customer service. Mr. Aoki asked what the two AI professionals thought about the use of AI.Mr. Kuniyasu stated that what Tokyu Fudosan HD's customers value is a prompt and appropriate response to general questions, and that he believes that AI may be able to replace 80% of all responses in this regard. He explained that by covering the part that can respond quickly and accurately, a coexistence can be realized where humans can show hospitality to the remaining 20% of the customers. He added that, in terms of improving customer service, there is also the option of using avatars and clones rather than chatbots to reply to customers.Mr. Yamada also stressed that the value of inbound travel is so important to Japan's economy that AI, which excels at multilingual support, should be utilized. He agreed with Kuniyasu that using technology to focus on hospitality and creativity, which only humans can do, would add value to Japan's tourism industry. He also cited his own multinational colleagues as an example of how he believes the language barrier will be dramatically reduced with the use of generative AI.<h3>Q5. What should employees keep in mind when implementing and operating AI in the company?</h3>Mr. Aoki raised a new consultation.He said that in the real estate business, various specialized knowledge such as construction, related laws, taxation, and accounting is necessary, but at the same time, he thinks it is important to acquire new knowledge of digital and AI, and to cooperate with experts in each field to advance the practice. He then asked if there were any important points or thoughts on how to acquire digital and AI knowledge in practice.Mr. Yamada suggested three things to try that he could recommend. First, make a habit of updating yourself. For example, he said, based on his own experience, that by using social networking services and following media that interest you, you will always acquire new information, and if you immediately search for and look up questions and things that interest you, you will acquire a lot of knowledge and generate new ideas over the course of a year.Second, try out the AI project. By setting an objective for an issue and turning in a specialized project, new knowledge and results can be gained. However, instead of being reckless, he explains that you can acquire practical skills by clarifying the objectives of the AI project and working on it.Recently, he added, more and more large companies and startups are collaborating on projects, and such collaboration, in which a large company sends personnel to a startup to participate in a joint project, can be a very beneficial experience, as it allows the two parties to enter the field and exchange ideas and opinions on how to proceed with each other's projects. He added that such a collaboration would be a very beneficial experience, as it would allow for mutual progress and exchange of ideas.In response, Mr. Kuniyasu pointed out that it is important to consider having people learn how to use AI and how to make the best use of AI. Specifically, he explained that he believes that by finding areas of work that can be automated or replaced by AI in the work we are currently doing, and utilizing AI, we can create an environment that will allow everyone to do more creative and rich work.<h3>Q6. Questions from participants.</h3>My first question is: We have a large amount of various paper data in our company, which we sometimes wish we could utilize for AI, but it is difficult to devote time to input data. Can you give me some advice on this? The question was, "What is your advice on this?Mr. Yamada said that Japan is a paper-intensive culture, and that in the past it was predicted that the amount of paper use would decrease, but in fact the data shows that it has not. He responded that it is still important to digitize paper, and now that AI-OCR has evolved and there are many tools that can easily digitize paper, it is important to try even small-scale implementation.The last question was about how some participants might think that generative and custom AI might be a threat in the real estate industry.Mr. Kuniyasu stated that the important thing is not to use AI, but to always think about what AI can do instead. He stressed that it is important, especially in the real estate industry, for people to be careful not to lose their original judgment in response to the results of AI judgments.The seminar ended with the advice to keep in mind that AI is not a threat, and that the world in which humans and AI coexist will become more and more widespread.<h3>Seminar Participant's Comments</h3>We also received the following comments from the participants after the seminar.“It was interesting to hear the real voices of the people concerned about generative AI reported in the news, etc."“As mentioned in the Q&A session, unlike the title of the seminar, I felt AI has a future or potential more than ever.”"I could understand the outline of AI. I will discuss this with the DX staff in my department to see if I can put it to practical use right away."Recursive will continue to promote its business to create a more AI-based sustainable society.

【Press Release】The seminar report on the "Threat and Potential Use of AI in the Real Estate Industry" Seminar hosted by Tokyu Fudosan Holdings Corporation, which Katsutoshi Yamada, COO of Recursive, Inc. attended as a panelist.

Our CEO, Tiago Ramalho, appeared on the Google Cloud Platform Podcast to talk about our company and its activities under the title "Tapping onto AI to build a more sustainable future with Recursive AI."The show can be heard <a href=https://googlecloudpodcast.libsyn.com/tapping-onto-ai-to-build-a-more-sustainable-future-with-recursive-ai>here</a>.
This program is available in English audio only.

CEO interview on Google Cloud Platform Podcast

The seminar recorded on May 25 will be re-transmitted due to popular demand.
Hiroki Murata, from Circulation, Inc. and Katsutoshi Yamada, COO of Recurisve Inc. will explain the four steps necessary to leverage generative AI in business for executives, new business managers, and those in charge of DX promotion.Generative AI is a hot topic in countries around the world through ChatGPT and other means, and this presentation will make the introduction of AI easier to understand, including the risks and precautions necessary for business introduction. If you are interested, please register by clicking <a href=https://circu.co.jp/pro-sharing/seminar/20230718-bizdev-ai-utilize-webinar/>the link.</a>Please check it out if you were not able to see it last time."Gaining competitiveness with “AI first”"Date: Tuesday, July 18, 2023, 12:00-13:00
Speaker:
Katsutoshi Yamada, Recursive Inc.
Hiroki Murata, Circulation Inc.Please note that this seminar will be held in Japanese only.

【Re-distributed by popular demand】Katsutoshi Yamada, COO of Recurisve Inc. will be a speaker at the online seminar "Gaining competitiveness with “AI first”" on Tuesday, July 18

Recursive Co., Ltd. and eiicon Co., Ltd., which operates the open innovation platform "AUBA," held a seminar entitled "The Future as Discussed by Industry Leaders in AI × Sustainability × Open Innovation" on June 29, 2023 (Thursday). We will provide a digest of the contents of this seminar.<img src=/content/images/image_ccc10ef0c42561334f4ecc6283473074.jpg>From left to right: Moderator Shun Wakabayashi, Tiago Ramalho, and Soichiro Murata. Takuya Kitagawa participated online.The speakers were Takuya Kitagawa, co-founder and director of the public interest corporation Well-being for Planet Earth, Tiago Ramalho, who founded Recursive in Japan in 2020, and Soichiro Murata, a professional in open innovation. The talk session started with discussion on the usage of AI and open innovation, which is also a trend in the world, to create more powerful businesses by utilizing fields that do not have resources within their own company, as highlighted by recent investments by Microsoft in OpenAI.Q1. In which fields is the utilization of generative AI already advanced?First, Mr. Kitagawa introduced how AI functions as an agent in place of humans. He explained how autonomous AI services provided by the American AI startup company Adept can understand natural language and derive answers. Continuing with the example of searching for real estate properties, if one speaks specifically about the desired requirements to the AI, it will search the website, show the location of the property on the map, and clicking on the detailed link will display the properties that meet the criteria. He explained that support AI that navigates results not by answering in natural language, but by navigating websites through clicks and transitions has already started to function as an AI agent.On the other hand, Mr. Ramalho explained that chatbots can answer questions for information that companies and governments disclose but are difficult for the general public to find, not just by creating text but also by generating text. He argues that this will make access to information more convenient and can achieve a transparent society. He also took education as an example of how AI functions as an agent, mentioning that generative AI can customize and provide appropriate questions for each student, which can enhance the learning effect, making it an area where generative AI can be expected to excel.Regarding education, Mr. Kitagawa further explained that a Shogi program that analyzes the winning percentage was another example of AI functioning as an agent. The winning percentage changes every time a move is made, and even those who are not familiar with Shogi can understand the game situation and context, making spectator sports of Shogi interesting again. He proposed that AI could play a major role in the field of "education x entertainment" as it would make anything more interesting if the context is understood.Q2. Challenges of generative AIMr. Ramalho said that the quality of the output is still a major challenge. Mr. Kitagawa mentioned that he has been given false answers by AI before, so reference checks and fact-checking of AI output are very important. Mr. Kitagawa continued saying that in the United States, ethical decision-making by AI and its ability to answer critical questions are being researched as challenges. The output of AI has a significant impact, as it can cause behavioral changes in people and can lead to good results in some cases, but it can also have the opposite effect. Therefore, it is a significant challenge to ensure the quality of AI output.Q3. Challenges on the Japanese corporate side in open innovation/ Q4. How to solve these challengesMr. Murata explained that there are three challenges for Japanese companies. First, they are not good at making decisions to work with other companies. Second, while they use open innovation as a means, it is not clear what they want to achieve with other companies and what they will do to achieve it. For example, "working on DX" or the goals and objectives are unclear. Third, they do not have knowledge of new technology fields, so they often delegate projects to startup companies.Regarding the differences between Japanese and American companies, Mr. Ramalho explained that in the United States, digital technology is regarded as the center and asset of business, and strategies are formulated by placing experts in that field. However, many Japanese companies do not have such an approach. Mr. Kitagawa explained that American employment is fluid, and people need to learn new things to survive. He also mentioned that employees' commitment is clear. Additionally, due to Japan's culture that values harmony, many cases spend time seeking consensus from many stakeholders for a single issue.After hearing the explanations from Mr. Ramalho and Mr. Kitagawa, Mr. Murata proposed that Japanese companies should review their strengths and expertise and actively impart industry and product knowledge to startup companies to work together to create new things.Q5. What can companies with technology do?Mr. Ramalho said that companies without data can educate on how to collect and clean up data and how to overlay that data with solutions like generative AI to create value. He also touched on the fact that data is as important an asset as cash, but many companies do not realize that. He wants to support greater understanding of the importance of data.Regarding open innovation, Mr. Kitagawa said it is essential to confirm what Japanese companies are expecting from it. For example, if it is ROI or an enterprise vision-based discussion. It is important to understand the needs and what kind of sense of satisfaction startups can provide to Japanese companies.Q6. How should generative AI be used for a sustainable society?Mr. Murata stated that it is essential to ask questions and learn basic things. Mr. Ramalho said that democratizing access to AI solutions is important, which will empower each individual, and he talked about the realization of a fair society where everyone has equal access. Mr. Kitagawa mentioned that when we think about sustainability, we often think about environmental issues, which are undoubtedly important, but he believes that it is possible to respond to the problem of declining population. He suggested that if there is a system that anyone can access, personal problems related to marriage and childbirth, such as financial concerns that prevent people from getting married, can be solved by a financial advisor system, and AI counselors can handle consultations such as infertility treatment. He proposed that such utilization of AI could solve individual problems.After the seminar, participants expressed positive feedback, stating that they gained new perspectives and knowledge on AI utilization and heard advice that would be helpful for realizing open innovation. Some also expressed that the seminar allowed them to think positively about the future of AI utilization and coexistence.As a multinational company with employees from 17 countries, Recursive will continue to report on global AI trends and related topics periodically.

【Press Release】Report on "AI x Sustainability x Open Innovation" Seminar - From case studies of agent functions in the real estate and education fields using autonomous AI to recommendations on how to use generative AI to address the issue of population decline

We will hold a webinar to introduce the enterprise AI platform "FindFlow" on Thursday, August 3 

Our case story was picked up by Google Cloud Blog, under the title of “Adopted Compute Engine as the AI development platform for tropical peatland afforestation project, and built a general-purpose model using its knowledge.”Please check out the article <a href=https://cloud.google.com/blog/ja/topics/customers/sfc-ihi-adopted-compute-engine-as-ai-development-platform?hl=ja>here</a>!

【Media】Case story on Google Cloud Blog

<h3>Contributing to Global CO2 Emission Reduction through Groundwater Level Prediction</h3>NeXT FOREST Inc., a joint venture of Sumitomo Forestry and IHI Corporation, has collaborated with Recursive Inc., an AI start-up company, to build an early AI model (AI hydraulic model) for tropical peatland management. With the introduction of this technology, it will become possible to carry out groundwater level predictions with the help of AI, something that only experienced engineers were previously able to do, based on the topographic maps and actual groundwater level measurement data that Sumitomo Forestry has collected over a span of 10 years. NeXT FOREST is going to use this AI technology to help curb CO2 emissions and forest fires in tropical peatlands around the world, including Indonesia.<img src=/content/images/image_fc68ab272038238936e7fce2a046ed14.jpg><h2>■ Background of Development</h2>Sumitomo Forestry and IHI Corporation are jointly developing a Hydraulic Model that simulates groundwater levels to effectively manage tropical peatlands. The management of tropical peatlands is crucial for addressing climate change issues, preventing peatland fires, and conserving biodiversity.Hydraulic models require a substantial amount of time and effort to gather and analyze diverse data, including precipitation, plant transpiration, elevation, slope, and weather conditions. That being said, due to the pressing nature of such issues, there is a necessity to adopt quicker and more efficient solutions. To address this challenge, Sumitomo Forestry and IHI Corporation are working on the practical application of AI technology to expedite the early deployment of groundwater level management techniques.The development of the said AI technology is being done in collaboration with Recursive Inc., which was established in Japan in 2020 by Dr. Tiago Ramalho, a senior researcher at DeepMind, an AI development company under Alphabet, Google's parent company. This collaboration aims to leverage Dr. Ramalho’s expertise to advance the practical application of AI in the early deployment of groundwater level management technologies.The AI model is a unique creation that combines AI-based machine learning using groundwater level data, which has been measured over more than 10 years in tropical peatlands in Indonesia managed by Sumitomo Forestry, as training data and physics modeling. With this technology, it becomes possible to predict groundwater levelsfor the entire designated area up to 7 days in advance. The predictions are made by leveraging various inputs, including topographic maps, measured groundwater levels at observation points, and actual precipitation data.In the future, there are plans to enhance the model so that each client can utilize the model to run simulations on the peatlands they manage, which will enable them to assess the impact of different channel operations and dam placements on maintaining proper groundwater levels.Flowchart of Physics-Based AI Model Architecture and Image of Groundwater Level prediction<img src=/content/images/image_81ba44e21253128f4aac6b04640b57f1.png>Sumitomo Forestry leverages a decade worth of accumulated data in its forest operations in West Kalimantan, Indonesia.↓<img src=/content/images/image_7293a168f8428a75d9950338811762f3.png>The AI model is fed with a range of data sources, including topographic details like land elevation and slope, rainfall statistics, and information on waterways.↓<img src=/content/images/image_e26996974d32143df6ed6cef4e415b1a.png>The AI model, that integrates an AI machine learning model with a physics-based model, forecasts groundwater levels several days into the future. The AI model targets groundwater level data when conducting its machine learning process.↓<img src=/content/images/image_48a4987e5639504a35ee92ad2b776f0f.png>The system is designed to issue a notification when the AI model predicts that groundwater levels in the forthcoming days may approach levels associated with potential fire hazards.<h2>■ Project Background</h2>Tropical peatlands, land areas marked by an accumulation of undecomposed organic matter from dead plants, are significant reservoirs of water and carbon. Spanning regions like Indonesia, the Congo Basin, and the Amazon, these peatlands cover over 82 million hectares globally (approximately twice the total land area of Japan) and are believed to hold at least 89 billion tons of carbon, nearly tenfold the worldwide carbon emissions in 2017.
Unregulated destruction of these peatlands through logging or slash-and-burn activities can lead to the decomposition of this organic matter, consequently releasing massive amounts of carbon dioxide into the atmosphere. This could disrupt the essential hydrological cycle of absorbing rainwater underground and releasing it back into the atmosphere through evapotranspiration, potentially resulting in extreme weather events such as droughts and floods.
Proper management of the groundwater levels in tropical peatlands is of utmost importance. If levels drop excessively, drying out can occur, turning the peatlands into highly flammable areas prone to forest fires. Conversely, overly high groundwater levels can inhibit tree growth, underscoring the necessity of continually maintaining an optimal balance."Sumitomo Forestry and IHI recognize the crucial necessity of conserving and appropriately managing tropical peatlands. Such actions are imperative for maintaining a stable environment for human life, including the achievement of carbon neutrality and the preservation of biodiversity and water cycles. To this end, IHI and Sumitomo Forestry are developing consulting services aimed at promoting worldwide implementation of tropical peatland management technology. This service merges Sumitomo Forestry's established high-precision land observation system for tropical peatlands with IHI's expertise in drone and satellite data utilization and weather observation and prediction technology, focusing on the aerospace sector.The Sumitomo Forestry Group is engaged in global business endeavors centered around wood, ranging from forest management to the manufacturing and distribution of timber building materials, contracting for standalone homes and medium to large-scale wooden structures, real estate development, and wood biomass power generation. Through the implementation of the “wood cycle”, Sumitomo Forestry's distinct value chain, the long-term vision “Mission TREEING 2030” is developed with the objective to augment the decarbonization efforts by increasing the carbon dioxide absorption capacity of forests, and promoting the proliferation of wooden construction for long-term carbon sequestration.
In accordance with the goal set out in the long-term vision, “accelerate the recycling-oriented forest business”, Sumitomo Forestry is committed to establishing a forest fund on a global scale and thereby expanding forest areas.IHI is committed to grow its venture into a highly sustainable business that harmonizes with nature, while concurrently striving to safeguard the “beautiful earth”. This involves addressing global-scale problems through aspirations for a CO2-free, recycling-oriented society and mitigating natural disaster risks. Moreover, by offering engineering services that combine a variety of solutions, IHI is actively contributing towards the realization of a carbon-neutral society by 2050.Recursive is a service provider of AI solutions for building a sustainable future. We provide AI system development and consultation services by combining our knowledge of diverse industries such as environment, energy, medical, pharmaceutical, food, and retail with our advanced technological capabilities and expertise in sustainability business. Our unparalleled professionals lead the way in creating a new society with world-class technology in order to leave a better global environment and society for future generations.<h2>■Project Logo</h2><img src=/content/images/image_3cca8335fbe4cfed6843b13d60cacd7e.png>

【Press Release】NeXT FOREST AI Model Building for Tropical Peatland Management

This is an implied translation of a press release issued in Japan on May 29, 2023.<h3>Seminar "AI x Sustainability x Open Innovation. Industry's Frontline Players Talk about the Future" To be held online and at the venue on June 29, 2023.</h3>Recursive, Inc. and eiicon, Inc. will hold a seminar on Thursday, June 29, 2023, titled "AI×Sustainability×Open Innovation: Industry Leading Players Talk about the Future.Takuya Kitagawa, a specialist in AI/data strategy and co-founder and board member of Well-being for Planet Earth, a public interest incorporated association, and Ricursive, an AI solutions provider after working for DeepMind, a global leader in AI companies Thiago Ramal, founder of Recursive, a provider of AI solutions, will explain how AI can contribute to creating a sustainable society and how it can be useful for business, citing examples from around the world. Soichiro Murata of Eikon, a professional in open innovation, will also explain in an easy-to-understand manner how AI technology can be incorporated into businesses to add value.This seminar will be held at CityLab Tokyo (Kyobashi, Chuo-ku, Tokyo) and can also be attended online at the same time. Pre-registration is required and participation is free of charge.Seminar Outline
Title
AI x Sustainability x Open Innovation: The Future as Discussed by the Industry's Leading Players
(It proceed in Japanese language, and English to Japanese interpretation will be provided in some parts.)
Date and Time
Thursday, June 29, 2023 (Doors open at 18:00, 18:30 - 20:00)
Networking Session
20:00 - 20:40 for those who wish to attend at the venue
Venue
CityLab Tokyo (Tokyo Square Garden 6F, 3-1-1 Kyobashi, Chuo-ku, Tokyo)
and online
Registration
<a href=https://techplay.jp/event/903842>https://techplay.jp/event/903842</a>Program (subject to change without notice)<ul><li>Introduction of Speakers</li><li>Overview of Generative AI and Open Innovation</li><li>Talk Session
i) How to apply generative AI to business?
ii) What are the market challenges for the diffusion and industrial application of generative AI?</li><li>Q&A</li><li>Social event (for those who wish)</li></ul>

【Press Release】We will hold a hybrid seminar on June 29

Hiroki Murata, from Circulation, Inc. and Katsutoshi Yamada, COO of Recurisve Inc. will explain the four steps necessary to leverage generative AI in business for executives, new business managers, and those in charge of DX promotion.Generative AI is a hot topic in countries around the world through ChatGPT and other means, and this presentation will make the introduction of AI easier to understand, including the risks and precautions necessary for business introduction. If you are interested, please register by clicking <a href=https://circu.co.jp/pro-sharing/seminar/20230525-bizdev-generative-ai-webinar/>the link.</a>"Gaining competitiveness with “AI first”"Date: Thursday, May 25, 2023, 12:00-13:00
Speaker:
Katsutoshi Yamada, Recursive Inc.
Hiroki Murata, Circulation Inc.Please note that this seminar will be held in Japanese only.

