Improving AI models’ ability to explain their predictions
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
MIT Schwarzman College of Computing
A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster
The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.
Read MoreNew method could increase LLM training efficiency
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Read MoreMixing generative AI with physics to create personal items that work in the real world
To 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 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 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 MoreHelping AI agents search to get the best results out of large language models
EnCompass executes AI agent programs by backtracking and making multiple attempts, finding the best set of outputs generated by an LLM. It could help coders work with AI agents more efficiently.
Read More
You must be logged in to post a comment.