How to assess a general-purpose AI model’s reliability before it’s deployed
A new technique enables users to compare several large models and choose the one that works best for their task.
Laboratory for Information and Decision Systems (LIDS)
When to trust an AI model
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
School of Engineering welcomes new faculty
Fifteen new faculty members join six of the school’s academic departments.
A crossroads for computing at MIT
The MIT Schwarzman College of Computing building will form a new cluster of connectivity across a spectrum of disciplines in computing and artificial intelligence.
Using generative AI to improve software testing
MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.
Dealing with the limitations of our noisy world
Tamara Broderick uses statistical approaches to understand and quantify the uncertainty that can affect study results.
New AI model could streamline operations in a robotic warehouse
By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.
Generative AI for smart grid modeling
MIT LIDS awarded funding from the Appalachian Regional Commission as part of a multi-state collaborative project to model and test new smart grid technologies for use in rural areas.
MIT Generative AI Week fosters dialogue across disciplines
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
AI accelerates problem-solving in complex scenarios
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.