AI system learns to keep warehouse robot traffic running smoothly
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
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3 Questions: On the future of AI and the mathematical and physical sciences
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.
Read MoreAI to help researchers see the bigger picture in cell biology
By providing holistic information on a cell, an AI-driven method could help scientists better understand disease mechanisms and plan experiments.
Read MoreEnhancing maritime cybersecurity with technology and policy
Strahinja Janjusevic brings an international perspective and US Naval Academy education to his graduate research in the MIT Technology and Policy Program.
Read MoreParking-aware navigation system could prevent frustration and emissions
By 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 MorePersonalization features can make LLMs more agreeable
The 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.
Read More3 Questions: Using AI to help Olympic skaters land a quint
MIT Sports Lab researchers are applying AI technologies to help figure skaters improve. They also have thoughts on whether five-rotation jumps are humanly possible.
Read MoreStudy: Platforms that rank the latest LLMs can be unreliable
Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
Read MoreThe technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.
Read MoreA smarter way for large language models to think about hard problems
This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.
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