MIT scientists investigate memorization risk in the age of clinical AI
New research demonstrates how AI models can be tested to ensure they don’t cause harm by revealing anonymized patient health data.
Research
New Research on Copycat Products, Your Pitch-Day Dress Code, When Hand Gestures Help Persuasion, and More
And what these findings could mean for you and your business.
Read MoreMIT in the media: 2025 in review
MIT community members made headlines with key research advances and their efforts to tackle pressing challenges.
Read MoreGuided learning lets “untrainable” neural networks realize their potential
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
Read MoreA new way to increase the capabilities of large language models
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.
Read MoreA “scientific sandbox” lets researchers explore the evolution of vision systems
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
Read More“Robot, make me a chair”
An AI-driven system lets users design and build simple, multicomponent objects by describing them with words.
Read MoreDeep-learning model predicts how fruit flies form, cell by cell
The approach could apply to more complex tissues and organs, helping researchers to identify early signs of disease.
Read MoreEnabling small language models to solve complex reasoning tasks
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
Read MoreNew Research on AI and Fairness in Hiring
AI promises to make hiring fairer by reducing human bias. But it often reshapes what fairness means.
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