Katsutoshi Yamada, COO of Recurisve Inc. will be a speaker at the online seminar "Gaining competitiveness with “AI first”" on Thursday, May 25

We will hold a webinar to introduce the enterprise AI platform "FindFlow", which has received many inquiries, on June 8, 2023 (Thursday) starting from 1:00 PM.On the day, we will conduct a demo of FindFlow's features and usage, and also provide time for Q&A from everyone. We will send the URL for participating in the webinar to those who have registered in advance.Registration Form: XXX<Date and Time>June 8th, 2023 (Thursday) 13:00-14:15<Webinar Schedule>12:55 Admission starts13:00 Webinar starts13:45 Q&A session14:15 Distribution of survey and conclusion<Precautions>As we will be using Zoom, please download the app if necessary.<Materials>Will be sent after the webinar.<Notes><ul><li>Please watch the webinar in a location with a stable Wi-Fi environment.</li><li>Please keep your microphone off during the webinar.</li><li>We will have a Q&A session at the end. Please save your questions until then.</li></ul><Participation URL>Will be sent to the email address registered prior to the event.<Speaker>Recursive Inc.
COO Katsutoshi Yamada<Webinar Inquiries><a href=https://recursiveai.co.jp/en/contact/>Contact form</a>

We will hold a webinar to introduce the enterprise AI platform "FindFlow" on Thursday, June 8

Tiago Ramalho, President and CEO of Recursive Inc., will moderate a seminar at The ORIGIN Conference "How is AI shaping Southeast Asia's Next Generation (tentative title)" at BEYOND Expo 2023, to be held in Macau from May 10 to 12, 2023. BEYOND Expo 2023 is Asia's largest exhibition of more than 800 IT and other technology-related companies, and aims to promote technology in the Asia Pacific region and worldwide."How is AI shaping Southeast Asia's Next Generation (tentative title)" will feature talks by Pantheon Lab and SenseTime, global players in the field of artificial intelligence. Stage O "The ORIGIN Conference
Theme: How is AI shaping Southeast Asia's Next Generation
Date: Wednesday, May 11, 2023, 15:00-15:40
Speakers:<ul><li>Pantheon Lab</li><li>SenseTime</li></ul>

Our CEO Tiago Ramalho to Moderate Seminar at BEYOND Expo 2023 in Macau

On Wednesday, April 11th, 2023, Woojung Kim, our Solution Architect, spoke at "MARKETING X -23 Osaka" held at the Osaka International Convention Center and discussed the use of AI and ChatGPT in marketing, which is widely discussed today.Variety of people from a wide range of companies, from companies that are actively using AI to many people who are new to AI, attended the event. This report provides a digest of the seminar's presentations on "AI in Marketing" and "Challenges and Solutions for AI in Marketing".<h3>Use of AI in Marketing</h3>We asked a marketer who attended the seminar to give us an actual example of an AI application.1. "To whom" Market Search and Segmentation
While market size and segments are still often defined based on marketers' experiences and ideas, companies that are increasingly using AI models are analyzing customer and user data to more efficiently and accurately visualize and analyze potential market segments and their trends. AI models can predict demand, reduce unnecessary costs, and identify customer segments that may not have known they existed.2. “How” Marketers Communicate with Customers and Users
Equally important as market segmentation is branding your company. AI enables marketers to identify more effective communication methods (channels, time frames, frequency, etc.) based on past ad and email viewing data.3. "What" Content Production
After determining the potential market and how the company wants to brand itself, the marketer needs to decide what to deliver. There are mainly ad banners, videos, posters, copywriting, emails, campaigns, etc., all of which can be produced using an AI model; AI can be used to produce the most appropriate productions for each target segment based on customer data analysis and detailed customer segments.
The issues raised by the participants during the presentation are challenges that can be found in the use of AI in many areas, not only in marketing. While some aspects of the solutions vary from one situation to another, there are many common aspects as well. This article will describe some of the examples for your reference.<h2>Challenges and Solutions for Using AI in Marketing</h2>1. Data Availability
Many companies want to utilize the data but do not have it in the first place, or have data but are not organized enough to use it. The only way to solve this problem is to do it. As the saying goes, "Garbage In, Garbage Out," if you do not collect data that is meaningful from a business perspective and improve the quality of input data, the quality of output will also be low. This is an essential step for AI implementation.2. Data Accuracy
The most frequently cited issue was "We have started using AI, but the accuracy of the output data is very low.” To solve this issue, there are several things to consider, such as reviewing the introduced AI model itself and reviewing the operational environment (e.g., internal organization). If tuning of the input data, the model, or other maintenance is not performed after implementation, various problems can occur, such as a drop in accuracy (proximity between output data and true values) despite little variability (dispersion of output data), or a higher average accuracy of outputs but greater variability.3. Culture of AI use
It is often said that they have implemented AI, but it is not used in the field in the first place. The two main reasons we hear are "we don't know how to use it" and "we don't trust the data. The key to both is to develop an internal culture, but a more concrete solution that can be implemented immediately is the application of Business Intelligence (BI). BI is basically a technology wrapped by UX. Therefore, BI can make the onboarding of AI models and data more smooth, and visualize the effects of measures using AI model output data, thereby improving the confidence in AI and promoting its use in the field.We hope it will be helpful to you.

Event Report: "For Marketers to Master AI" at Marketing X Osaka 2023

This is an implied translation of a press release issued in Japan on April 11, 2023.<h3>Control security levels and AI Supports Employees in Improving Work Efficienc</h3>Recursive has developed and launched FindFlow (hereinafter "FindFlow"), which crowls files in the company network to collect information and respond to inquiries and writing requests from internal users. FindFlow is an artificial intelligence (AI) system that can collect information from files in the company network and respond to inquiries and writing requests from internal users.FindFlow is a tool to enhance the abilities of Large Language Models*1 like ChatGPT with internal documents, images, audio and other data types. By analyzing these data, FindFlow-based AI solutions such as chatbots, business intelligence tools, or R&D assistants can combine internal and external data for maximum efficiency. In addition to the simple functionality of asking a question and getting an answer based on natural language, it also provides a quick summary of the document. For questions that cannot be answered in a single file, the system will research across multiple files, generate appropriate answers from them, and respond in natural language.The system is also developed to allow for customization to build your own models, and access control at the user level within your company is possible. Data is kept private, secure, and compliant, and information obtained by internal users using FindFlow is not reflected outside the company, e.g., on the Internet.In addition, file types, not only documents and slides, but also email, chat tools, and other platforms used to share information within the company are subject to search and analysis, efficiently supporting employee work. The system is available in two languages, Japanese and English. Its reasoning engine based on Large Language Models supports both private models (like GPT-4) and open source models, which can be run on-premises*2 for maximum data privacy.*1: Large Language Models
A computer program trained on a large amount of text data collected from the Internet. It uses advanced machine learning technology that can analyze patterns and relationships in natural language data and generate new text or perform other language-related tasks based on its understanding.*2: on-premises
A software and a hardware infrastructural setup deployed & running from within the confines of the organization.■Technical Features<ul><li>User-level access control is available</li><li>Supports a wide range of formats including Microsoft Word, Microsoft PowerPoint, PDF, and other documents, Microsoft Teams, Slack and other chats, email, Notion documents, Salesforce, etc.</li><li>Support Large Language models such as GPT-4, and also private Open-Source models</li><li>Connects structured (database) and unstructured data (documents, messages, emails)</li></ul>■ Functions offered to internal users<ul><li>Enter a question and the AI will search, analyze, and respond to internal information</li><li>Cross-research across different documents and synthesize answers to complex questions</li><li>Summarizes long documents, etc.</li><li>Available in English and Japanese</li></ul>■Pricing/System Installation
Varies depending on system specifications.

【Press Release】Recursive Launches “FindFlow”,  AI Platform for the Enterprise

Woojung Kim, Solution Architect, Recursive Inc. will be participating at "MARKETING X -23 Osaka-" as a guest speaker on April 12 13:00 - 13:50, and will talk about the use of AI in marketing and ChatGPT, a recent hot topic..
This event will be held on Osaka International Convention Center, during April 12 and April 13.B stage
Date: Wednesday, April 12, 2023, 13:00-13:50Discussion Theme: “For Marketers to Master AI”About the detail, please see the <a href=https://marketingx.jp/osaka/>official website. </a>

Our Solution Architect, Woojung Kim, will be speaking at "MARKETING-X 23 Osaka"

This is an implied translation of a press release issued in Japan on March 23, 2023.<h3>Even at locations far from the default weather observation points
AI model developed that enables simulations with high accuracy of 100m mesh or better</h3>Recursive has developed Borealis, which analyzes past weather data and generates weather information such as precipitation, temperature, and solar radiation at any location in Japan, and has begun providing this information to companies and local governments.Borealis was developed based on various satellite data, AMeDAS and other meteorological data, and topography. The system analyzes information from weather observation points over the past 20 years, simulates weather information for any given point, and generates it using AI. Since the number of weather observation points in Japan is limited to a certain number of locations, it is not easy to obtain detailed weather data for points far from the observation points, and it has been common to spend manpower, time, and money to conduct surveys. Borealis supports quick decision-making by companies and local governments.For example, Borealis can be used to select sites for small-scale solar power plants and wind power generation, which are expected to be in demand in Japan in the future. Borealis can provide highly accurate information on the amount of sunlight, wind direction, and rainfall, making it possible to simulate the amount of electricity generated in a shorter time. Borealis can also be used for a wide range of other applications, such as hazard mapping for natural disasters, farmland planning for optimal agricultural production, and development of regional cities.Recursive is developing AI models for a sustainable society. Through Borealis, we will contribute to society in multiple ways, including the promotion of clean energy, disaster countermeasures, and regional development.■Technical Structure<img src=/content/images/image_d4e2537ec9c5561a25d381268d278fb3.jpg>

【Press Release】Recursive Launches Weather Data Generation AI “Borealis”

Katsutoshi Yamada, the COO of Recursive, appeared on Tokyo FM's "DIGITAL VORN Future Pix" to discuss Recursive's efforts, the low adoption rate of AI in Japan, and what the future holds. You can listen to the program here <a href=https://www.tfm.co.jp/podcasts/futurepix/>podcast</a> or on radiko. The program was also featured on <a href=https://news.yahoo.co.jp/articles/c246f3cd7467bcd46e95d1c3aeab194499466b09>Yahoo! News</a>. Please take a look.

Tokyo FM's "DIGITAL VORN Future Pix" was featured on Yahoo!

We are pleased to announce that Shun Wakabayashi, Executive Director / VP of Business, will be speaking at Google Cloud Next '22, a global conference hosted by Google Cloud.This event will focus on the challenges and opportunities required for digital transformation in a world of uncertainty, known as VUCA. It is an event where global business leaders will discuss the challenges and opportunities that will be required at that time.This year's theme is "What SDGs should Japanese companies tackle now?" and Mr. Taro Hosaka from the Forestry Agency, Mr. Masami Jenkins from Tokyo Electric Power Company Holdings, Inc., and Shun Wakabayashi from Recursive will speak from their respective perspectives.Please register and watch the event at the URL below.>>> <a href=https://cloud.google.com/blog/ja/topics/google-cloud-next/next-22-japan-original-contents>Click here for details</a>

VP of Business Shun Wakabayashi to speak at Google Cloud Next '22

We are pleased to announce that Recursive has been certified as a Google Cloud Build Partner in the Google Cloud Partner Advantage program.Google Cloud Build Partners are partners with a proven track record of developing solutions that integrate with or run on Google Cloud. With this certification, we aim to leverage Google Cloud technology to provide more clients with AI solutions that enhance sustainable innovation.For more information on the Google Cloud Partner Program, please visit<a href=https://cloud.google.com/partners>https://cloud.google.com/partners</a>For the detailed release, please visit the link below:>><a href=https://prtimes.jp/main/html/rd/p/000000008.000078033.html>https://prtimes.jp/main/html/rd/p/000000008.000078033.html</a>

Recursive Receives Google Cloud Build Partner Certification

Recursive has been selected for Microsoft for Startups, a startup support program offered by Microsoft Corporation.We will continue promoting the development of AI by utilizing the support of Microsoft's Azure and other technologies.■What is "Microsoft for Startups"?Microsoft for Startups, which is available in more than 140 countries around the world, is a startup support program that aims to create a global ecosystem by connecting startups with new customers and channel partners.Startups selected for the program will receive technical support, including Azure, as well as access to the company's partner network and dedicated resources to help them expand their business.Microsoft for Startups Overview<a href=https://www.microsoft.com/ja-jp/biz/startups/default.aspx>https://www.microsoft.com/ja-jp/biz/startups/default.aspx</a>For more information, please see the following link.<a href=https://prtimes.jp/main/html/rd/p/000000007.000078033.html>https://prtimes.jp/main/html/rd/p/000000007.000078033.html</a>

Recursive has been selected for "Microsoft for Startups

Our COO, Mr. Yamada, was featured in the AERA's serial article, "Hataraku Fufu Kankei”.The series introduces a couple's relationship from each of their perspectives, including how they met, how they got married, and how they divided household finances and chores after their marriage.The article covers the lifestyle of the couple, who are both entrepreneurs, how they first met, and so on.For more information, please visit the link below.<a href=https://dot.asahi.com/aera/2022072200026.html>>>https://dot.asahi.com/aera/2022072200026.html</a>

COO Mr. Yamada was featured in AERA's serial article "Hataraku Fufu Kankei"

VP of Business Shun Wakabayashi spoke at the "Sustainability Summit" hosted by Google Cloud.Shun Wakabayashi, Executive Director and VP of Business, participated as a panelist at the "Sustainability Summit" hosted by Google Cloud, which focused on "Contributing to the SDGs with Cloud Technology".This event was held to discuss how cloud technology can be applied to address the SDGs issues, as well as various case studies and solutions.Recursive presented a joint project with Mr. Tsuyoshi Kato from Sumitomo Forestry Co., Ltd., and Mr. Fumiharu Namiki from IHI Corporation on the theme of "Sustainable Forestry through Live Data and AI".Please register and watch the video from here.

VP of Business Shun Wakabayashi spoke at the "Sustainability Summit" hosted by Google Cloud

A YouTube video and report from the "Sonopro ONLINE Seminar" hosted by Circulation, Inc. where our COO Mr. Yamada spoke on February 15, 2022, are now available.Mr. Yamada participated in the seminar as a professional in the field of AI utilization in businesses linked to the SDGs and introduced advanced cases that turned sustainability/SDGs into new business opportunities.For more details, please check the following link.<a href=https://circu.co.jp/pro-sharing/mag/article/9089/>https://circu.co.jp/pro-sharing/mag/article/9089/</a>

The seminar report of "Sonopro ONLINE Seminar" in which our COO, Mr. Yamada spoke is now available

Collaboration between Circulation and " Dataseed" by Recursive has been featured in the Nikkei Electronic Edition.Please see below for details.<a href=https://www.nikkei.com/article/DGXZQOUC309O60Q2A530C2000000/>https://www.nikkei.com/article/DGXZQOUC309O60Q2A530C2000000/</a>

Our collaboration with Circulation has been featured in the Nihon Keizai Shimbun's e-edition

Recursive Inc. has announced a business alliance with Circulation Inc. for our ESG management all-in-one platform “Dataseed”.For more information, please visit the link below.<a href=https://circu.co.jp/news/20220531-3146/>https://circu.co.jp/news/20220531-3146/</a>

Business alliance with CIRCULATION has been started

Our CEO Tiago has been featured in INSIDER's "Meet the DeepMind mafia: These 18 alumni from Google's AI research lab are raising millions for their own startups, from climate to crypto" article.Please check it out from the link below.<a href=https://www.businessinsider.com/former-deepmind-employees-who-founded-own-AI-startups-2022-3>https://www.businessinsider.com/former-deepmind-employees-who-founded-own-AI-startups-2022-3</a>

Our CEO Tiago has been featured in INSIDER as a "DeepMind mafia”

