some remarks on learning to learn

From Chelsea Finn Jul 18, 2017

Current AI systems can master a complex skill from scratch, using an understandably large amount of time and experience. But if we want our agents to be able to acquire many skills and adapt to many environments, we cannot afford to train each skill in each setting from scratch. Instead, we need our agents to learn how to learn new tasks faster by reusing previous experience, rather than considering each new task in isolation. This approach of learning to learn, or meta-learning, is a key stepping stone towards versatile agents that can continually learn a wide variety of tasks throughout their lifetimes.

From Ke Li Sep 12, 2017

the components of learning to learn:
The term traces its origins to the idea of metacognition (Aristotle, 350 BC), which describes the phenomenon that humans not only reason, but also reason about their own process of reasoning. Work on “learning to learn” draws inspiration from this idea and aims to turn it into concrete algorithms. Roughly speaking, “learning to learn” simply means learning something about learning. What is learned at the meta-level differs across methods. We can divide various methods into three broad categories according to the type of meta-knowledge they aim to learn:

• Learning What to Learn
• Learning Which Model to Learn
• Learning How to Learn