Like human brains, large language models reason about diverse data in a general way
A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.
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AI model deciphers the code in proteins that tells them where to go
Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.
Cultivators of research
Professors Maria Yang and Kenneth Oye are honored as “Committed to Caring” for their mentorship of graduate students.
Bridging philosophy and AI to explore computing ethics
In a new MIT course co-taught by EECS and philosophy professors, students tackle moral dilemmas of the digital age.
To keep hardware safe, cut out the code’s clues
New “Oreo” method from MIT CSAIL researchers removes footprints that reveal where code is stored before a hacker can see them.
Creating smart buildings with privacy-first sensors
Butlr, founded by former Media Lab researchers, uses insights from thermal sensors to make buildings safe and efficient.
Puzzling out climate change
Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.
Creating a common language
New faculty member Kaiming He discusses AI’s role in lowering barriers between scientific fields and fostering collaboration across scientific disciplines.
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.
Streamlining data collection for improved salmon population management
Assistant Professor Sara Beery is using automation to improve monitoring of migrating salmon in the Pacific Northwest.
Aligning AI with human values
“We need to both ensure humans reap AI’s benefits and that we don’t lose control of the technology,” says senior Audrey Lorvo.
Introducing the MIT Generative AI Impact Consortium
The consortium will bring researchers and industry together to focus on impact.
User-friendly system can help developers build more efficient simulations and AI models
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
With generative AI, MIT chemists quickly calculate 3D genomic structures
A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.