Project Bonsai pushes the frontier of AI to a point where intelligent control systems do not rely on data alone to solve problems. The technology is targeted at autonomous industrial robots.
Introduction of machine teaching Project Bonsai
Traditional AI agents use machine learning (ML) to become smart to the point of solving different kinds of problems. They learn based on data collected autonomously. But machine teaching introduces human input in the learning process. For Microsoft, the goal is to enable end-users to develop and use autonomous control systems.
How Project Bonsai works
Machine teaching is not the opposite of ML. Instead, it is a complementary technique that fast-tracks the ML process for AI agents. With Project Bonsai, experts, such as engineers, break a problem into easier modules or tasks. Then, they teach ML models how to find a solution faster. The machine teaching approach has multiple advantages over ML alone, according to Microsoft. For starters, it enables professionals in specific fields to build autonomous systems without having to be AI experts. Similarly, the approach allows users to develop a perfect grasp of how the AI agents work.
Project Moab
Project Moab is a robot that is essential in the implementation of Bonsai. As such, its purpose is enabling engineers and developers to learn how to build AI-powered control systems with machine teaching. Machine teaching Project Bonsai is a futuristic idea that has positive implications for autonomous industrial systems. If you have any questions or suggestions about AI systems, feel free to leave them in the comments section below.
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