The sweet taste of a new idea
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.
Laboratory for Information and Decision Systems (LIDS)
With AI, researchers predict the location of virtually any protein within a human cell
Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.
Study shows vision-language models can’t handle queries with negation words
Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.
New tool evaluates progress in reinforcement learning
“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.
Designing a new way to optimize complex coordinated systems
Using diagrams to represent interactions in multipart systems can provide a faster way to design software improvements.
A faster way to solve complex planning problems
By eliminating redundant computations, a new data-driven method can streamline processes like scheduling trains, routing delivery drivers, or assigning airline crews.
Researchers teach LLMs to solve complex planning challenges
This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.
Validation technique could help scientists make more accurate forecasts
MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.
Expanding robot perception
Associate Professor Luca Carlone is working to give robots a more human-like awareness of their environment.
Algorithms and AI for a better world
Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.