Abstract Scientific progress is limited not by a lack of new ideas but by the time and cost involved in physical experimentation. Scientific discovery is a needle in the haystack problem: it does not help if AI gives you a vastly bigger haystack. Without knowing if any of the ideas work, an AI system that designs experiments just increases the effort required, since performing the experiments to validate the ideas is the real bottleneck. In my view, AI's most transformative impact in enabling scientific discoveries lies in reducing the need for such experiments. To get there, we need to build AI models that are able to granularly simulate and understand physics at all scales, rather than just abstractly reason in the textual domain. In this essay, I explore what methods like Neural Operators have already helped achieve, what still needs to be done, and what lies ahead.
Anima Anandkumar (Thu,) studied this question.