Artificial intelligence for science: The easy and hard problems

TL;DR

This paper distinguishes between the 'easy' problems AI has solved in science through data-driven optimization and the 'hard' problem of formulating scientific questions, proposing cognitive science insights to develop better AI agents.

Contribution

It highlights the gap between current AI capabilities and the hard problem of scientific discovery, suggesting a focus on cognitive science to improve AI's problem formulation abilities.

Findings

AI has advanced in solving optimization problems in science.

Current AI struggles with formulating new scientific problems.

Studying human cognition can inform AI development for the hard problem.

Abstract

A suite of impressive scientific discoveries have been driven by recent advances in artificial intelligence. These almost all result from training flexible algorithms to solve difficult optimization problems specified in advance by teams of domain scientists and engineers with access to large amounts of data. Although extremely useful, this kind of problem solving only corresponds to one part of science - the "easy problem." The other part of scientific research is coming up with the problem itself - the "hard problem." Solving the hard problem is beyond the capacities of current algorithms for scientific discovery because it requires continual conceptual revision based on poorly defined constraints. We can make progress on understanding how humans solve the hard problem by studying the cognitive science of scientists, and then use the results to design new computational agents that automatically infer and update their scientific paradigms.