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 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 MoreA new method developed at MIT could root out vulnerabilities and improve LLM safety and performance.
Read MoreBy minimizing the need to drive around looking for a parking spot, this technique can save drivers up to 35 minutes — and give them a realistic estimate of total travel time.
Read MoreThe context of long-term conversations can cause an LLM to begin mirroring the user’s viewpoints, possibly reducing accuracy or creating a virtual echo-chamber.
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