“Success in applying AI isn’t about having the most sophisticated system—it’s about creating the right system for your unique needs.”This quote from Anthropic captures the essential truth of the modern AI landscape. While competitors promote complicated (and expensive) products that can write, design, code, and generate videos, the main question persists: How does it contribute to my company’s bottom line?In this article, we’ll explore what AI agents are, their advantages over tools like ChatGPT, and how we build them at Recursive to meet your business's unique needs.<h2>What is an AI Agent?</h2>An AI agent is an intelligent system designed to autonomously complete tasks on behalf of a user. It achieves this by breaking down goals into smaller tasks, using the right tools to accomplish these tasks, and learning and adapting over time. These agents can tackle complex challenges across various enterprise contexts, from improving customer support to accelerating drug discovery.At their core, AI agents use the advanced natural language processing capabilities of large language models (LLMs). This allows them to understand and respond to user inputs step-by-step while deciding when to engage external tools.trHowever, there are key differences between how LLMs are traditionally used in tools like ChatGPT—where you ask a question and receive a single response—and how they function in an AI agent framework. To better understand these differences, let’s examine the comparison below:<img src=/content/images/image_dc71796cbefc6ec31de2e656d9a7417c.png><h2>How Do AI Agents Work? </h2>Let’s say your marketing team is planning a product launch campaign and needs help developing a strategy. The query or input might be:"What’s the best campaign strategy for our new product launch, and how should we allocate resources?"Let’s compare how ChatGPT and an AI agent would handle this request.1. How ChatGPT Handles ItChatGPT provides an answer based on its training data:"To optimize a product launch campaign, consider aligning your strategy with market trends and targeting key customer demographics. A typical allocation could be 50% to digital marketing, 30% to events, and 20% to print ads. Use analytics tools to monitor performance and adjust accordingly."Limitations of ChatGPT:<ul><li>Generic Advice: ChatGPT lacks access to your company’s proprietary data, such as budget constraints or historical campaign performance.</li><li>No Direct Action: ChatGPT can’t interact with tools or databases to generate detailed, actionable insights.</li><li>Static Response: The recommendation is based on predefined patterns in training data, not tailored to real-time market conditions.</li></ul>2. How an AI Agent Handles ItAn AI agent approaches the task differently. It doesn’t just generate advice—it actively solves the problem and takes action.First, the AI agent breaks the query into smaller, actionable objectives, such as:<ul><li>Analyze internal sales data to identify peak engagement periods.</li><li>Gather market insights, such as competitor campaign timelines and current industry trends.</li><li>Access proprietary marketing budget records and historical campaign performance.</li><li>Use ROI modeling to simulate different resource allocation strategies.</li></ul>Then, the AI agent uses multiple tools and data sources to achieve these objectives:<ul><li>Retrieves insights from your company’s CRM, identifying historical customer engagement trends.</li><li>Queries external databases and uses web-searching tools to analyze competitors’ marketing activities.</li><li>Models various budget scenarios for digital ads, events, and print media, comparing projected ROIs.</li><li>It can also collaborate with other agents. For example, a financial planning agent validates budget allocations to ensure they align with company policies.</li></ul>Finally, after analyzing the data, the AI agent provides a detailed response:"Based on market trends, a launch in Q2 will likely maximize engagement. Allocate 70% of your budget to digital ads targeting younger demographics, 25% to in-person events in key regions, and 5% to print ads in trade magazines. This strategy is projected to deliver a 15% higher ROI compared to historical averages."As the campaign progresses, the AI agent monitors real-time data and adjusts recommendations. For example, if digital ads underperform, it might suggest reallocating resources to events or revising ad targeting strategies.<img src=/content/images/image_f34c527a2cecbbd8abf36bec37f9bd65.png>Unlike ChatGPT, which stops at providing generic advice, AI agents go further by taking responsibility for both generating insights and executing actions. This significantly reduces the need for human input at every step, ensures recommendations are grounded in real data, and drives effective implementation to achieve measurable results.You can learn more about the <a href=https://recursiveai.co.jp/en/solutions/recursive-ai-marketing-agent/>AI Marketing Agent</a> and other proprietary agents developed by Recursive on our <a href=https://recursiveai.co.jp/en/solutions/>Solutions page</a>.<h2>Benefits of Recursive AI Agents</h2>Leading companies like Johnson & Johnson and eBay are already recognizing the transformative power of AI agents, leveraging them to drive efficiency and innovation. In the coming years, adopting AI agents will no longer be optional—it will be a case of “adopt or be left behind.”At Recursive, we specialize in building cutting-edge, customizable AI solutions based on our proprietary agent platform. Here’s what sets our AI agents apart from the competition:<ul><li>High Customization </li></ul>We don’t do out of the box. Our team of top-tier machine learning engineers takes the time to understand your business’s unique challenges and leverages the best aspects of evolving technology to deliver measurable outcomes. If we haven’t moved the needle on the metrics that matter to you, we haven’t done our job.<ul><li>Faster Build Time</li></ul>We don’t need to start from scratch. With our agent platform, FindFlow, already pre-built, we can fine-tune it for your specific tasks and seamlessly integrate it into your business, saving valuable time.<ul><li>High Accuracy in Core Data Ingestion</li></ul>Our agent platform is optimized for retrieving and analyzing your proprietary documents and communications using RAG (Retrieval-Augmented Generation). This reduces hallucinations and ensures the outputs are accurate and relevant.<ul><li>High Data Security</li></ul>We prioritize your data security and transparency. By running AI agents on our own cloud infrastructure, we avoid vendor lock-in, giving you full ownership of your data and fostering innovation.<ul><li>Multi-Lingual Support</li></ul>Most AI agents developed in the West rely heavily on English training datasets, which can become a significant limitation for non-English-speaking companies. At Recursive, our platform is trained on both English and Japanese datasets, making it easier to fine-tune for other languages and adapt to global business needs.If you’d like to explore examples of AI agents built by Recursive, please visit our <a href=https://recursiveai.co.jp/en/solutions/>Solutions page</a>. For a consultation on how an AI agent can deliver measurable results for your business, feel free to contact us at sbdm@recursiveai.co.jp.

In this article, we’ll explore what AI agents are, their advantages over tools like ChatGPT, and how we build them at Recursive to meet your business's unique needs.

What are AI Agents, and How Do We Build Them at Recursive?

The rapid adoption of generative AI is transforming industries worldwide. According to McKinsey's 2024 report, AI adoption has surged to 72% of organizations, with 65% of respondents reporting regular use of generative AI in at least one business function.This rapid growth emphasizes the need for cost-efficient, customizable, and privacy-focused solutions like small-size open-source large language models (LLMs). However, as highlighted in our <a href=https://recursiveai.co.jp/en/blog/best-open-source-llm-models/>benchmark test results</a>, these models often fall short in performance compared to their larger counterparts. To address this, we explored fine-tuning and prompt optimization as strategies to close the gap.<h1>What Are Small Open-Source LLMs?</h1>Small open-source LLMs are publicly accessible AI models with fewer parameters (under 10B), making them deployable on standard hardware such as consumer-grade PCs. Their accessibility makes them an attractive option for organizations and individuals with limited technical resources.<h3>Key Advantages</h3><ul><li>Cost Efficiency: Operate on standard systems with minimal resource requirements, reducing costs.</li><li>Customizability: Can be fine-tuned to specific tasks, delivering tailored solutions.</li><li>Privacy Assurance: Deployed locally, eliminating the need for external APIs and ensuring data security.</li></ul>In this article, we focus on optimizing three popular small open-source models: Qwen 2.5-7B (Alibaba), Llama 3.1-8B, and Llama 3.2-3B (Meta).<h1>Evaluating RAG Capabilities of Small Models</h1>We assessed the models using Recursive’s proprietary <a href=https://recursiveai.co.jp/en/blog/introducing-flow-benchmark-tools/>Flow Benchmark Tools</a>, designed to evaluate models on real-world tasks. The benchmark utilized a dataset of Japanese government documents paired with challenging questions to test two critical capabilities:<ol type=1><li>Question-Answering Using Retrieval-Augmented Generation (RAG)</li><li>Whole Document Analysis</li></ol>Flow Benchmark Tools evaluated model performance across English and Japanese, using a scoring system from 0 (worst) to 10 (perfect).<h1>Prompt Optimization: A Low-Cost Enhancement</h1>Prompt optimization involves refining how instructions or input text are presented to an AI model. Think of it as crafting a clear, well-structured directive to guide the model’s response. This low-cost strategy is often the first step to improving performance without altering the model itself. In our experiments, the optimizations were focused on document analysis, as question-answering with RAG relies more heavily on retrieval quality, which is less influenced by prompt adjustments.<h3>1. Query Placement Improves Accuracy</h3>The position of the query within the prompt significantly influenced results. For example, placing the query after the document block for the Qwen 2.5-7B model improved its score from 6.35 to 6.88. This demonstrates the value of careful prompt design in enhancing performance.<img src=/content/images/image_a9c925f5c6ecdfd963de592a5af2e4b7.png><h3>2. Addressing Hallucinations</h3>Hallucinations occur when AI models generate incorrect or fabricated answers with confidence. For example, a model might invent URLs that do not exist or provide information absent from the input data. This issue is particularly problematic for tasks requiring factual accuracy, such as document analysis.In our tests with Llama 3.1-8B, minor prompt adjustments were insufficient to mitigate hallucinations. However, using a structured data prompt—adapted from DSPy—significantly reduced hallucinations, improving the model’s performance score from 4 to 8. This template defines clear input fields and enforces strict output formats, ensuring the model produces accurate and consistent responses.Structured data prompt template (adapted from DSPy):<img src=/content/images/image_609d34255c09d42c7bb9d717a8939030.png><h1>Fine-Tuning LLMs on a Custom Dataset</h1>Fine-tuning adapts pre-trained AI models by further training them on a specialized dataset aligned with specific tasks. Using LoRA (Low-Rank Adaptation), we efficiently trained the models by adding small, trainable modules to existing layers, which adjust weights without changing the original parameters, reducing memory usage and speeding up training. To align the models with our benchmark tasks, we created a dataset modeled on the Flow Benchmark instruction style. Fine-tuning was applied to two models, Llama 3.1-8B and Llama 3.2-3B, and produced mixed results depending on the task.<h3>1. Question-Answering with RAG</h3><ul><li>Llama 3.1-8B showed negligible improvement.</li><li>Llama 3.2-3B achieved a 7% improvement.</li></ul>The limited improvement stems from the task’s reliance on information retrieval—a capability the models already handled well. Fine-tuning focused more on helping the models follow task-specific instructions than introducing new knowledge. Since the Llama 3.1-8B model’s performance was already comparable to larger models like Llama 3.1-70B, further fine-tuning provided minimal additional benefits.<img src=/content/images/image_0f27e302009957b243bf35870cabec53.png><h3>2. Whole Document Analysis</h3><ul><li>Llama 3.1-8B improved by 10%.</li><li>Llama 3.2-3B achieved a 26% boost.</li></ul>These significant improvements resulted from the fine-tuning process, which enhanced the models’ ability to understand and adapt to document analysis tasks, leading to higher scores.<img src=/content/images/image_5a5d38fca4f4bfeec3a22ea3be4a0a26.png><h1>Key Takeaways</h1>The results presented in this article highlight the importance of fine-tuning and prompt optimization in maximizing the potential of small open-source LLMs:<ol type=1><li>Prompt Optimization: While thoughtful prompt design can improve model performance in many cases, addressing hallucinations remains a complex challenge. Various approaches exist, but most lack general applicability. However, in our experiments, the structured data prompt proved highly effective. While this approach worked in our scenario, it should be seen as one of many potential methods rather than a universal solution.</li><li>Fine-Tuning: Aligning fine-tuning datasets with task-specific requirements is crucial. Although fine-tuning showed limited impact on question-answering with RAG, it delivered substantial improvements for complex tasks like document analysis, enabling more accurate and reliable outputs.</li></ol>By combining these techniques, small open-source models can narrow the performance gap with larger alternatives while maintaining advantages in cost, customization, and privacy.At Recursive, our commitment to open-source technologies reflects our vision of democratizing AI while empowering enterprises to build fairer, more sustainable solutions. Reach out to us at sbdm@recursiveai.co.jp to discuss how our tools can enhance your AI strategy.

How can small open-source LLMs rival larger models? Discover how Recursive used fine-tuning and prompt optimization to reduce hallucinations and achieve a 26% improvement in document analysis performance. 

Fine-Tuning Small LLMs: Improved Performance with Fewer Resources

At Recursive, we are committed to delivering cutting-edge AI solutions, and today, we are excited to share the results of our extensive evaluation of open-source LLM models. As more businesses embrace open-source technologies for their transparency and customizability, one key question remains: Can these models match the performance of closed-source alternatives like GPT-4?To address this, we used our recently launched <a href=https://recursiveai.co.jp/en/blog/introducing-flow-benchmark-tools/>Flow Benchmark Tools</a> to thoroughly test the performance of leading open-source models by integrating them into FindFlow, a platform developed by Recursive for text generation, search, and analysis.The tests focused on assessing two critical functions: question answering using retrieval-augmented generation (RAG) and whole document understanding. These capabilities form the core of our FindFlow platform as SearchAI and AnalysisAI features.<h1>What Are Open-Source LLM Models?</h1>Open-source LLMs are publicly accessible models whose code, architecture, and sometimes training data are made available for use, modification, and redistribution. Businesses can leverage these models for their own applications, taking advantage of three key benefits:<ul><li>Customizability: Models can be fine-tuned to meet specific business needs.</li><li>Transparency: Full visibility into the model’s architecture and inner workings.</li><li>Cost Efficiency: Open-source models eliminate the need for subscription fees or API costs associated with closed-source systems.</li></ul><h2>Open-Source vs. Closed-Source LLM Models: A Quick Comparison</h2><img src=/content/images/image_fe558b3edda755ded978b0b04f54b96c.png><h1>Flow Benchmark Tools: A Closer Look</h1>To provide accurate and actionable insights, we used Recursive’s proprietary Flow Benchmark Tools to test SearchAI and AnalysisAI features of FindFlow on several open-source LLM models provided by top companies such as Google, Microsoft, Meta, and others.<ul><li>SearchAI focuses on question-answering using Retrieval-Augmented Generation (RAG), assessing how well models retrieve accurate, context-specific information from documents.</li><li>AnalysisAI centers on whole document understanding, evaluating how effectively models extract insights from complex, full-length documents.</li></ul>A unique feature of our tools is the multilingual capability, allowing us to evaluate model performance not only in English but also in other languages, with a specific focus on Japanese. This multi-language assessment makes our benchmarks particularly relevant for global applications.The approach used in the Flow Benchmark Tools mirrors real-world use cases by utilizing a dataset of Japanese government documents with challenging questions to ensure a thorough evaluation. It employs a multi-model approach, including state-of-the-art models such as GPT-4, Claude 3, and Gemini, to generate objective results, with ratings from 0 (worst) to 10 (perfect). For more details, check out our <a href=https://recursiveai.co.jp/en/blog/introducing-flow-benchmark-tools/>launch announcement article</a>.Our Flow Benchmark Tools are publicly available on<a href=https://github.com/recursiveai/flow_benchmark_tools> GitHub</a> and<a href=https://pypi.org/project/flow-benchmark-tools/> PyPi</a>.<h1>Performance Overview: Evaluating Open-Source LLM Models by Size</h1>It’s essential to analyze open-source LLM models by size, as performance varies significantly between big, medium, and small models. Larger models generally deliver superior accuracy, especially in complex tasks, while smaller models offer a cost-efficient solution, capable of running on personal computers with lower hardware requirements.To establish a performance benchmark, we included GPT-4o, a closed-source model renowned for its strong capabilities. When integrated with FindFlow, it achieved impressive scores of 8.27 for SearchAI and 8.9 for AnalysisAI.<h2>Big Size Models: Key Insights</h2><img src=/content/images/image_7c3d51a86cc5c0025effb7b5b65332c5.png><ul><li>Llama 3.1-70B (Meta) and Qwen 2.5-32B (Alibaba) emerged as top performers among the larger models. Both demonstrated robust capabilities, making them ideal for more complex tasks.</li><li>Llama 3.1-70B showed particularly strong instruction-following in English but lacked built-in support for Japanese, which could be improved through fine-tuning.</li><li>Mistral small-22B (Mistral AI) delivered lower scores in Analysis AI, especially for Japanese tasks, limiting its effectiveness in complex document understanding.</li><li>Gemma 2-27B (Google) showed weaker overall performance, particularly when handling larger documents. By design, it supports an 8,000-token context length, which is significantly lower than other models. However, its effectiveness drops when the context exceeds 7,000 tokens, limiting its capability for complex tasks.</li></ul><h2>Medium Size Models: Key Insights</h2><img src=/content/images/image_6aa4b8d39362eeccd6006f509f413c04.png><ul><li>Qwen 2.5-14B (Alibaba) led the medium-size category, delivering strong results in both English and Japanese queries, with particularly high AnalysisAI scores.</li><li>Mistral Nemo-12B (Mistral AI) showed strong English SearchAI scores but lagged in Japanese SearchAI, indicating inconsistencies across languages.</li><li>Phi-3-medium-14B (Microsoft) underperformed in AnalysisAI compared to other mid-size models, highlighting limitations in handling complex document understanding tasks. Additionally, Japanese is not on the list of supported languages, restricting its usefulness for multilingual analysis.</li></ul><h2>Small Size Models: Key Insights</h2><img src=/content/images/image_767fd7ce013d03534b06b81ce26e9bd4.png><ul><li>GLM4-9B (Zhipu AI) demonstrated top-tier performance, surpassing many larger models in SearchAI and AnalysisAI tasks.</li><li>Llama 3.1-8B (Meta) showed solid performance in SearchAI. In AnalysisAI, it performed moderately well for its size, but its Japanese capabilities lagged behind. Fine-tuning could enhance its ability to process Japanese queries more effectively.</li><li>Qwen 2.5-7B (Alibaba) showed respectable performance relative to its size, excelling in certain tasks, making it a competitive model in the small-size category.</li><li>Gemma 2-9B (Google) underperformed compared to other small models. Although it supports an 8,000-token context length, its effectiveness significantly declines beyond 5,000 tokens, posing challenges for handling more demanding tasks or long-form content.</li></ul><h1>Open-Source LLMs Show Promise with the Right Integration</h1>Our Flow Benchmark results showcased the top-performing open-source LLM models across three size categories. For big-size models, Llama 3.1-70B (Meta) and Qwen 2.5-32B (Alibaba) excelled in both SearchAI and AnalysisAI. In the medium category, Qwen 2.5-14B (Alibaba) stood out, particularly for its multilingual strengths, performing well in both English and Japanese. Among small models, GLM4-9B (Zhipu AI) impressed by outperforming larger models in accuracy and efficiency.These findings show that with the right integration—like our FindFlow platform—open-source LLMs can achieve performance on par with closed-source models while providing greater flexibility and cost savings. To discover how FindFlow can save your business 20+ hours a month and guide you in selecting the best platform, reach out to us at <a href=mailto:sbdm@recursiveai.co.jp>sbdm@recursiveai.co.jp</a>.

This article reveals the results of testing top open-source LLM models to see if they can match closed-source alternatives like GPT-4. Using Recursive’s Flow Benchmark Tools, we evaluated models from Google, Microsoft, Meta, and others on question-answering with Retrieval-Augmented Generation (RAG) and whole document understanding.

