MIT engineers grow “high-rise” 3D chips
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
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3 Questions: Tracking MIT graduates’ career trajectories
Deborah Liverman, executive director of MIT Career Advising and Professional Development, offers a window into undergraduate and graduate students’ post-graduation paths.
MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
Study reveals AI chatbots can detect race, but racial bias reduces response empathy
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
Lara Ozkan named 2025 Marshall Scholar
The MIT senior will pursue graduate studies in the UK at Cambridge University and Imperial College London.
MIT affiliates named 2024 Schmidt Sciences AI2050 Fellows
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
In a unique research collaboration, students make the case for less e-waste
SERC Scholars from around the MIT community examine the electronic hardware waste life cycle and climate justice.
Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.
AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Study: Some language reward models exhibit political bias
Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.
Enabling AI to explain its predictions in plain language
Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.
Daniela Rus wins John Scott Award
MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.
Introducing MIT HEALS, a life sciences initiative to address pressing health challenges
The MIT Health and Life Sciences Collaborative will bring together researchers from across the Institute to deliver health care solutions at scale.
Citation tool offers a new approach to trustworthy AI-generated content
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.