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American Academy of Arts and Sciences publishes Dædalus special issue 'AI & Science: What Is the Future of Discovery?' edited by James Manyika

Anima Anandkumar examines AI simulation of physical processes.

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I am thrilled that my article in @aaas Daedalus special issue on AI & Science: What Is the Future of Discovery? edited by James Manyika. https://www.amacad.org/daedalus/ai-science-what-is-the-future-of-discovery I talk about : How Do We Build AI to Push the Frontiers of Scientific Discovery? 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. I explore what methods like Neural Operators have already helped achieve, what still needs to be done, and what lies ahead.

3:06 PM · May 22, 2026 View on X

I am thrilled that my article in @americanacad Daedalus special issue on AI & Science: What Is the Future of Discovery? edited by James Manyika. https://amacad.org/daedalus/ai-science-what-is-the-future-of-discovery I talk about : How Do We Build AI to Push the Frontiers of Scientific Discovery? 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. I explore what methods like Neural Operators have already helped achieve, what still needs to be done, and what lies ahead.

10:20 PM · May 22, 2026 · 2K Views