Best Open-Source LLM Models: Flow Benchmark Results Revealed

In late February and early March of 2024, a team from Recursive embarked on a significant business trip to Indonesia. The primary objective was to enhance their AI model designed to predict future Ground Water Levels (GWL) in the tropical peatlands of West Kalimantan. This initiative aims to prevent forest fires, anticipate floods, and ensure the growth of plants. The team, consisting of <a href=https://www.linkedin.com/in/qsiutkowski/>Quentin Siutkowski</a>, <a href=https://www.linkedin.com/in/dmitrylyamzin/>Dmitry Lyamzin</a>, and <a href=https://www.linkedin.com/in/david-landup-859455144/>David Landup</a>, had been working on the GWL project for over two years and faced numerous challenges that required on-site observations and experiments to resolve. Discover their insights and passion from this through an interview!<img src=/content/images/image_c82ef93b5dfc1704c2ecc0f8e3235c71.png>From left to right: Quentin, Dmitry, and David enjoying a meal.　<a href=https://borealis.jp/en/>Borealis</a> is an AI model as one of Recursive’s 4 Technology Platforms, using advanced physics-based modeling and AI to improve environmental management. It helps with reforestation, carbon storage, water resource preservation, and minimizing fires and ecosystem degradation. Borealis integrates large-scale climate and geological data, hybrid environment simulation, satellite data, and ground observations to support sustainability use cases like water level management, fire hazard mapping, renewable energy forecasting, and power demand forecasting. Here the project focuses on groundwater levels. 　Question #1 : How was it to finally go there directly when you've worked on this project for more than two years?Dmitry: It was very exciting! We finally got to see where the data comes from. Riding a speedboat for three hours into the jungle and then arriving to find chickens everywhere was surreal. It was a unique experience that helped us understand their daily challenges and the life they live. Being there allowed us to develop personal connections with them, which brought a lot of motivation.　Quentin: People who work on the site live there, so it's an isolated area with everything needed to sustain themselves. It was different from just seeing images and data, we could play volleyball, eat, and talk with the local team, which was very motivating.　David: There were always be question marks about the things we've done, the assumptions we've made, and not just resolving those questions, but finally seeing what we spent so much time trying to understand in real life was a surreal feeling. I remember sending messages to some of our colleagues who also worked on the project beforehand saying “This is the thing we couldn’t figure out a year ago!”. So finally being able to see those in real life was a pretty surreal feeling.　Question #2 : Did this trip allow you to improve your solution Borealis? How, if yes?Dmitry: We definitely learned a lot about the irrigation system they use there! We will use this knowledge in future model developments. We finished a certain phase and delivered a product before we went, so we haven’t fully used the acquired knowledge just yet, but we will in the future. 　Question #3 : So when you were there, did you receive any feedback on the work you did before?David: We had multiple discussions with people there, both up and down the chain of management, and received good feedback on what we did as well as potential areas for improvement. The dynamics of the area are complex, making it difficult to model and understand what's happening. However, we gained valuable insights into the daily problems people face and what could be resolved by our technology. Overall, it was a gratifying experience.　Quentin: Communication between the head company and the subsidiary isn't straightforward; it's restricted to specific channels and a few key individuals. Engineers on the ground have certain concerns and ideas that people in Tokyo don't necessarily share. We strive to balance the needs of both the head company and the engineers, which is a challenging task but ultimately benefits everyone involved. Our prior interactions with them established a foundation of trust, evident in the warm reception and welcome we received as guests. They recognized our genuine efforts to improve their lives and work collaboratively, which further deepened the mutual trust between us.<img src=/content/images/image_4055e5c54365574ceaaba5f9ba03f479.png>David getting on the speedboat.　Question #4 : How do you feel this project aligns with the values and goals of your team?Dmitry: This project is directly related to our values and goals as a company. It's a sustainable operation in the jungle where people genuinely care about their business and its impact on the wider territory. They care about the wildlife and have dedicated personnel responsible for conservation management, which aligns very well with our values.　Quentin: I think it's the flagship project of the company when it comes to sustainability. While we are getting more projects aligned with our values, this one is the bread and butter of the company. The impact extends far beyond just the facilities (physical infrastructure and operational areas that are part of the project), as most of the area consists of conservation zones. They shared videos of orangutans and other endangered species they are protecting. They are putting cameras and sensors to keep track of the populations and ensure they are not disturbing them.　David: Highly aligned, and has the potential to prevent massive carbon-emitting events. Peat has extremely high carbon sink effects, capturing a lot of carbon that gets stored in the soil and also helps massive trees grow into rainforests. This biomass is then used to build housing, essentially converting CO2 from the air into housing for people. Tropical forests and peatlands are particularly good at this, but they require specific weather conditions, making it crucial to maintain the existing ones. For this specific reason, peat is also very flammable and it’s easy to re-release that same captured carbon back into the atmosphere. We're hoping that our work may mitigate fire hazards, such as the 2015 fire that burned 2.6m hectares of forests in Indonesia, causing up to ~1.3B tonnes of CO2 to re-enter the atmosphere, which is higher than the yearly CO2 emissions of many countries. This was a planet-scale event.<img src=/content/images/image_8ae4cc11afb62508fde919f2138c1925.png>From left to right: Quentin, Dmitry, and David at the airport.　Question #5: How do you see the Borealis team evolving now as a result of this project?Dmitry: Now that we've completed this project and been there, we have gained more expertise. We could look at growing in the future by taking on a bigger area and more long-term contracts.　Quentin: The Borealis team will expand to support projects similar to this one, and we are also working on other projects, including long-term ones with other companies. Sometimes we see connections between projects, even if they seem far apart on paper. Some are based in Japan, some in Indonesia. The environments are different, but the approach, knowledge, and systems we develop can be reused or adapted. We have many projects related to water, which is a common thread that connects them. We also have many exciting ideas for Borealis.　<img src=/content/images/image_2c7087d072bf5366404de2329b8df14a.png>Dmitry in the speedboat.　Question #6: How can Borealis concretely help?Dmitry: It was a good surprise to see how sophisticated their water operations and canal systems are. They know what they're doing and need minimal intervention. Our approach may not be to tell them what to do daily, but to support them in their activities. They are already doing a lot of good things, but there's still a lot of guessing. We hope to make their guessing more accurate and science-based.　Quentin: We can support them in their daily activities, even though they are already doing well. There are factors like changes in evapotranspiration, and rainfall that affect groundwater, which their system may not fully account for. Our solution can help them. It didn't rain much while we were there, but on the last day, it rained heavily. They said all the work done in the past two days had to be undone because of the rain. We hope to help avoid such situations in the future and make their life easier.　Question #7 : How was your experience interacting with the local communities there?Dmitry: People were very friendly and the homemade food was amazing. Quentin played volleyball with them as well. I didn't because it was too hot. It was just too hot to function.　Quentin: People were very kind and welcoming. It was nice to exchange with them and not feel like outsiders. We created something more than just a visitor relationship. The person we had talked to before was an expert of the area and respected by the people there. It was good to see him in his natural environment and get his support. 　David: The atmosphere was filled with smiles, and people seemed genuinely happy. They were incredibly kind and welcoming towards the work we were doing. Although there was a bit of a language barrier since they didn't speak English and we didn't speak Bahasa, we managed to communicate effectively using our hands. It was a wonderful experience, I wish we could have stayed a bit longer to enjoy more of the beauty and the fun environment.<img src=/content/images/image_34a7fe6472c3a64ff473a59c91a327e0.png>David and Dmitry getting to do some ground water level measurements.　Question #8: What is the next step?Quentin: We are talking about expanding the project to newer areas and seeing what's possible. It's not decided yet, but the project has huge potential. We are optimistic that we'll do even more and have an even bigger impact.　David: The next step would be to generalize the solution to other areas. There are massive zones this could be used for, both for planning where to create irrigation canals and for long-term analysis and future forecasts. <img src=/content/images/image_fa85e249a52911778b63dd23af3ca12d.png>Quentin and David at a briefing meeting before going to the field.　Recursive is a service provider of AI solutions for building a sustainable future. We provide AI system development and consultation services by combining our knowledge of diverse industries such as environment, energy, medical, pharmaceutical, food, and retail with our advanced technological capabilities and expertise in sustainability business. Our unparalleled professionals lead the way in creating a new society with world-class technology in order to leave a better global environment and society for future generations.　　Please visit our Borealis web page here:
<a href=https://borealis.jp/en/>https://borealis.jp/en/</a>　Our 4 technology platforms are following; <img src=/content/images/image_3d2d7bdae0543e3d6d1fa9cb1d4352b0.png>

In late February and early March of 2024, a team from Recursive embarked on a significant business trip to Indonesia. The primary objective was to enhance their AI model designed to predict future Ground Water Levels (GWL) in the tropical peatlands of West Kalimantan. This initiative aims to prevent forest fires, anticipate floods, and ensure the growth of plants. 

[Borealis Project background report] Indonesia Business Trip : February 27 - March 2, 2024

Today, we're proud to present the Flow Benchmark Tools, designed to complement and strengthen our FindFlow platform. This open-source release marks a significant milestone in our commitment to advancing retrieval-augmented generation (RAG) technology and empowering the global AI community.The Flow Benchmark Tools represent a major leap forward in our ability to evaluate and optimize RAG systems. As an extension of our FindFlow suite of platforms, these tools address a critical need in the AI landscape: providing a standardized, comprehensive method for benchmarking RAG pipelines.Our goals for the Flow Benchmark Tools are ambitious yet focused:<ol type=1><li>To establish a new standard for assessing RAG system performance, particularly in multilingual contexts.</li><li>To empower developers and researchers with the tools they need to push the boundaries of RAG technology.</li><li>To foster transparency and collaboration within the AI community by open-sourcing our benchmarking methodology.</li></ol><h3>Understanding RAG and LLMs: Bridging the Knowledge Gap</h3>Before diving into the specifics of our benchmarking tools, it's crucial to understand the technologies they're designed to evaluate. Large Language Models (LLMs) are the backbone of modern AI systems, leveraging vast neural networks to understand and generate human-like text. These models, measured by the number of parameters they contain, excel at capturing the general patterns of language use.However, LLMs have limitations when it comes to accessing current or highly specific information. This is where Retrieval-Augmented Generation (RAG) comes into play. RAG is a powerful technique that enhances the capabilities of LLMs by incorporating external knowledge sources. It allows AI systems to combine the fluency and reasoning capabilities of LLMs with up-to-date, factual information retrieved from external databases.The importance of RAG cannot be overstated. It enables a wide range of use cases where accuracy and reliability are paramount. In customer support, RAG ensures that AI assistants provide accurate, up-to-date information about products, services, and policies, leading to improved customer satisfaction and reduced workload for human agents. The education sector benefits greatly from RAG, as it allows educational AI tools to access and present the most current knowledge in various fields of study, keeping learners at the forefront of their disciplines. In the realm of industrial and academic research, RAG empowers researchers with AI assistants that can draw upon vast libraries of scientific literature and data, accelerating the pace of discovery and innovation. Content creators also find immense value in RAG systems, which help them by providing accurate, factual information to support their work, enhancing the quality and credibility of their output.By combining the strengths of LLMs with external knowledge retrieval, RAG significantly reduces the risk of AI hallucinations – instances where models generate plausible but incorrect information. This makes RAG-enhanced systems more trustworthy and applicable in critical domains where factual accuracy is essential.<h3>State-of-the-art benchmarking capabilities</h3>The Flow Benchmark Tools offer unparalleled insights into RAG system performance. By addressing the complexities inherent in RAG pipelines—including semantic retrieval, query generation, and LLM-based answer generation—our tools provide a nuanced, comprehensive evaluation framework.While there are numerous LLM benchmarks available in the field, such as LMSYS's Chatbot Arena and Arena-Hard, SEAL Leaderboards, and ChatRAG-Bench, the Flow Benchmark Tools stand out for their unique approach. Unlike most benchmarks that focus on general queries or pre-processed documents, our tools emphasize document-specific information retrieval and end-to-end document processing. This approach more closely mirrors real-world scenarios, making our benchmarks more product and end-user focused.By evaluating the entire pipeline from raw document processing to information retrieval and response generation, the Flow Benchmark Tools provide a more holistic and practical assessment of RAG system performance. This comprehensive approach aligns with our goal of developing RAG technologies that excel not just in controlled environments, but in the complex, document-rich scenarios that businesses and researchers encounter daily.We're especially proud to announce that our initial release focuses on Japanese language performance, setting a new benchmark for non-English RAG systems. This emphasis on multilingual capability reflects our commitment to developing AI technologies that serve a global audience.In conjunction with this release, we're sharing benchmark numbers that compare FindFlow's performance against several leading RAG systems. These results demonstrate the power and versatility of our approach, while also providing valuable data to the wider AI community.<h3>Embracing the open-source ethos</h3>By open-sourcing the Flow Benchmark Tools, we're inviting the global AI community to join us in advancing RAG technology. We believe that collaboration and transparency are key to unlocking the full potential of AI, and we're excited to see how developers, researchers, and companies will leverage and build upon our work.Our release of the Flow Benchmark Tools is just the beginning. We're committed to continually improving and expanding these tools, with plans to incorporate additional languages, extend our benchmarking capabilities, and refine our methodologies based on community feedback.We can't wait to see how the AI community will use the Flow Benchmark Tools to drive innovation, improve RAG systems, and ultimately create more powerful, accurate, and reliable AI applications. We look forward to your feedback and contributions as we work together to shape the future of AI technology.Our Flow Benchmark Tools are now <a href=https://github.com/recursiveai/flow_benchmark_tools>publicly available on Github</a> and <a href=https://pypi.org/project/flow-benchmark-tools/>PyPi</a>.<h3>FindFlow demonstrates state of the art performance</h3>We're excited to share the results of our comprehensive benchmarking efforts, which highlight FindFlow's exceptional performance in two critical areas: Question Answering using RAG (Retrieval-Augmented Generation) and Whole Document Understanding. These benchmarks not only showcase the capabilities of our SearchAI and AnalysisAI pipelines but also demonstrate how FindFlow stands out in the competitive landscape of AI-powered document analysis tools.<h3>Benchmark methodology</h3>Our benchmarking process was designed to be thorough, fair, and reflective of real-world scenarios. We used a dataset of Japanese government documents paired with challenging questions, which we plan to open-source soon to contribute to the wider AI research community. To ensure the most objective and robust evaluation possible, we employed an automated evaluation system that leverages multiple state-of-the-art LLMs, including GPT-4, Claude 3, and Gemini. This multi-model approach allows us to mitigate potential biases and provide a comprehensive assessment of performance. The evaluation system outputs a mean opinion rating with values ranging from 0 (worst) to 10 (perfect).<h3>RAG question answering: SearchAI shines</h3>In the realm of RAG question answering, our FindFlow SearchAI pipeline was put to the test against two popular question answering pipelines: OpenAI Assistant and the default LangChain RAG pipeline. The results speak for themselves:<ul><li>FindFlow SearchAI achieved a mean rating of 8.42</li><li>OpenAI Assistant followed with a rating of 7.81</li><li>LangChain's default RAG pipeline scored 7.01</li></ul>These scores clearly demonstrate FindFlow SearchAI's superior performance, outpacing the nearest competitor by a significant margin of 0.61 points and the third-place solution by 1.41 points.<img src=/content/images/image_5e5b06ca4e88b94f153d3b382c3f9b9b.png><h3>Document analysis: AnalysisAI leads the pack</h3>For whole document understanding, we benchmarked our FindFlow AnalysisAI against OpenAI Assistant and Gemini's long-context LLM (Gemini-1.5-Pro). The results once again highlight FindFlow's capabilities:<ul><li>FindFlow AnalysisAI achieved an impressive mean rating of 8.90</li><li>Gemini-1.5-Pro secured the second position with a rating of 7.22</li><li>OpenAI Assistant followed with a score of 6.29</li></ul>In this category, FindFlow AnalysisAI's performance surpasses the runner-up by 1.68 points and the third-place contender by a substantial 2.61 points.<img src=/content/images/image_3a450bab77fc62571659cd54b677701c.png>As we continue to refine and enhance our technology, we remain dedicated to maintaining this high standard of performance and to providing our users with the most advanced, reliable, and effective document analysis tools available in the market.

Today, we're proud to present the Flow Benchmark Tools, designed to complement and strengthen our FindFlow platform. This open-source release marks a significant milestone in our commitment to advancing retrieval-augmented generation (RAG) technology and empowering the global AI community.

