Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in
  AlphaZero
Lisa Schut Nenad Tomasev Tom McGrath Demis Hassabis Ulrich Paquet Been Kim
Artificial Intelligence (AI) systems have made remarkable progress, attaining
super-human performance across various domains. This presents us with an
opportunity to further human knowledge and improve human expert performance by
leveraging the hidden knowledge encoded within these highly performant AI
systems. Yet, this knowledge is often hard to extract, and may be hard to
understand or learn from. Here, we show that this is possible by proposing a
new method that allows us to extract new chess concepts in AlphaZero, an AI
system that mastered the game of chess via self-play without human supervision.
Our analysis indicates that AlphaZero may encode knowledge that extends beyond
the existing human knowledge, but knowledge that is ultimately not beyond human
grasp, and can be successfully learned from. In a human study, we show that
these concepts are learnable by top human experts, as four top chess
grandmasters show improvements in solving the presented concept prototype
positions. This marks an important first milestone in advancing the frontier of
human knowledge by leveraging AI; a development that could bear profound
implications and help us shape how we interact with AI systems across many AI
applications.