Higher industrial productivity allows us to maintain modern standards of living with less impact on the environment. The more efficient agro-industrial production is, the less land area and natural resources we use and fewer pollution we generate.
In the agriculture business, AI is being used to monitor lifestock
at a large scale, improving productivity per animal; as well as automatically identify and remove pests
, which reduces the need for pesticides.
Automated food sorting devices can be used to order food items according to consumer preferences, optimizing product quality automatically. These technologies rely on automatic instance segmentation
, a technology which has seen huge progress the last few years.
In industrial production, instance segmentation and classification is also an important tool used in automated quality control inspection as well as in robotics. For example a robotic tool may be able to select specific parts from a bin or measure how many items are in a specific area using vision alone.
AI can also be used for assembly line integration and optimization. Legacy systems consist of individual machines optimized for a specific task, with gaps in the process filled by humans. Using techniques such as global optimization
and endowing each tool with sensors, the assembly line can be completely automated and integrated.
Forecasting techniques can be applied to historical data for predictive maintenance techniques. For example, we can predict when a tool is likely to fail and preemptively deploy a maintenance team. Resources can also be intelligently redirected by an AI controller in the case of a fault to prevent a system shutdown.
Automated control systems powered by AI can also be used to optimize energy efficiency
, as well as reduce pesticide usage
by identifying which areas need to be sprayed or not. Similarly, food manufacturers can optimize their production processes
to use less raw material and reduce calorie counts in their final products.
Logistics is another area ripe for disruption. A machine learning based algorithm
can predict public transit delays with up to 30% higher accuracy than existing algorithms, while a graph neural network
based system achieved up to 50% more accurate travel time predictions on Google Maps.
These algorithms can be readily applied to global supply chains and logistics networks, potentially reducing their carbon footprint as well as speeding up transit times and reducing waste of perishable goods due to delays.
Reinforcement learning is a promising technology to automate certain types of tasks that are hard to define specifically using rules or quantitive functions. This has the potential to revolutionize robotics
in the manufacturing industry by freeing up humans from repetitive, but infrequent, tasks.