Introducing Flow Benchmark Tools

In the realm of business and operations, optimizing complex systems is a central challenge, particularly in sectors like manufacturing, pharmaceuticals, and power production. These industries often deal with extensive operational pipelines where the performance of one component can have cascading effects on others, creating a network of interdependencies. Traditionally, the optimization of these systems is handled one step at a time, which may not capture potential efficiencies or the broader impact of changes across the system.At Recursive, we develop methods that optimize such systems from end-to-end. This approach increases production efficiencies and reduces greenhouse gas emissions by enhancing the overall system's performance rather than its individual parts. Our approach surpasses traditional optimization methods that struggle with the complexity and scale of these tasks and are unsuitable for achieving the desired outcomes efficiently.<img src=/content/images/image_1e16d1c103f8f212a190e4540bb49970.jpg>Zenith, our deep learning platform, addresses these challenges by leveraging computational optimization techniques that significantly improve upon the solutions that human experts can create. By adopting AI-driven strategies, Zenith is capable of exploring a wider range of potential solutions quickly and with greater precision, thus enabling more effective system-wide optimizations.<h2>Why is Combinatorial Optimization Difficult?</h2>Traditional methods struggle significantly in the realm of combinatorial optimization due to the nature of the problems at hand. Combinatorial optimization involves manipulating discrete parameters, akin to repositioning pieces on a chessboard to achieve the best strategic setup. Each piece can be placed on specific squares, and each potential configuration could vastly differ from another, impacting the outcome significantly.This contrasts sharply with traditional optimization tasks, such as determining the optimal speed for a car to maximize fuel efficiency. In such scenarios, even minor adjustments to speed result in small, predictable changes in fuel consumption, allowing for a smooth and gradual optimization process. This is visually represented by a smooth landscape where gradual adjustments lead towards an optimal point (as shown in the image below).<img src=/content/images/image_0c7a28b5d15f0d5a2fffb043b77e7e80.png>In contrast, combinatorial optimization does not allow for such minor tweaks. Since the solutions involve discrete choices (like switches being either on or off), there is no way to 'nudge' the solution slightly in one direction and measure the effect. This absence of a gradient to follow makes the problem non-continuous and dramatically increases the complexity of finding the optimal solution. Consequently, most approaches resort to trial and error or rely on simple heuristic methods, which are computationally intensive and often fail to locate the best solutions. This methodological gap underscores the need for innovative approaches like those implemented by Zenith, which rethink how these challenging problems are tackled.<h2>Examples Where Combinatorial Optimization Can Help</h2>The potential of combinatorial optimization to revolutionize diverse sectors is immense, with each application bringing specific benefits that translate into direct business advantages.<h3>Delivery Route Optimization and Logistics</h3>In the logistics industry, optimizing delivery routes is crucial for efficiency. Currently, routes are often planned based on simple heuristics that consider distance but might ignore other variables like traffic patterns or delivery time windows. This can lead to suboptimal paths that waste time and fuel. By applying combinatorial optimization, delivery routes can be strategically designed to account for multiple factors simultaneously, such as shortest paths, least congested routes, and specific time slots for deliveries. This optimization not only reduces fuel consumption and delivery times but also enhances customer satisfaction through more reliable delivery schedules. A logistics company that does not implement best-in class route optimization not only incurs wasted costs, it risks losing market position to competitors that will outperform them using these technologies.<h3>Drug Formulation Optimization in Pharmaceuticals</h3>The process of drug formulation involves finding the optimal combination and proportion of various ingredients to achieve a desired therapeutic effect while minimizing side effects. Traditionally, this process is performed by trial and error, consuming considerable time and resources. Combinatorial optimization transforms the approach by treating the formulation as a complex puzzle where each combination of ingredients and their quantities can be precisely calculated to optimize efficacy. This method can significantly speed up the development process, reduce costs, and bring more effective drugs to market faster, providing a competitive edge to pharmaceutical companies.<h3>Industrial Machinery Optimization</h3>In the manufacturing sector and related fields,, the arrangement of machinery and the settings of complex systems like HVAC are critical for operational efficiency. Traditionally, the layout of factory floors and the settings of HVAC systems are often determined based on static criteria that do not account for dynamic operational conditions. Combinatorial optimization enables a more dynamic approach by considering various operational scenarios to determine the most efficient layouts and settings. For example, optimizing the placement of machinery can minimize the time materials are in transit between processes, reducing cycle times and increasing productivity. Similarly, optimizing HVAC settings in real-time based on factors such as occupancy and external weather conditions can drastically reduce energy consumption. The result is a significant reduction in operational costs and an enhancement in production efficiency, contributing directly to the bottom line.Businesses that are leveraging combinatorial optimization are achieving a level of operational efficiency and product effectiveness that is not attainable with traditional methods. This optimization not only brings cost savings but also fosters innovation, setting the stage for next-generation infrastructure and operations.<h2>How Zenith Addresses Combinatorial Optimization Challenges with AI</h2>Zenith's approach to combinatorial optimization incorporates advanced techniques from machine learning and graph theory to tackle the complex nature of these problems efficiently. The following is a detailed breakdown of the processes involved:<h3>Graph Representation</h3>Zenith models the elements of the problem as nodes in a graph, with the relationships between these elements represented as edges. In drug formulation, each ingredient would be a node and the chemical interactions between different ingredients would be the edges. This graph-based approach allows for a structured analysis of the problem, providing a clear framework to assess how changes in one element affect others.<img src=/content/images/image_7b558609b5dab79b402aa8a6c694b6bf.png><h3>Embeddings for Smoothing</h3>Due to the discrete nature of combinatorial optimization problems, traditional gradient-based optimization methods are not applicable. Zenith utilizes embeddings to map these discrete elements into a continuous, high-dimensional space. This mapping process transforms the optimization landscape into a form that is more amenable to computational analysis and manipulation. The embeddings help to identify potential directions for exploration in the solution space, creating a way to estimate the impact of altering different elements even though the problem itself does not allow for small, continuous adjustments.<img src=/content/images/image_a4e20480963795c48b3deee7006866cb.png><h3>Discrete Optimizers</h3>Zenith employs specialized optimization algorithms designed to handle the discrete and structured nature of the problems. These include evolutionary algorithms and graph neural network optimizers. Evolutionary algorithms are particularly effective as they generate a wide range of potential solutions and evolve these solutions over time based on their performance, mimicking natural selection. Graph neural network optimizers leverage the graph structure of the problem, enabling the optimizer to consider the complex interactions and dependencies between elements to suggest optimal configurations.This methodological framework ensures that Zenith can systematically explore and evaluate a vast array of possible solutions, significantly increasing the probability of identifying the most effective configurations. This process not only streamlines the search for optimal solutions but also reduces the computational load compared to traditional methods, which often rely on exhaustive search or simplistic heuristics.Zenith’s technology strength goes beyond traditional methods by combining discrete optimization algorithms with deep learning features, harvesting the best of two worlds. Discrete optimization algorithms by themselves are often limited to perform optimization of problems that can be described in explicit mathematical detail. Deep learning methods on the other hand find data-driven solutions, which means that the problem description is inferred from examples. Such data-driven solutions require a lot of examples and deep learning method can often only draw limited benefits from explicit problem formulations, even if they are available. The patterns developed in Zenith allow a combination of both approaches, resulting in a system that can perform optimization for extremely complex problems, taking full advantage of available data and available expert knowledge, providing interpretable solutions following controllable parameters.<img src=/content/images/image_e48ce0df3c07e24db4889d647353cfa7.png><h2>Use Cases for Zenith</h2>Zenith’s use of these advanced technologies aims to transform physical infrastructure into highly optimized systems. We can significantly boost productivity in industries such as manufacturing, energy, and transportation, by increasing the efficiency of layouts and operational sequences. A more efficiently designed factory floor layout, optimized for minimal movement of materials, can reduce production times and lower the energy required for manufacturing operations.By optimizing energy use in large systems, such as HVAC systems in commercial buildings or routing in logistics, we can achieve substantial reductions in carbon emissions. Optimized routes mean fewer miles driven, less fuel consumed, and reduced emissions. Similarly, smartly regulated energy systems in buildings can maintain comfort while minimizing energy waste, contributing to a significant decrease in greenhouse gas emissions.In the pharmaceutical and healthcare sectors, Zenith's optimization technology brings best-in-class solutions to a company’s drug development processes and patient care. Advanced optimization techniques for drug formulation can reduce overall development time and associated costs, and the precise optimization of compound combinations and dosages can lead to more effective drugs with reduced side effects, directly improving patient outcomes.Furthermore, in healthcare management, applying combinatorial optimization to scheduling, appointment systems, and resource distribution can enhance operational efficiency. By optimizing these aspects, healthcare facilities can manage patient flow more effectively, utilize staff more efficiently, and minimize the unnecessary use of resources. These changes can lead to reduced operational costs and improved patient care, providing tangible benefits to healthcare systems.<h2>Future Vision for Zenith</h2>We see a promising potential in the integration of quantum computing within our optimization processes. Quantum computing holds the promise of exponentially faster computations compared to classical computers, particularly suited for problems where potential solutions grow combinatorially. Quantum algorithms could dramatically accelerate the identification of optimal configurations in logistics or complex manufacturing processes, where traditional methods falter due to computational limits.Zenith’s current trajectory includes cutting-edge developments across several domains. We are expanding our toolkit to include large transformer models and state-of-the-art reinforcement learning techniques, which offer significant improvements in handling complex optimization tasks. These AI models can analyze vast amounts of data and learn optimal strategies across a range of scenarios, providing solutions that are both innovative and effective.

An introduction to Zenith, our technological platform to enable increased productivity and accelerated innovation

Introducing Zenith: A Deep Learning Platform for Optimization

Authors: Oktay Kurtulus (Head of Corporate), Rie Le Maître (Corporate Associate)Our recent company retreat was more than just a day away from the office. Taking a moment to step back, reflect, and bond with your team is not only beneficial but also essential to further accelerate our business. That is precisely what we aimed for in our recent one-day company retreat, on Friday, March 15, 2024 and oh, what a day it was! If you are interested in Recursive, please read until the end!<h3>Setting the Stage with Keynote Speeches</h3>Our day kicked off with keynote speeches and objective key results presentations by our leadership team which aimed to provide clarity on our company's overarching vision and objectives for the fiscal year. We used this opportunity to recap our success and failure stories in the previous fiscal year and shared our learnings with the whole team. <img src=/content/images/image_8ff2c00567065373d4550b31faace872.jpg>Our CEO, Tiago, is presenting company vision and strategy for the new fiscal year.<img src=/content/images/image_93e3b607268e1dad1711280835b36064.jpg>Our COO, Toshi, is dropping exciting news regarding company goals.The presentations served two purposes: One, they allowed us to realign our individual goals with the broader organizational mission, fostering a sense of purpose and unity, and two, they helped our new members to get a good grip on our company vision and priorities. The session ended with a lively Q&A session in which many members asked various questions, fostering an insightful, engaging, and an interactive dialogue between the leadership team and members.<img src=/content/images/image_7574b51cb4207141bf75875d9f62410e.jpg>Our members asked many questions to the leadership team following the OKR presentations.<h3>Lunch and Group Photo Session</h3>Before munching on some nice lunch following the presentations, our members took some time to take some memorable group photos in a great setting. As a fast-growing, hybrid-work style company with members spanning Japan and overseas, it was certainly a moment to remember to cherish and capture everyone together.<img src=/content/images/image_48db3e647fe64b0103cd7786ce337265.jpg>Recursive core members, doing their best to make Recursive logo with their hands It is also worth mentioning that for this year's company retreat, we had the exciting opportunity to immerse ourselves in "Link Forest," the brand new training facilities offered by KDDI Learning Inc. KDDI Learning, a distinguished subsidiary of our esteemed client, KDDI, opened their doors for our company retreat and provided us an experience that is nothing short of spectacular, blending state-of-the-art amenities with an innovative learning environment.<img src=/content/images/image_00d4a2e61598af3500df56aa84cd958a.png>Link Forest Training Facilities in Tama, Tokyo. Source: <a href=https://link-forest.jp/>https://link-forest.jp/</a><h3>Icebreaker Workshops: Bonding Through Games</h3>After lunch, we moved on to icebreaker workshops. The aim of these activities was to connect with other team members through games. It was a moment to share the laughter, the mistakes, and the unstoppable sense of unity.<img src=/content/images/image_6bb37ab331904fbc39478cb170a76597.jpg>Project Management team’s out of the box solution to “Tall Tower Challenge.” <img src=/content/images/image_5429ea4d16f08339ccb67afb31e3ea61.jpg>Engineer team’s seems to master the game… do they ?<h3>Workshops Galore</h3>After the icebreaker, we did workshops which were focused on team building and goal setting. These weren't your run-of-the-mill sessions; each workshop was designed to be as engaging as well as educational. While acknowledging our successes is vital, what is equally as important is recognizing areas where we can grow. Through a Strength-Weakness-Opportunities-Threats analysis, we identified key areas for improvement and brainstormed strategies to overcome challenges collectively.<img src=/content/images/image_78ac959265d0800bf2963b471b1fc781.jpg><img src=/content/images/image_fe935cb84ee5dbc7b756ff4c53805d5e.jpg>Our workshops for this retreat mark the initial step towards a comprehensive understanding of our strategic landscape, emphasizing the importance of applying these insights in real-world scenarios. Adhering to the 70-20-10 rule for learning and development, we recognize that the bulk of our valuable knowledge will be acquired through hands-on experiences and collaborative efforts, beyond the confines of such training.<h3>Finishing the retreat with a wholesome Award Ceremony</h3>We also took the time to celebrate our peers with an award ceremony, recognizing their embodiment of our Company Values: Trust & Cooperation, Leadership, Impact, and Challenger Mentality. Among them, one of our youngest members, Shuying, received the Excellence Award for embodying all of our 4 values since joining as a new graduate. From heartfelt speeches to peer recognition, we celebrated our collective achievements. Cheers to our incredible team!<img src=/content/images/image_4ecf081c5ff5c1bee1985c9af7c92351.jpg>For Challenger Mentality, we got 3 recipients! Left to right: David (Machine Learning Engineer), Giuliano (Software Engineer), our CEO Tiago, and Shuying (Business Development).<img src=/content/images/image_9f6c5e91d8983c2884b96e0fb37c4a82.jpg>Our CEO, Tiago (left), awarding Quentin (Project Manager) for both Impact and Trust Cooperation.<h3>From Izakaya to Karaoke</h3>After a day of intense concentration, the evening was our time to let loose. Our first stop? An izakaya in Shinjuku, where laughter and stories flowed. But the fun didn't stop there. Next up was karaoke, a universal equalizer where we cheered each other on regardless of vocal talent, proving that a team that sings together, stays together. And yes, we did discover some hidden talents that night!<img src=/content/images/image_c2c645e523f4fbaf86560ec0c77a2694.png>Our team is nailing the classic Japanese song, Zankoku na Tenshi no Tēze by Yoko Takahashi.<h3>The Voice of Our Team</h3>As the day came to a close, we collected feedback from our team. Many highlighted the retreat as a good moment for team cohesion. "It was a lovely event and I think especially for people that just joined like me it was a great team building event. I got the chance to talk in person to many collaborators that otherwise I would have only met virtually" shared one team member. Another remarked, "The location was a nice change of pace, the award ceremony was really wholesome, and the after-party + karaoke were a blast!"<img src=/content/images/image_c4a3980016757f3a557d76efdd18bf16.jpg>“Take my picture as if I am not aware.” <h3>What’s Next?</h3>Our one-day company retreat was more than just a series of activities; it was a kick off for the journey that awaits our team for this year. A journey that not only reminds us the excitement of our goals, but also the undeniable strength that lies in our diversity that will help us overcome the myriad of challenges that we will face along the way. Here's to Recursive and to many more success!<img src=/content/images/image_fdd9bfc5504a544737d5af0f152b0ca3.jpg>Recursive team, more diverse than ever.

Recursive had a company retreat in March 2024. This blog provides a report of the event

Event Report: Recursive Holds Company Retreat to Accelerate Value Creation

Recursive is a service provider that offers AI solutions for building a sustainable future. As a Japan-based startup, it develops its own AI products. In order to always be able to propose optimal solutions to our clients' increasingly complex business challenges, Recursive recognizes and promotes the importance of connecting with researchers and students within the industry so that we can continue to stay ahead of the latest technologies and trends in AI, particularly in the field of natural language processing.Against that background, we had the pleasure of welcoming a group of students from Chalmers University of Technology in Gothenburg, Sweden, to our office in Tokyo!The students, part of the WASP (Wallenberg AI, Autonomous Systems and Software Programming) research initiative, visited Recursive’s office on March 13, 2024. Their focus on Machine Learning (ML), Artificial Intelligence (AI), and autonomous systems aligned perfectly with Recursive's core areas of expertise.<h3>Exploring Shared Expertise and Knowledge</h3>The day was a moment to exchange about our expertise and knowledge as we both presented our projects and engaged in lively discussions. The presentations made by Chalmers students, Adam and Lena, tackled pressing issues in machine learning, using complex methods to boost model effectiveness in practical settings. Adam Breitholtz's talk on transferability was particularly pertinent, addressing how it can overcome data scarcity and high data acquisition costs. Lena Stempfle's presentation delved into the intricacies of healthcare data, showcasing how machine learning can significantly enhance decision-making in healthcare, reflecting a profound grasp of the field's opportunities and obstacles. <h3>Looking Forward: Building Connections</h3>The visit wasn't just about research presentations. We fostered a casual and interactive environment where students and our team could connect. This allowed for a deeper understanding of Recursive’s work culture and the exciting tech landscape in Japan.<img src=/content/images/image_8746ee32622d17a2abf3c799c4cd7146.jpg>After the presentations, discussing about our workOne of our guests provided a positive feedback on how Recursive provided a welcoming atmosphere for this exchange event:Excerpt comment from one of our participants:“The mingling session after presentations allowed us to get to know the Recursive and its employees better, learn about the startup scene in Japan and discuss about the sustainably perception and efforts from Japanese society, government and companies. This shows the shared interests between Recursive AI and WASP and could be the starting point for further exchanges.”It was also extremely encouraging to hear that the students thought Recursive has a clear vision and some of the key projects that were briefly presented seemed interesting and environmentally relevant. <img src=/content/images/image_925af63934a74580394e21f457ba4c59.jpg>Group picture with our team and Chalmers University students These interactions solidify our belief that collaboration between companies and academia is key to pushing the boundaries of what's possible in the field of artificial intelligence. This visit served as a springboard for future collaboration. We at Recursive look forward to working with researchers and academia to co-create new innovations.

Chalmers students visit Recursive to discuss AI and Machine Learning. Both sides presented projects, highlighting the importance of industry-academia partnerships for AI advancement.

Event Report: Recursive holds exchange session with Chalmers University of Technology, aiming for industry-academia co-creation

In this article, we provide a description of some of the key responsibilities of our MLEs, and a brief overview of some of the problems that our Machine Learning Engineers solve on a daily basis.<h1>The key responsibilities of a Machine Learning Engineer</h1>In most projects, some of the key responsibilities of our MLEs are:<ul><li>Conducting comprehensive data collection, often involving literature and dataset reviews to assess dataset availability and data collection methodologies.</li><li>Employing data visualization and analysis techniques to extract meaningful insights from the collected data.</li><li>Engaging in data engineering tasks, which often requires deepening your understanding of the problem, and analyzing the data in different lights.</li><li>Developing machine learning or deep learning models, a process that typically encompasses literature reviews, the formulation of modeling proposals, technical discussions with the team, model training, inference runs, and result evaluations.</li><li>Collaborating closely with our project managers to gain a comprehensive understanding of the client's needs and devising a strategy for a successful project delivery.</li><li>Thoroughly documenting your work and sharing your findings with the team.</li></ul>While the list provided is extensive, it does not encompass all the aspects of being a Machine Learning Engineer at Recursive. The role is dynamic and multifaceted, with each project having its distinct requirements and challenges.<h1>What Machine Learning Engineers do at Recursive</h1>Recursive collaborates directly with corporations who present us with challenges that can be addressed through data science, machine learning, deep learning, software engineering, or a combination of these fields. In addition to our client projects, we actively maintain and enhance our own products, such as FindFlow (<a href=https://www.findflow.ai/>https://www.findflow.ai/</a>) and Borealis (<a href=https://borealis.jp/>https://borealis.jp/</a>).At Recursive, Machine Learning Engineers are integral to every data-related aspect of solving our clients' problems, encompassing data collection, data engineering, analysis, AI modeling, and model deployment.Our MLEs have engaged in diverse projects, including developing product recommendation systems, forecasting from time series data, generating images and videos using generative AI, and tackling natural language processing challenges.<h2>Groundwater level prediction</h2><h3>Problem description</h3>We have joined forces with IHI and Sumitomo Forestry to leverage machine learning for the forecasting of groundwater levels in a forest in West Kalimantan. The prediction of groundwater level holds significant importance as it informs the formulation of fire prevention strategies. This is especially pertinent in the Indonesian region in question, where the forests contain abundant peat deposits deep underground, which are rich in carbon content and are highly flammable, making fire management in these peatlands an arduous task, and emphasizing the critical role of fire prevention and groundwater level management in that area.Our client currently manages groundwater level through the irrigation and drainage of specific zones, primarily relying on sensor-generated groundwater level measurements collected throughout the forest. However, there is a distinct advantage in transitioning to a predictive groundwater level management approach, and in expanding the groundwater level measurements and predictions to cover the whole terrain of the forest, instead of being limited to specific groundwater level sensor locations. The motivation to achieve that was the driving force behind our collaboration with IHI and Sumitomo Forestry. In particular, our MLEs had the goal of delivering a method to provide groundwater level predictions with high spatial and temporal resolution up to 7 days in the future for arbitrary locations within the region of interest.In the scope of this project, our clients have supplied a diverse dataset specific to the West Kalimantan region, spanning several years. This dataset encompasses various information, including groundwater level measurements, precipitation records at select locations within the region of interest, manually recorded elevation data, the geospatial distribution of canals used for irrigation and water table control, as well as the water levels within these canals.<h3>The traditional solution</h3>The traditional approach to solving this problem would be to define a physical model of the region, which involves a detailed description of physical properties of the region at a high spatial and temporal resolution (e.g. topography, peat depth, weather conditions, hydraulic coefficients), as well as a precise definition of the physical equations that govern the subsurface water flow in a simulator. Such approach, when successful, tends to have a high predictive accuracy. However, the main challenges with defining a physical model are:<ol type=1><li>Data collection for physical modeling is resource intensive, but it is crucial. If the physical properties are not precisely described at high resolutions, the model accuracy cannot be guaranteed.</li><li>A physical model is specific to a single region, and it cannot be generalized to other regions.</li></ol>Given these challenges, the cost of data collection, and our client’s expectation to quickly apply one solution to different regions in the future without a lot of additional effort, we have proposed to tackle this challenge in a more data-driven manner.<h3>Our solution and the work done by our MLEs</h3>Our MLEs have worked with leveraging the data collected by IHI and Sumitomo Forestry with machine learning methods.The first steps of the project involved careful investigation and analysis on the provided data. By collaborating with the client, our MLEs built a deep understanding of the data and the problem, which was crucial for them to proceed with:<ul><li>Developing methods to clean and process different types of data (e.g. determining the best method to deal with missing values and outliers).</li><li>Developing methods to combine data collected at different frequencies.</li></ul>By building on these steps, they then successfully applied different machine learning models to make predictions on the sensor locations.However, due to the sparse nature of the data, both temporally and spatially, and the presence of noisy measurements, developing data-only methods, by either adapting our machine learning methods or developing deep learning models, to extend the groundwater level predictions to the whole region of interest would not be the best approach.Instead of relying solely on data, our MLEs developed a unique fusion of machine learning and physics-based modeling. By including physics-based priors to a machine learning model, our MLEs managed to overcome the challenge of working with a small dataset, and they empowered our client to forecast groundwater levels for the entire specified area, up to seven days in advance, as depicted in the image below.<img src=/content/images/image_a859d98eceb60af597b4e5db9dba8fed.png><h3>Why our solution is better than existing solutions</h3>The advantages of combining physical priors and machine learning compared to data-only approaches are:<ul><li>Physical priors enhance data-based predictions, specially when the data is noisy.</li><li>Physical priors are specially helpful to guide the predictions where there are no sensors. They can help our model make reasonable guesses.</li><li>The addition of physical priors encourage the model’s predictions to be in accordance to the laws of physics.</li></ul>The advantages of our fusion approach compared to the traditional physical modeling approach are:<ul><li>It is not required to have a detailed and high-resolution specification of the physical characteristics of the region of interest to apply our model.</li><li>The fusion approach developed by our MLEs can be applied to other regions without additional modeling.</li></ul><h2>Borealis</h2><h3>Problem description</h3>To make informed decisions, having access to historical weather data at a high spatial resolution is crucial. For instance, when deciding the optimal locations for solar panel installation, it's essential to consider factors like the amount of solar radiation received in a specific area, often measured within a few square kilometers for industrial purposes. Additionally, understanding weather-related variables is critical for identifying regions susceptible to certain disasters, such as wildfires, and taking preventive measures.Today, we are fortunate to have access to vast amounts of data. The Japan Meteorological Agency (JMA), for instance, has collected decades' worth of weather data, including information on solar radiation, precipitation, temperature, and more, from weather stations across Japan and through satellites and other sensors. This data allows us to trace the historical weather conditions at these sensor locations. However, the challenge we at Recursive aimed to address with Borealis was extending this historical data to cover the entire Japanese territory at a high spatial resolution.<h3>Our solution and the work done by our MLEs</h3>Borealis (<a href=https://borealis.jp/>https://borealis.jp/</a>) is our exclusive AI-powered product—an advanced dashboard empowering users to visualize various weather parameters (e.g., solar radiation, rainfall, temperature) across Japan in high-resolution maps. These maps are constructed using 20 years' worth of data gathered from weather stations distributed across the country. A concise overview of Borealis is provided in the diagram below.<img src=/content/images/image_b8d8148771b89514ffbcb993dade271a.png>Our AI model harnesses the combined capabilities of satellite, weather, and topographical data to extrapolate sparse weather measurements in Japan into high-detail weather maps with a remarkable 100-meter resolution.To accomplish that, our Machine Learning Engineers (MLEs) undertook the following tasks:<ul><li>Conducted comprehensive data collection from various sources, involving extensive research on potential datasets and, when necessary, the development of code for data retrieval.</li><li>Processed and analyzed the acquired data, which included cleaning and handling missing values.</li><li>Devised techniques to align data obtained at different times and locations effectively.</li><li>Researched and developed a deep learning approach to generate a high-resolution mesh from the sparse data points, covering the entire Japanese territory.</li><li>Together with our Software Engineers, enhanced the data and model pipeline, ensuring it could operate efficiently in production via API access.</li></ul>In the ongoing development of this product, our Machine Learning Engineers continue to collaborate closely with our Software Engineers. Together, they address various challenges, including data collection from diverse sources, data preprocessing, optimization of model inference speed to enhance user experience, and the integration of physical priors into the AI models. As the project evolves, new challenges may arise, and our team remains dedicated to overcoming them to further enhance Borealis.<h3>Why our solution is better than existing solutions</h3>Borealis offers several key benefits:<ul><li>Exceptional Map Resolution: Our generated maps boast significantly higher resolutions than those of the majority of our competitors.</li><li>Utilizing Decades of Reliable Data: Borealis leverages extensive datasets accumulated over decades from reputable and trusted institutions.</li><li>Empowering Sustainability Decision Makers: Borealis is dedicated to equipping decision makers with invaluable insights for their sustainability-driven initiatives.</li></ul>

