Learning to Continually Learn

  • Clune, Jeff
  • Invited Talk
  • [Slides]

Abstract

A dominant trend in machine learning is that hand-designed pipelines are replaced by higher-performing learned pipelines once sufficient compute and data are available. I argue that trend will apply to machine learning itself, and thus that the fastest path to truly powerful AI is to create AI-generating algorithms (AI-GAs) that on their own *learn* to solve the hardest AI problems. This paradigm is an all-in bet on meta-learning. After introducing these ideas, the talk focuses on one example of this paradigm: Learning to Continually Learn. I describe a Neuromodulated Meta-Learning algorithm (ANML), which uses meta-learning to try to solve catastrophic forgetting, producing state-of-the-art results.

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