Fun LoL to Teach Machines How to Learn More Efficiently
It’s not easy to put the intelligence in artificial intelligence. Current machine learning techniques generally rely on huge amounts of training data, vast computational resources, and a time-consuming trial and error methodology. Even then, the process typically results in learned concepts that aren’t easily generalized to solve related problems or that can’t be leveraged to learn more complex concepts. The process of advancing machine learning could no doubt go more efficiently—but how much so? To date, very little is known about the limits of what could be achieved for a given learning problem or even how such limits might be determined. To find answers to these questions, DARPA recently announced its Fundamental Limits of Learning (Fun LoL) program. The objective of Fun LoL is to investigate and characterize fundamental limits…
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