In this article, we provide a description of some of the key responsibilities of our MLEs, and a brief overview of some of the problems that our Machine Learning Engineers solve on a daily basis.

The Machine Learning Engineer (MLE) role at Recursive

At the internal seminar of Scope Co., Ltd., our COO, Katsutoshi Yamada, delivered a seminar, which we will summarize in this digest. The lecture was planned to be divided into two parts, and this time we will cover the first part, which is the basics.<h3>Current situation: Japan is the world's second-largest plastic waste producer</h3>Japan has a high consumption of plastic products, and when comparing the per capita disposal of plastic containers and packaging waste by country, Japan is second only to the United States. When plastic waste is not properly disposed of and flows into the ocean, it not only affects the marine ecosystem but also causes pollution in coastal and marine environments. There is data that suggests it takes 400 years for a PET bottle to decompose in the ocean, and if the amount of ocean plastic waste continues to increase, it is said that by 2050, there will be more marine debris than fish. In Japan, disposable plastic containers and packaging materials provided in supermarkets and restaurants are considered a major cause of plastic waste. Efforts are needed to reduce these plastic wastes.To solve these plastic waste problems, Japan has implemented initiatives such as charging for plastic bags and promoting the use of reusable plastic containers. However, in order to further enhance environmental protection and sustainability, more and more companies around the world are working on this issue, while still maintaining it as a business.<h3>World business examples that combine sustainability and profitability</h3>First of all, the footwear and apparel brand Allbirds, founded in the United States in 2016, uses environmentally friendly materials as much as possible to produce its products. It has already gone public and is valued at over 180 billion yen, and is a typical example of building a revenue pillar while considering environmental impact. There are also fashion brands such as Ecoalf, which originated in Spain, and KAPOK KNOT, a Japanese apparel brand that uses materials derived from nuts, and more and more companies and brands are focusing on sustainability.Recently, there has been a growing interest in the technique called upcycling, which involves reusing useless byproducts and waste to upgrade them into more valuable new materials or products. There are specific measures such as upcycling beer lees into granola."Ethical Spirits" is a Japanese startup that uses upcycling to manufacture spirits from sake lees, for example. It has been talked about as the world's first recycling distillery that specializes in ethical production and consumption, and its packaging is also distributed as a gift because of its high design quality. In addition, the use of vegan leather products is increasing worldwide, in addition to apple and pineapple leather, cactus leather sneakers have also become popular.Furthermore, there are more and more companies with technology based on scientific discoveries obtained through research in specific natural science fields, which is called "deeptech". This concept, which is prevalent in Europe, is attracting attention in Japan as well. Hello Tomorrow, which has a global network of deeptech companies, entrepreneurs, researchers, and others, is promoting deeptech with companies and entrepreneurs around the world.<h3>World consumer trends</h3>In Europe, more than 80% of people are aware of environmental and social negative impact products, and they do not buy them. Beef is particularly recognized as a food that emits a large amount of greenhouse gases and some universities in Europe are not serving beef as a result. Worldwide, there is a growing awareness of avoiding products from companies that harm the environment, and more than 80% of people tend not to choose such products. In particular, there is an increasing interest in sustainable products and services.ESG (Environmental, Social, and Governance) investment is becoming more prevalent in universities and companies. The amount of investment in ESG is increasing every year and is expected to increase further by 2025. To catch up with the trend in Europe, companies in Japan need to focus on ESG. Although it is currently difficult to manage a business based solely on ESG, it is necessary to respond to it because there is also a possibility that money will no longer be collected without ESG.<h3>Government trends in Japan and the world</h3>The Japanese government has announced a policy to achieve carbon neutrality by 2050. Countries and regions aiming for carbon neutrality by 2050 are increasing worldwide, with the UK and the EU setting different numbers at the midpoint. It seems that the world is moving towards sustainability, and companies and consulting firms are moving in unison based on the government's announcement. There are many acquisitions related to sustainability, and major companies are increasingly creating sustainability departments, with more Chief Sustainability Officers being appointed. Experts in these fields are being recognized due to the high demand for sustainability, and companies are aware that it is important to communicate information about sustainability.<h3>The beginning of ESG management</h3>Since around 2020, there has been a surge in ESG (Environmental, Social, and Governance) investment. 30 leading asset management companies jointly established the Net Zero Asset Manager Initiative in December 2020, and in 2021, assets equivalent to 87 trillion dollars have joined the initiative, increasing the amount of assets under management. BlackRock, a leading institutional investor, announced a series of action plans in January 2020 to enable its clients to invest sustainably in the future. ESG investment as a whole has grown at an astonishing rate worldwide, with ESG investment increasing by 15.1% from 2018 to 2020, reaching 35 trillion dollars. It is expected to reach 53 trillion dollars, or about one-third of total assets under management by 2025.The movement in the advertising industry is further igniting this trend. CO2 measurement and reduction are now being demanded in the production and deployment of advertising. The organization Ad Net Zero has been established to lead the advertising industry towards net-zero, and companies such as Assembly have announced plans to launch tools to audit the CO2 emissions of the advertising industry. The Cannes Lions International Festival of Creativity has changed submission requirements this year and recommends that applicants submit information about CO2 emissions and sustainability impacts associated with their works.Although this is the first attempt, it is not included in the judging criteria, but it is important to understand these trends and develop business strategies that incorporate sustainability in the advertising industry.<h3>Voices of seminar participants</h3>We also received the following comments after the seminar.
”I felt the difference in the way of thinking and approach between Japan and the rest of the world. I was surprised to see how much of a business opportunity sustainability is.”
”I was surprised at how many business opportunities there are in sustainability. I was surprised at how many business opportunities sustainability offers. I learned a lot by switching my awareness from global environmental issues to a business mindset.”
”I was not clear on how sustainability activities are connected to business before the lecture, but now I understand better where the money comes from and how it becomes business.”Recursive will continue to promote the compatibility of sustainability and business in society.

COO Katsutoshi Yamada spoke at an in-house seminar for Scope Inc. This blog provides a digest of the seminar.

“Future-oriented Sustainability x Business Perspectives" Global Trends You Need to Know to Balance Sustainability and Profitability

From May 28th to June 2nd, 2023, I went on a business trip to three cities in the Middle East for the first time in my life: Saudi Arabia, Abu Dhabi, and Dubai.In conclusion, I returned home in a very excited state. I strongly believe that we should expand our business in the Middle East. I would like to summarize my experiences for other Japanese startups to consider. Below are the three reasons that I found particularly attractive.<h3>1. An overwhelmingly growing market</h3>The Middle East has set an ambitious goal to double its current economy size within 10 years. The engine for this growth is non-oil business areas, which account for 73% of the entire economy. There is an acceleration of investments in various fields, and new business opportunities are expanding.<h3>2. High interest in Japanese companies</h3>I felt that both the government and private sectors in the Middle East have a high interest in Japanese companies. There is a need to quickly progress in business. Moreover, as the presence of Japanese companies is not yet very high, there are opportunities for new entrants to stand out in the market. Especially in the field of AI, there is still relatively low competition, which can be described as a blue ocean state.<h3>3. Well-established support system</h3>The support from Japanese government agencies and JETRO is also well-established. They strongly support entering companies by connecting them with companies and government agencies, introducing them to events, and providing various information.Based on these elements, we can say that the Middle East market has great appeal for Japanese startups. For companies considering entering the Middle East in the future, I recommend using the support measures provided by the Ministry of Economy, Trade and Industry and JETRO, as well as researching the startup programs being deployed by the UAE government. This will help gain a deeper understanding of the business potential in the Middle East market.<h3>Additional inspection details</h3>I will also briefly introduce my experiences in each city. First, I visited Saudi Arabia. The first thing I noticed when I landed at the airport was that I had come in shorts. I immediately felt anxious about the cultural difference that one should not show their skin. There was a big gap between westerners wearing suits and my attire. I was also surprised by the abnormal heat and the feeling of sand in Saudi Arabia.I stayed at a Holiday Inn, which was more comfortable than a famous hotel in San Francisco. The quality of the hotel bathroom was also higher than expected, and the breakfast was impressive. I realized that there is no culture of exchanging business cards in Saudi Arabia, and I thought that QR codes should be used instead of business cards, not only for us but also for Japan.On the second day, we presented Borealis, which we developed, to the Saudi Arabian Energy Agency (photo) and received a lot of interest. I will write more about this later as it progresses.<img src=/content/images/image_3b90f505689ccceaed9445db56b93a42.jpg>On the same day, we also visited an exhibition of "THE LINE," one of the NEOM projects promoted by the Saudi Arabian Crown Prince. The scale of the idea was so big that it was not easy to express my thoughts about it.<img src=/content/images/image_98c3aff4180771450c33279489d72ff1.jpg>Next, I visited Abu Dhabi. The atmosphere, beautiful buildings, and townscape reminded me of Singapore. Compared to Saudi Arabia, I felt that the heat was somewhat calmer. I visited Hub71, which is jointly operated by WeWork and the Abu Dhabi government, and heard various stories from a Japanese startup based there. I directly felt the fun of working with diverse sectors and people.<img src=/content/images/image_05955786bab564e33a8d2dc07d058390.jpg>On the second day in Abu Dhabi, I participated in an event called "MAKE IT IN THE EMIRATES" held at the Energy Center, where I listened to various talks and presentations by startups from around the world. Furthermore, on the second day, I had a valuable opportunity to exchange opinions with government officials, the Energy Agency, and local large corporate executives in a roundtable format, which led to the next step.<img src=/content/images/image_1f61990158ffcd226871d31b054b0872.jpg><img src=/content/images/image_7a794b78bce8ec2c66c2619372949ec2.jpg>Lastly, I visited Dubai. I felt that the atmosphere was somewhat similar to Las Vegas. I met with the Vice President of Sobha Limited, a real estate development company, and was surprised by the scale and quality of the town they are developing. The completion of those projects is progressing as planned, and they are selling like hotcakes.<img src=/content/images/image_5ad5011259d800b7bf4b119da8c9d3bd.jpg><h3>Conclusion</h3>I am very excited to have experienced the attractive market of the Middle East. As I have chosen to be a startup, I have the desire to do something that can only be challenged here, and I think it should be done. I am aware that it will not be easy, but I hope to move forward with various help. I would be happy if this could be useful as a reference for Japanese startups.

This is a business trip report for the end of May 2023 to Saudi Arabia, Abu Dhabi, and Dubai. Publishing this report in the hope that it will provide some hints for your business in the Middle East.

Middle East Visit Report / The Potential of AI Business

Hello, this is WoojungThank you very much for the overwhelming feedback I received from many of you after my first post! Here goes a second one on Beyond EXPO 2023 in Macau. Hope you enjoy!Beyond EXPO 2023 in Macau was held from May 10th to 12th, and numerous East and Southeast Asian companies developing cutting-edge technologies in the areas of healthcare and sustainability exhibited. At the event, Tiago Ramalho, CEO of Recursive, moderated the discussion of “How is AI Shaping Southeast Asia's Next Gen?"While the innovative growth of Large Language Models (LLMs) is making headlines in the media on a daily basis, there are many challenges to their implementation to the level that it addresses the social issues. In order to make the vision of "AI for society" happen in the near future, each country and region needs to promote implementation suited to their own circumstances. Southeast Asia lags behind in DX compared to East Asia. However, that is why there is a great potential to innovatively accelerate DX in the region by introducing AI from the beginning, which is why Recursive is focusing on the Southeastern market. The following is a summary of the characteristics of Southeast Asia and the potential of AI in Southeast Asia based on the content of the speakers.<h2>Linguistic characteristics of Southeast Asia and their impact on AI development</h2>The most important characteristic of Southeast Asia in AI development, especially in LLM development, is that all the countries are multilingual. While the majority of the Southeast Asian population has the ability to speak English on a daily basis, each country and ethnic group uses its own language in addition to English.Traditionally, existing tech companies such as Google develop their software and AI in English, which makes it difficult to localize their products for launch in other language countries. However, the bottom line is that this should not be a major problem.In addition to Natural Language Processing and speech recognition technologies, the people in the Southeast Asian countries have a high level of understanding the nuances, intonation, and other linguistic elements that affect communication beyond the meaning of individual words which is an important foundation for the development of LLM.Furthermore, from the perspective of corporate strategy, LLM can be used for customer engagement, etc., and the speed at which the technology can be deployed will be comparable to other regions.<h2>Social Challenges in Introducing AI in Southeast Asia</h2>While the development and implementation of AI in Southeast Asia has great potential for growth as described above, there are also several social challenges that could slow the spread of AI.<h3>1. Lack of Governmental Policies</h3>The market potential of Southeast Asia is maximized when the 10 Southeast Asian countries are combined. However, less than half of the ten Southeast Asian countries have not yet announced their national AI strategies, and the ten countries are hardly moving collectively. Only Singapore, Thailand, Malaysia, and Indonesia, the countries with the fastest growing economies, have announced theirs, and the rest of the region still have some work to catch up. In addition, with the exception of Singapore, some countries have low levels of trust among governments, businesses, and citizens, and many countries lack the infrastructure and data systems necessary to introduce AI, which could raise the hurdle for AI adoption.<h3>2. Distrust Regarding Data Provision</h3>Again, because of the lack of a legal system for data use, companies and the public tend to have a high level of distrust on providing their personal data. As a result, it is difficult to accumulate data necessary for AI development, which is a challenge for AI diffusion.<h3>3. Lack of education on AI</h3>In order for AI to spread, it is necessary for the public to understand what AI is, how to use it, and to some extent about its risks. Since awareness of AI is still lower in Southeast Asia than in other regions, finding engineers and product talent is a challenge. Startups from Southeast Asia often find talent from overseas (mainly China), and the low level of AI literacy in the country as a whole will be a major challenge for future growth.The ability to learn and take an interest in not only technical knowledge but also how AI is being used around you and where your data is being used from an early stage will have a significant impact on the development of AI human resources in Southeast Asia. Institutional support for the reskilling required of workers as technology develops will be necessary.<h2>What is inclusive and fair AI?</h2>Although we have picked up issues in Southeast Asia, there are some things that Japan can learn from Southeast Asia. In Japan, even though there is a lot of noise about AI in the media, many people feel that it is still a distant topic for them, and even when I work as an AI consultant, I hear people say, "I never thought our company could use AI.” Singapore is an example of social implementation of AI.From the example of social implementation of AI in Singapore, we can say that "good AI" has four major characteristics. (1) It is beneficial to society, (2) it is transparent, (3) it is provided equally to all, and (4) it is explainable.Of course, the difficulty of implementing AI in a relatively small population of 5.5 million people is different from that of implementing it in a country with 100 million people. Japan still needs to make many efforts to introduce AI, including social changes, infrastructure development, institutional guarantees and support, etc. However, if we learn from advanced cases such as Singapore and have the right direction, we may be able to achieve a society in which everyone can live happily and AI can be implemented. .P.S.As we have been accumulating AI use cases in Japan since last year, Recursive has been working toward overseas expansion this year, and we have been putting a lot of effort into events, such as speaking opportunities in and out of Japan, exhibiting booths, etc. If you have any good opportunities for us to join, feel free to reach out to us anytime.Note: This article is based on the content of the event.

