Fun LoL to Teach Machines How to Learn More Efficiently

Fun LoL brings rigor to quest for the ultimate learning machine.  #math #AI #machinelearning

  • The objective of Fun LoL is to investigate and characterize fundamental limits of machine learning with supportive theoretical foundations to enable the design of systems that learn more efficiently.
  • DARPA seeks mathematical framework to characterize fundamental limits of learning
  • To find answers to these questions, DARPA recently announced its Fundamental Limits of Learning (Fun LoL) program.
  • The goal of Fun LoL is to achieve a similar mathematical breakthrough for machine learning and AI.”
  • If you slightly tweak a few rules of the game Go, for example, the machine won’t be able to generalize from what it already knows.

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@DARPA: “Fun LoL brings rigor to quest for the ultimate learning machine. #math #AI #machinelearning”


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 of machine learning with supportive theoretical foundations to enable the design of systems that learn more efficiently.


Fun LoL to Teach Machines How to Learn More Efficiently