Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.
Corvus Robotics, founded by Mohammed Kabir ’21, is using drones that can navigate in GPS-denied environments to expedite inventory management.
The neuroscientist turned entrepreneur will be hosted by the MIT Schwarzman College of Computing and focus on advancing the intersection of behavioral science and AI across MIT.
MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.
An electronic stacking technique could exponentially increase the number of transistors on chips, enabling more efficient AI hardware.
Junior Katie Spivakovsky describes her path through New Engineering Education Transformation to biomedical research and beyond.
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
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.
The MIT senior will pursue graduate studies in the UK at Cambridge University and Imperial College London.
Five MIT faculty members and two additional alumni are honored with fellowships to advance research on beneficial AI.
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.
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.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.
Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.