An open source article from the latest PNAS from Schut et al.:
Significance
As
AI systems become more capable, they may internally represent concepts
outside the sphere of human knowledge. This work gives an end-to-end
example of unearthing machine-unique knowledge in the domain of chess.
We obtain machine-unique knowledge from an AI system (AlphaZero) by a
method that finds novel yet teachable concepts and show that it can be
transferred to human experts (grandmasters). In particular, the new
knowledge is learned from internal mathematical representations without a
priori knowing what it is or where to start. The produced knowledge
from AlphaZero (new chess concepts) is then taught to four grandmasters
in a setting where we can quantify their learning, showing that
machine-guided discovery and teaching is possible at the highest human
level.
Abstract
AI
systems have attained superhuman performance across various domains. If
the hidden knowledge encoded in these highly capable systems can be
leveraged, human knowledge and performance can be advanced. Yet, this
internal knowledge is difficult to extract. Due to the vast space of
possible internal representations, searching for meaningful new
conceptual knowledge can be like finding a needle in a haystack. Here,
we introduce a method that extracts new chess concepts from AlphaZero,
an AI system that mastered chess via self-play without human
supervision. Our method excavates vectors that represent concepts from
AlphaZero’s internal representations using convex optimization, and
filters the concepts based on teachability (whether the concept is
transferable to another AI agent) and novelty (whether the concept
contains information not present in human chess games). These steps
ensure that the discovered concepts are useful and meaningful. For the
resulting set of concepts, prototypes (chess puzzle–solution pairs) are
presented to experts for final validation. In a preliminary human study,
four top chess grandmasters (all former or current world chess
champions) were evaluated on their ability to solve concept prototype
positions. All grandmasters showed improvement after the learning phase,
suggesting that the concepts are at the frontier of human
understanding. Despite the small scale, our result is a proof of concept
demonstrating the possibility of leveraging knowledge from a highly
capable AI system to advance the frontier of human knowledge; a
development that could bear profound implications and shape how we
interact with AI systems across many applications.