Beyond EXPO 2023 in Macau was held from May 10th to 12th, and numerous East and Southeast Asian companies developing cutting-edge technologies in the areas of healthcare and sustainability were exhibited. In this blog, we report on a seminar held at this event.

Beyond EXPO 2023 in Macau event report

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.4, the final installment, is a continuation of “How I can use Large Language models in my business?”, with “6. Enabling custom AI systems” and “Conclusion”Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-01/>Part.1</a>Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-02/>Part.2</a>Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-03/>Part.3</a><h2>How I can use Large Language models in my business?</h2>Part.4 is a continuation of Part.2 and Part.3, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.<h3>6. Enabling custom AI systems</h3>Large language models can help make the development of customized AI models more efficient and cost-effective by enabling the extraction of insights from unstructured data and feeding it to scientific-based systems. This can lead to optimized industrial processes, improved scientific simulations, better demand forecasting, and more. By automating some of the data extraction and processing tasks, using generative AI can also help reduce costs associated with manual labor and increase productivity.When it comes to scientific simulations, large language models can be used to extract and crawl data from public and private data repositories that can be fed into simulators to improve accuracy and reduce the computational cost of simulations. This can lead to more accurate predictions and better understanding of complex physical phenomena.By analyzing electronic health records and other patient data, a model could be developed to create personalized recommendations for treatment plans, medication dosages, and more. These recommendations can be tailored to specific healthcare concerns, such as chronic diseases or mental health disorders, and can help improve patient outcomes while reducing healthcare costs.In infrastructure, these models can be used to extract insights from various data sources such as traffic patterns, energy consumption, and environmental impact. This data can then be fed into a model that optimizes the cost and benefit of developing a certain piece of infrastructure, such as a new highway or a wind farm.<h2>Conclusion</h2>In conclusion, large language models have the potential to revolutionize the way businesses operate, from knowledge management and business intelligence to customer service and vertical-specific AI models. By leveraging the power of language, businesses can extract insights from unstructured data and make informed decisions that drive growth and efficiency. With their ability to understand complex patterns and dependencies in language, large language models are a powerful tool for unlocking the potential of big data and advancing AI capabilities. As these models continue to evolve and improve, we can expect to see their impact on businesses grow even more significant in the coming years.

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.4, the final installment, is a continuation of “How I can use Large Language models in my business?”,  with “6. Enabling custom AI systems” and “Conclusion”

The value of Large Language Models for enterprise Part.4

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.3 is a continuation of “How I can use Large Language models in my business?”, with “3. Automation for everyone”, “4. Processing unstructured data” and “5. Customer Support”Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-01/>Part.1</a>Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-01/>Part.2</a><h2>How I can use Large Language models in my business?</h2>Part.3 is a continuation of Part.2, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.<h3>3. Automation for everyone</h3>Large language models have the potential to make some APIs obsolete by allowing people with no knowledge of code to automate common actions and connect different software tools without writing code. APIs, or application programming interfaces, are a set of protocols and tools for building software applications. They enable different software tools to interact with each other, making it easier for developers to create new applications and services.However, APIs can be complex to work with, requiring developers to have a deep understanding of coding languages and syntax. This can create a barrier for people who want to automate common actions or connect different software tools, but don't have the technical skills to do so.Large language models can help to overcome this barrier by providing a more intuitive interface for automating actions and connecting software tools. By using natural language commands, users can instruct the model to perform specific actions, such as sending an email, scheduling a meeting, or updating a database. The model can then take care of the technical details, such as interacting with APIs, so that users don't have to worry about the underlying code.For example, imagine a marketing team wants to automate their social media postings. Instead of writing code to interact with different social media APIs, they could use a large language model to automate the process. They could provide the model with a natural language command, such as "Schedule a tweet for 2pm tomorrow with this image and caption." The model could then take care of the technical details, such as interacting with the Twitter API, to schedule the tweet.By making it easier for non-technical users to automate actions and connect software tools, large language models have the potential to democratize the automation process. This could lead to a more efficient and productive workforce, as more people can take advantage of automation tools without needing advanced technical skills.<h3>4. Knowledge management and business intelligence</h3>Knowledge management and business intelligence are two areas where large language models (LLMs) can have a significant impact on enterprise operations. By using LLMs to analyze and summarize large amounts of data and content, companies can improve decision-making, streamline workflows, and drive business growth.Automatically generated summaries of documents and other content make it easier for employees to quickly access the information they need. For example, a law firm can use a large language model to summarize legal briefs or case law, making it easier for attorneys to quickly find the information they need to support their cases.Search and synthesis systems powered by large language models can be a game-changer for companies when it comes to accelerating internal work. By providing easy and efficient access to a company's internal documents, these systems can help any worker quickly find the information they need to do their job effectively. For example, imagine a marketing professional who needs to create a new campaign for a product. Instead of spending hours manually searching through past campaigns and marketing materials, they could use a search and synthesis system powered by a large language model to quickly find relevant information and even generate new content.One industry where search and synthesis systems have proven especially useful is healthcare. Healthcare providers deal with an enormous amount of data and documentation, from patient charts and medical histories to research studies and clinical trials. By using a search and synthesis system powered by a large language model, healthcare providers can quickly access this information and use it to inform patient care and research efforts. For example, a physician who needs to diagnose a rare disease could use the system to search for similar cases and relevant medical literature, saving valuable time and potentially improving patient outcomes.Another industry where search and synthesis systems have proven useful is industrial manufacturing. Manufacturing companies deal with large volumes of technical documentation, such as schematics, assembly instructions, and safety guidelines. By using a search and synthesis system powered by a large language model, workers can quickly find the information they need to build and maintain products, troubleshoot issues, and ensure safety compliance. For example, a worker who needs to repair a machine could use the system to quickly find the relevant schematics and troubleshooting guides.Finally, the retail sector is another industry that can benefit from search and synthesis systems powered by large language models. Retail companies deal with large volumes of customer feedback, sales data, and product information. By using a search and synthesis system, workers can quickly find the information they need to make informed decisions about product development, sales strategies, and customer service. For example, a customer service representative could use the system to quickly access customer feedback and complaints, enabling them to provide a better customer experience.<h3>5. Customer Support</h3>Large language models can be a valuable tool for companies looking to improve their customer service operations. Chatbots and virtual assistants powered by large language models can provide immediate assistance to customers, reducing the need for human support agents to handle routine inquiries. These chatbots can be trained to recognize common queries and provide relevant answers quickly and efficiently, resulting in higher customer satisfaction rates and lower support costs for the company.For example, a retailer can use a chatbot to help customers track their orders, check product availability, and process returns or exchanges. By training the chatbot to recognize common queries and provide accurate information, the retailer can reduce the workload for human support agents, freeing them up to handle more complex issues.One advantage of large language models is their ability to connect with a company's internal documentation, allowing them to provide up-to-date and accurate information to customer queries. With the correct access scopes, chatbots and virtual assistants can quickly search through a company's internal knowledge base and provide the customer with the information they need. This can significantly reduce the amount of manual work required to respond to customer inquiries, freeing up human support agents to handle more complex issues.In addition to improving customer service, chatbots and virtual assistants can also be used for internal communication and collaboration. For example, a company can use a virtual assistant to schedule meetings, set reminders, and send notifications to team members. By automating these routine tasks, the virtual assistant can help increase productivity and free up employees to focus on more important work.Continue to Part.4

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.3 is a continuation of “How I can use Large Language models in my business?”,  with “3. Automation for everyone”, “4. Processing unstructured data” and “5. Customer Support”

The value of Large Language Models for enterprise Part.3

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.2 includes “How I can use Large Language models in my business?”, “1. Accelerating Knowledge work” and “2. Processing unstructured data” Read <a href=https://recursiveai.co.jp/en/blog/the-value-of-Large-Language-Models-for-enterprise-01/>Part.1</a><h2>How I can use Large Language models in my business?</h2>Now that we have discussed the potential of large language models (LLMs) and how they are likely to become ubiquitous in the business world, you may be wondering how you can actually use these technologies to improve your own operations.To help you get started, we have put together a summary of the various applications of LLMs in the enterprise context. This overview will give you a sense of the types of problems that LLMs can help you solve, and the ways in which they can be integrated into your existing workflows.<h3>1. Accelerating Knowledge work</h3>Large language models can be used to accelerate the typical office worker workflow by being used as a "calculator for writing." This refers to the ability of large language models to quickly generate text based on a prompt provided by the user.For example, imagine a typical office worker needs to write a report on a complex topic. Instead of spending hours researching and writing the report, the worker can use a large language model to generate a first draft. The worker can simply provide the model with a prompt, such as "Write a report on the benefits of using artificial intelligence in business," and the model will generate a complete report that the worker can then edit and refine.Using a large language model as a "calculator for writing" can save office workers a significant amount of time and improve their productivity. Instead of spending hours researching and writing, workers can focus on other tasks that require their attention. In addition, using a large language model can also improve the quality of the writing, as the model can generate text that is well-written and coherent.It's important to note that using a large language model as a "calculator for writing" does not replace the need for human input and editing. While the model can generate a first draft, it's up to the user to edit and refine the text to ensure that it meets their specific needs and requirements.In addition to providing a prompt for the large language model, users can also provide structured data from another program and let the model incorporate the language into the report. This can be especially useful for generating reports that require a combination of structured data and natural language.For example, imagine a sales team needs to generate a report on the performance of their products. They can use a spreadsheet program to generate the data and then provide that data to a large language model. The model can then use that data to generate a report that incorporates the data in a natural language format. The report might include insights on which products are performing well, which are underperforming, and what actions the team can take to improve performance.By using structured data in conjunction with a large language model, users can generate reports that are both accurate and easy to read. The structured data provides the necessary information, while the language model helps to generate the narrative around that data.<h3>2. Processing unstructured data</h3>Unstructured data is information that is not organized in a specific way. For example, a long paragraph of text is unstructured data because it doesn't have any specific format or organization.On the other hand, structured data is information that is organized in a specific way. For example, a table with columns and rows is structured data because it has a specific format and organization.Large language models can be used to "read" unstructured data and extract the relevant information in a structured format. For example, if you have a long document with information about sales, a large language model can be trained to identify the relevant sales data and extract it into a structured format such as a table. This can make it much easier to analyze and work with the data.This is particularly useful in supply chain operations, where there is often a large amount of unstructured data in the form of emails, invoices, and other documents. By analyzing this unstructured data, large language models can help to identify patterns and insights that might not be apparent from traditional data analysis techniques.For example, a large language model could be used to analyze emails and other communication between suppliers and buyers to identify potential bottlenecks or delays in the supply chain. It could also be used to analyze customer feedback and social media data to identify trends and patterns in consumer demand.In addition, large language models can be used to improve the accuracy of demand forecasting models by incorporating additional sources of data. For example, a large language model could be used to analyze news articles and social media data to identify trends and events that might impact consumer demand.Continue to Part.3

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.2 includes “How I can use Large Language models in my business?”, “1. Accelerating Knowledge work” and “2. Processing unstructured data” 

The value of Large Language Models for enterprise Part.2

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.1 includes “introduction”, “What is a Large Language Model?” and “Why looking at Large language models and generative AI now?” <h1>Introduction</h1>In recent years, large language models have become increasingly popular due to their ability to process natural language and generate text that is virtually indistinguishable from that written by humans. While these models were initially developed for use in natural language processing tasks such as language translation and sentiment analysis, their capabilities have since been extended to a wide range of applications.Large language models are a subfield of Generative AI, which is focused on the creation of artificial systems that can generate outputs that are similar to those produced by humans. This includes not just language, but also images, music, and other forms of creative output.Enterprises have only just begun to recognize the potential of large language models to improve their operations and enhance their offerings. These models can have an impact on knowledge work in virtually any industry, from finance and healthcare to marketing and customer service. From accelerating knowledge work to optimizing business processes, large language models have the potential to provide valuable insights and automation that can help businesses operate more efficiently and sustainably.In this blog post, we will explore some of the key applications of large language models for enterprises. We will discuss how these models can be used to accelerate workflows, automate common tasks, optimize supply chain operations, and improve sustainability. We will also provide specific examples of how large language models have been used in these applications to demonstrate their value and potential impact.<h2>What is a Large Language Model?</h2>Large language models are computer programs that have been trained on massive amounts of text data, typically crawled from the internet. They are trained using advanced machine learning techniques that allow them to analyze patterns and relationships in natural language data, and then use that understanding to generate new text or perform other language-related tasks.The process of training a large language model is resource-intensive, often requiring thousands of graphics processing units (GPUs) to train the model on vast amounts of text data for weeks or even months. During this process, the model is exposed to trillions of words, allowing it to build a deep understanding of language patterns and structures.The basic principle behind large language models is that they are trained to predict the next word in a sentence or sequence of text. This simple loss function is used to guide the training process, but it can lead to emergent properties of the model as it learns to model text dependencies over a long range. This means that the model can make accurate predictions about what words should come next, even when those words are several sentences away from the current position.The result is a powerful tool that can generate new text, answer questions, and perform other language-related tasks with a high degree of accuracy. Large language models have been used to improve machine translation, natural language understanding, and even generate creative writing such as poems and stories.<h2>Why looking at Large language models and generative AI now?</h2>The potential of large language models (LLMs) for the enterprise is enormous. With their ability to process and understand language in a way that was previously only possible for humans, LLMs can have an impact on knowledge work in virtually any industry. As the capabilities of generative AI continue to grow, we anticipate that in the coming years, every company will be using these technologies in a way similar to how email or spreadsheets are used today.One of the main reasons for this is that generative AI, including large language models (LLMs), has the potential to revolutionize many aspects of business operations. By automating repetitive tasks, providing new insights and recommendations, and streamlining workflows, LLMs can help companies improve their efficiency, reduce costs, and gain a competitive edge.In addition, the adoption of generative AI is being driven by advances in cloud computing and machine learning technology. As these technologies become more powerful and accessible, they are enabling even small businesses to leverage the power of LLMs to solve complex problems and gain new insights.Moreover, we are seeing a growing number of startups and technology companies developing solutions based on generative AI, which are making it easier than ever for businesses to incorporate these technologies into their operations. This is leading to a proliferation of tools and applications that are specifically designed to help businesses take advantage of the power of LLMs.

Featuring "The Value of Large-Scale Language Models in the Enterprise” which is a four-part series. Part.1 includes “introduction”, “What is a Large Language Model?” and “Why looking at Large language models and generative AI now?” 

The value of Large Language Models for enterprise Part.1

Innovation, put simply, is the act of solving a problem. This is what distinguishes innovation from research, which is the act of figuring out how or why something works. When you're innovating, you're solving a problem using a new tool or methodology that hasn't been used before, with the goal of solving it faster, cheaper, or more efficiently than before.There are many reasons for establishing innovation as an important goal on this increasingly fast path towards sustainability. Firstly, because sustainability is the right thing to do and innovation will allow us to become sustainable more quickly. But in and of itself, stating that something is "the right thing to do" is an intangible, nebulous concept - how do we convince people (and corporations) of this?Thanks to shareholder and customer pressure, organizations are transforming their business models and operations to save energy, minimize pollution, and combat gender, racial and ethnic bias. In the long-term, sustainable practices can reduce costs, increase efficiency, and create a better society for the world at large. As more organizations embrace sustainable innovations, it is becoming increasingly clear that sustainability is no longer just a trend, but rather an essential part of doing business. Key to this change process is innovation.Innovation is about solving concrete, real-word problems, and perhaps contrary to popular belief, innovation does not automatically require preceding research knowledge. You don't have to be an accomplished researcher or scientist to be a successful innovator. For example, with the advent of the steam engine, the innovation work preceded the understanding (or research) of the steam cycle; the cycle of thermodynamics that underlies the steam engine was only discovered much later. People, (who are in this case, innovators) simply realized that if you build a machine with certain parameters, it would work.It is vital to recognize, however, that research can and often does lead to further innovation; once we understood the mechanism of the steam cycle, we were able to create more efficient machines that used the steam cycle. Innovation and research often go hand-in-hand, but there is no requirement of one for the other.But how can innovation (whether it precedes or proceeds research) specifically aid our pursuit towards sustainability? Consider, for instance, lab grown meat. Startups such as <a href=https://upsidefoods.com/>Upside Foods</a> are successfully growing meat directly from cells and plant feed, reducing the amount of pollution and waste generated by meat farming, as well as reducing animal suffering resulting from industrial farming.Innovation is the act of taking an understanding of a topic or concept and applying it to a problem where it can be useful. So, even as technology changes, the definition of innovation doesn't change, though the pace and character of innovation definitely do. A hundred or two hundred years ago, innovation was mostly done by elite members of society because accessing knowledge was a privilege. You needed free time (and therefore, economic capital) to access books and libraries, and often you were limited to localized knowledge of your immediate neighborhood, your country, and what was available in your language. This meant that there were huge disparities in the ability to innovate between different countries and different social and economic classes.Today, with information technologies and the internet, the number of people with access to knowledge has increased exponentially. This has leveled the playing field somewhat and has made innovation more accessible. Although there are still limitations based on language, socioeconomics, and geographic location, these have been significantly lessened; this change in access has meant that smaller communities are able to innovate almost as quickly or sometimes even quicker than areas served by big corporations.Companies and corporations, however, are often guided by principles of short-term profit growth, which can conflict with the goal of sustainability. Corporate sustainability is a principle that tries to align these two goals. Businesses strive to be financially healthy and return a profit to ensure that their business remains solvent over the long-term. At the same time, it is important to ensure that the outcomes of business have a positive impact on society.Companies that lack sustainability tend to do poorly over the long run for many reasons; not merely in terms of optics and public perception but also in terms of legal and financial risks. Unsustainable companies tend to lack innovation and remain stuck in a particular market, making them vulnerable to disruption by innovators (even those companies who are initially operating in a monopoly). When income is guaranteed, at least to a certain degree, people tend not to realize the importance of innovation. Innovation requires a level of long-term thinking from company leadership - and often a certain degree of monetary input. The output of these "innovation investments" pay off both directly as revenue growth, and indirectly - there is a strong preference for employees to want to work for and contribute to an organization that is sustainable, leading to a more engaged workforce.Innovation is essential for improving sustainability and addressing the challenges facing our planet. By developing new technologies, practices, and solutions, we can create a more sustainable future and protect our planet for generations to come. Companies like Recursive are at the forefront of this effort. With projects such as predicting groundwater level in forests, generating high resolution weather maps, and improving healthcare outcomes, Recursive is making a real difference in the fight for sustainability. We can all play a role in supporting and promoting sustainable innovation.

Companies are looking for creative ways to meet their sustainable development goals. AI provides many solutions.

Is Innovation the solution to Business Sustainability?

