Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
Read MoreA new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
Read MoreThe approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.
Read MoreEnd-to-end AI tools that are tuned to specific industries and case types can dramatically boost productivity.
Read MoreInsights from conversations with CEOs, CHROs, and innovation leaders.
Read MoreBy leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Read MoreTo help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.
Read MoreBy providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
Read MoreStrahinja Janjusevic brings an international perspective and US Naval Academy education to his graduate research in the MIT Technology and Policy Program.
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