AI enables the development of new technologies every day, and applying it to sustainability could be revolutionary. Many companies are seeking creative ways to improve their environmental, social, and governance (or ESG) goals, and leveraging AI provides many solutions.Increasing your organization's sustainability impact can also help boost your bottom line by saving money and increasing sales. Consumers are becoming increasingly cognizant of environmental and societal issues, which means that sustainability is vital to the success and longevity of your organization.<h1>How AI Can Help You Meet Your Sustainable Development Goals</h1>There are several ways <a href=https://recursiveai.co.jp/en/book>AI can help your organization</a> reach its ESG goals. AI can accelerate innovation by enabling faster research and development strategies. Researchers can easily access information using natural language processing (known as NLP) and text mining technology. This can drive progress for your company and achieve sustainable development goals.AI has increased productivity for organizations across the globe. They benefit from higher economic outputs that can lift people out of poverty and reduce pollution, raw material use, and even land use. Thus, improving productivity in these industries can have a major impact on improving their overall sustainability.Prevention and mitigation are other critical aspects. As we continue to see more disasters caused by climate change, deforestation, and even ecosystem pollution, it becomes increasingly important to adequately forecast potential problems and mitigate them. Predicting when misfortune will occur can help your organization prepare in advance.AI systems can help accelerate education and improve work conditions for workers at your organization. We know that AI systems cannot set goals and achieve creativity. Human input is still crucial when figuring out what problems must be solved. This allows organizations to direct algorithms to optimize specific areas.<h1>Why You Should Leverage AI in Your Organization</h1>AI can positively impact your ability to reach all your organization's sustainable development goals. A study published in the scientific journal <a href=https://www.nature.com/articles/s41467-019-14108-y>Nature Communications</a> found that AI technologies strongly impact nearly all of the 17 sustainable development goals. Although any new technology has the potential for negative impacts, the positive impacts significantly outweigh any harm they might cause.AI is necessary to deliver sustainable solutions and allow organizations to reach their sustainable development goals while reaping the economic benefits. A <a href=https://www.mckinsey.com/business-functions/quantumblack/our-insights/global-survey-the-state-of-ai-in-2021>McKinsey survey</a> showed that AI implementation offers significant benefits for organizations, including cost savings.<h1>How AI Has Helped Organizations Reach Sustainability Goals</h1>AI can unlock trillions of dollars for organizations across the globe. When AI is implemented and deployed with care, it can help achieve ESG goals, such as:1. Data center coolingOrganizations across the globe utilize AI to save time and money and to help achieve their sustainability goals. Take Google, for example; implementing AI into their data centers allows them to make a few <a href=https://www.deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-by-40>percentage improvements in power efficiency</a>. This quickly translates into millions of dollars in savings each year. Additionally, they can operate their hyperscale data centers more efficiently, allowing for far larger outputs than local workstations.The company achieved this by training the DeepMind team on deep neural networks on data and enabling them to develop a more energy-efficient cooling system. This helped produce significant gains in energy efficiency and reductions in their carbon footprint and operating costs.2. LivestockIn the agricultural industry, livestock farms are largely responsible for emitting a lot of carbon dioxide. Even though there are many efforts to create meat alternatives, it's not yet a reality to mass produce these. Right now, the focus is on reducing carbon emissions by making a few efficiency changes, which could result in more affordable nutrition.The animals in livestock farms require constant personal care from humans, which is a lot of manual labor. Beijing Unitrace Tech developed <a href=https://www.washingtonpost.com/world/asia_pacific/facial-recognition-china-animals-farms-agriculture/2020/08/23/9808c710-d6fb-11ea-b9b2-1ea733b97910_story.html>a facial recognition software</a> that uses AI to monitor each animal's behavior and habits. The technology can also detect illnesses and unusual behaviors using a deep learning system. So far, this has only been developed for cows because pigs are more difficult to detect with facial recognition. Without the need for human intervention, this drastically reduces the time it takes workers to physically monitor their livestock.3. Harmful speech detectionAI technology can monitor language and detect hateful and harmful speech patterns on social media platforms that use NLP. For instance, <a href=https://ai.facebook.com/blog/ai-advances-to-better-detect-hate-speech/>Facebook uses NLP</a> to control harmful speech, including bullying, racism, and violence. Another major issue on Facebook is the spread of fake news, which leads to misinformation that destabilizes democracies.Facebook cannot use humans to moderate content that gets published, which is where NLP comes in. Facebook didn't stop there, though. Its research team also trained agents to sample novel training to prevent hate speech from spreading. Additionally, the system can self-supervise, meaning it can detect information outside the training data.It can feel impossible to meet your sustainable development goals manually. It is often not clear what to measure and how it relates to the issues society cares about. Leveraging AI is a key contributor to reaching your ESG goals. Not only will it save you time, but it will also help reduce your overall expenses.

How AI and Sustainability Go Hand in Hand

The agricultural industry has a sustainability problem. Not only is agriculture responsible for as much as <a href=https://online.maryville.edu/blog/importance-and-future-of-sustainable-agriculture/>30%</a> of current greenhouse gas emissions, but today's farming practices are also actively harming the environment farmers need to keep healthy if they want to continue growing crops long term. Massive corporate farms that take up huge expanses of land threaten biodiversity. At the same time, the regular use of chemical fertilizers, herbicides, and pesticides all contribute to the soil's deterioration. That's in terms of both its health and a more literal sense — modern farming practices significantly speed up the soil erosion rate.These practices also leave farmers even more vulnerable to the growing effects of climate change. Extreme weather events, such as floods, droughts, and unseasonable freezes, will only increase in frequency. With few protections in place, these events will continue to wreak havoc on the agricultural industry. Luckily, there is a sustainable path forward for farming, and it's in the form of localized mini-farms.The construction of mini-farms would be especially beneficial in countries such as Japan. With very little flat, arable land available to grow vegetables and a cooler climate that necessitates greenhouse gas use in the winter, farming is a costly business <a href=https://sfp.ucanr.edu/pubs/SFNews/95Nov-Dec/japanese_ag_obs/>financially and environmentally</a>. On top of this, Japan is highly vulnerable to extreme climate events, including typhoons, heavy rains, and extreme summer heat. So, a future of vertical, climate-controlled micro-farms should be a welcome one for the country.<h1>Why Smaller Is Better</h1><a href=https://medium.com/age-of-awareness/how-small-farms-can-sustainably-feed-the-future-45baf2ef6b4e>Small farms</a> are more sustainable and yield <a href=https://pdfs.semanticscholar.org/2b3c/36a7398c6bc0ed7ed351db6b67a28003d265.pdf>higher amounts of produce</a> relative to their areas than their large counterparts because of how small farms typically operate. Smaller farms are more efficient and less harmful to biodiversity as well. However, reducing the size of farms alone wouldn't be enough to solve the sustainability issues facing farming. Instead, the industry needs to think even smaller.Mini-farms are contained, vertically built facilities that yield small amounts of crops monthly or even weekly. The closed, controlled environment of these farms eliminates the need for chemical pesticides and herbicides and allows for the reuse of water — an important defense against droughts. The purpose of these farms is to serve only the local population. They also cut farming's contributions to greenhouse gases significantly.Of course, you might ask, if micro-farming is such a great solution, why hasn't it already been implemented? Although there isn't a single answer to this, one major element is simply the fact that the tech wasn't there to make it a viable solution for a long time. Now, however, it's finally time to tap into the full potential of the benefits of technology in farming through artificial intelligence.<h1>How AI Can Enable a Sustainable Future Through Mini-Farming</h1>There's a reason farms today rely on pesticides and chemical fertilizers instead of trying to grow crops through organic means — it's cheaper and easier to scale. To create a controlled environment that can grow quality produce at a highly efficient rate, you need systems in place that can analyze a wealth of data and act on its findings. In other words, you need AI and automation to make mini-farms work. For a long time, the future of AI in agriculture seemed like a pipe dream. That's no longer the case.You can already see <a href=https://www.japantimes.co.jp/news/2022/01/23/business/japan-farmers-sustainable-agriculture/>AI efforts at work in Japan</a> to make farming more sustainable. Through data analysis, farmers have been able to determine how many seedlings to plant and the best time to do so, reducing the need for special fertilizers and cutting the use of pesticides by as much as half. Takuro Sato, an organic farmer, is even developing a soil resistant to pests, using data analysis to determine the optimal compost ingredients for the land and an app to manage the temperature of the soil in each of his fields.When applied to mini-farming, AI can do even more. Algorithms can be employed to keep interior weather conditions at the most advantageous levels. Light, humidity, temperature — these can all be optimized by AI and managed through automation. AI will also continue to learn from each growth cycle and will be able to determine which breeds grow best in specific conditions. It will even be able to select the varieties that produce the tastiest and most nutritious fruits and vegetables.Food waste can also be a thing of the past, thanks to mini-farms and AI food waste management. Instead of growing a large number of crops per season and hoping most of it doesn't go to waste, mini-farms can take a more targeted approach. By applying demand forecasting to frequent, small-batch growth cycles, farmers can get much better ideas of which crops to produce during specific times of the year and how much is truly needed. Through this approach, farmers can have a food waste reduction roadmap that actually gets them where they need to be.In the past, micro-farming might have been an expensive, time-consuming process. Now, however, it has the power to be the more efficient and sustainable solution modern farming needs.

How can AI enable a sustainable future for the agriculture industry? By making it easier to implement mini-farms.

How AI and Mini-Farms Can Provide a Better Future for Farmers

Recently there's been a number of cases where a machine learning model was trained and deployed, and after deployment users realized that it was making predictions that were either biased against certain minority groups or just predictions that defy common sense. Such predictions can have obvious harmful consequences if not dealt with, and the damage done by such mistakes can derail an otherwise successful AI technological implementation program.If we hope to prevent these mistakes we need to take fairness seriously from the outset. This begins by identifying all stakeholders that could be affected by this technology's effects from the outset, at the planning and development stage, and making sure that they're involved in the process from the beginning through the development all the way through the quality assurance step.When it comes to data, we are all aware that data privacy is of utmost importance. Yet we've observed that many companies still don't have data management policies in place, and are not transparent about how they handle data. It's often difficult for users to understand what a certain company's data policy is, as it's buried under a long legal document that doesn't reflect the reality of their infrastructure. We believe that it's important to develop a simple, transparent data policy that helps customers make informed decisions about their data while at the same time increasing trust in the system.Budgeting enough time for quality control and inspection is also often overlooked. Not only do we need automated regression tests and to test on separate data that was unseen during model development, we also should have human inspection to test for edge cases. Being able to explain certain decisions the model makes also helps to allay certain fears. The field of model explainability is still very young and in some cases we don't have adequate answers for how to explain the models' output, but certain visualization techniques and mathematical guarantees, when coupled with extensive testing can provide reassurance that the model is unlikely to create a catastrophic mistake. Coupling machine learning models with logic-based systems is another way to provide safeguards for unpredictable scenarios. Crucially, we need to budget enough time for these tests, which often are overlooked in a rush to accelerate deployment.At recursive we believe that developing AI following such simple principles we can ensure that AI developments will have a positive effect on society. A recent publication "AI for social good" by leading AI researchers has suggested that AI development following certain recommendations will be a critical enabling technology for achieveing the SDGs.In "<a href=https://doi.org/10.1038/s41467-020-15871-z>AI for social good</a>" the authors outline the key principles for ethical AI development:<ol type=1><li>Expectations of what is possible with AI need to be well-grounded.</li><li>There is value in simple solutions.</li><li>Applications of AI need to be inclusive and accessible, and reviewed at every stage for ethics and human rights compliance.</li><li>Goals and use cases should be clear and well-defined.</li><li>Deep, long-term partnerships are required to solve large problems successfully.</li><li>Planning needs to align incentives, and factor in the limitations of both communities.</li><li>Establishing and maintaining trust is key to overcoming organisational barriers.</li><li>Options for reducing the development cost of AI solutions should be explored.</li><li>Improving data readiness is key.</li><li>Data must be processed securely, with utmost respect for human rights and privacy.</li></ol>We believe these guidelines form only the minimum set of requirements, and as we learn more surely we will only add to this list. Broadening the diversity of AI developers and including the broader community in AI development is critical to this effort.The deep long-term partnerships that are required to solve large problems successfully can only be achieved if all involved parties successfully build trust in each other.

An introduction to the principles that are important for AI development in terms of fairness and ethical considerations.

Fairness and ethics considerations

When training a model, a machine learning engineer will optimize the model's parameters in order to minimize the error on the collected training dataset. Success at this task however, does not mean the model will do well on unseen data. That's why it is common practice to test the model on a testing dataset containing data that the model has never seen before. Now, even if we see a good result on the test dataset, that doesn't necessarily mean that the model will do well in production.Data in production can differ from the data the model has been trained on in a number of ways. Different pre-processing techniques, temporal drift or different sources of data shifting the data subtly are just a few of the reasons why the data we've collected might not exactly match production data.For a successful deployment, there thus are a number of additional checks and tests we should perform to make sure we are deploying a robust system to the field. Just as a few examples, here are a few things to consider:<ul><li>A system to detect degradation in our model performance in real time and log data with high error or model uncertainty.</li><li>A system that can compare features in production data with expected data features to detect whether the environment has changed and we risk making incorrect predictions.</li><li>A system to capture novel data points that could be interesting to label and add to the training dataset.</li><li>Traditional rule-based systems to prevent outputs that could be significantly out-of-bounds for the problem at hand and prevent catastrophic failures.</li></ul>With these systems in place, we can implement a continuous monitoring and retraining system that can continuously update the model as we obtain more data. In this setting, it is crucial to have in place some way to keep a version control of the data so that we know on what data each model has been trained on. This helps us debug issues with model performance by checking whether they were trained with the correct data or not.If human labeling is necessary, we need a process by which we can efficiently label our new data. For example, a fast labelling platform and a way to track which data has been labeled and not; a set of guidelines for human labelers to define what to do in the case of ambiguous labels or to clarify vague definitions; and a verification procedure to make sure that the humans are not making mistakes themselves; as well as data analytics so that our researchers can check that that data set level statistics make sense.In realistic scenarios, certain labels are less frequent than others and we need to make sure that we balance our datasets. Then, we need to make sure that when we present our data to labelers, they can label the instances that we're more concerned with. For example, if we have a data set where we have 99% dogs and only one percent wolves we might not want to label 99 dog images for every wolf we label. It will be better to label only 10 dogs for every wolf to maximise the cost benefit of our labelling efforts.Finally, when deploying an AI system we need to consider the risk profile of mistakes. Mistakes are unavoidable, and even humans make them. In fact we've observed that in general a well trained AI model will make fewer mistakes on average than a human. However when an AI model does make a mistake, it can end up missing the mark much more than a human would, and that is a problem in terms of risk mitigation.So it is important to determine the level of autonomy that this system will have and how many safeguards as well as to what degree it needs to be tested before deployment. For example a recommendation for a video website probably can be deployed more quickly and iterated on much faster than an industrial control system, where a catastrophic error could cause a physical malfunction.Recently there has been a great push towards "explainable AI" systems. However, in our experience these systems don't really work as advertised, since state of the art AI models work precisely by performing calculations in a high dimensional space, and projecting them back into a low dimensional space for a human to visualize ends up over simplifying the model workings and are not enough to understand whether the model will correctly generalize in situations where it encounters very unfamiliar data. While there is value in visualization techniques for debugging and to have an idea of what the model is doing, we must do more if we want to prevent catastrophic failures.Instead, it is necessary to extensively test the model in unfamiliar situations, as well as present it with "edge cases", artificially constructed data points meant to cause the model to malfunction. For example, in the case of image classification, researchers have created a dataset of visually ambiguous images to determine whether the classification models were able to fail gracefully or would just make random predictions. This type of testing helped shape a new generation of more robust algorithms.

The third step to a successful AI implementation is deployment. Even if you get good results on a test dataset, it doesn't mean that the model will work well in production. Therefore, we will show you some examples of checks and tests.

How to lead a successful AI initiative: Step 3 Deployment

Often overlooked, the very first step in AI model development is to determine a combination of model and data that fulfills the objectives that we're looking for. This also involves determining exactly what will be output by the AI model and what objective is going to be optimized for.With the inputs and outputs of the model in mind, we have an idea of the dataset required to successfully train the model. The first step is to take stock of how much data we have right now and how much more data will be necessary to collect. If we do need to collect additional data there are various trade-offs to consider, namely how much does it cost to acquire additional data points, and how much it costs to label them (which could vary depending on the level of expertise required of the human labelers).Depending on these variables we can estimate the size of the initial training dataset, and this can be compared to the cost of developing different training techniques which may require more or less data. For example, data augmentation, regularization and transfer learning are techniques which make it feasible to train on smaller datasets, while techniques such as self-supervised learning may make it possible to train on unlabeled data. All these techniques however, will increase the time needed to develop the model.After considering the situation with the data and having decided on a modelling technique to handle it, engineers can start implementing the model architecture. That is, an algorithm that can take in data and an output, which could be a prediction or a decision. To train it, we perform an iterative learning process on the computer where the algorithm is fed the training data, its predictions are compared to the desired output, and an error signal is calculated. From this error signal, we can update the model's parameters until it minimizes the error.Once the model is trained it's very important to check that it hasn't memorized the data, and that it is generalizing in a way that is going to be helpful. So we need to have an analysis step where we calculate the model's results on a test set, with data it's never seen before. As well as calculate how robust it is to perturbations. And perhaps we need to optimize it by tuning the parameters or by training it again with a different architecture.During this iterative process it may happen that we realize that some of our prior assumptions were incorrect. At that point, we might decide that we need to change either the training data or the algorithm and train it again. That means we have another, higher-level learning loop at the human stage. This is very different from a traditional software development process where we can make a test, write the code and then we can immediately check whether this code we've written is correct.It is then crucial to understand whether we've budgeted enough time for this iterative development loop, which depends on whether this kind of model has been done before and we can take the learnings from another team, or we are developing something from scratch; as well as what are the tolerances for accuracy and robustness, which can make development time and costs much higher. In certain cases, if the way the model interfaces with the environment is controlled by a human or involves some kind of feedback loop, it might be acceptable to trade off some accuracy for a faster turnaround time in development.From the compute perspective, this iterative process can take a long time and training the model various times for different architectures, hyper parameters and so on will expend a very large amount of computational power. Additionally, the machine learning engineer will often need to wait for an experiment's results before proceeding with the learning loop. So when we're budgeting for our development process, we need to account for both the longer time for research and development as well as account for the compute cost to train the model multiple times.

The second step to a successful AI implementation is model development. Often overlooked, the first step in AI model development is to decide on the combination of model and data that will meet the objectives you are looking for.

AIPress release	2025-01-21	AIPress release2025-01-21【Press Release】AI startup, Recursive, develops an AI model for Kyoikudojinsha, a Japanese publisher of educational materials: enables automatic evaluation of whether answers handwritten by children are correct
AIPress release	2024-12-12	AIPress release2024-12-12【Press Release】AI Startup, Recursive, Expands into Overseas Markets, Appoints Social Entrepreneur Taylor Scobbie as Director of Overseas
AIMedia	2024-12-10	AIMedia2024-12-10Recursive's Blog Featured in Online Japanese Trade Media for IT Engineers, Levtech Freelance

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