How artificial neural networks build their own learning machinery
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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McGovern Institute
Estimating suicide risk from text
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
Read MorePoitras Center to fuel early careers of 50 young scientists dedicated to psychiatric disorders research
Patricia and James Poitras ’63 provide fellowships for graduate students and postdocs who will shape the future of mental health research.
Read MoreThe cost of thinking
MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.
Read MorePresentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.
Read MoreRationale engineering generates a compact new tool for gene therapy
Researchers redesign a compact RNA-guided enzyme from bacteria, making it an efficient editor of human DNA.
Read MoreA quarter century after its founding, the McGovern Institute reflects on its discoveries in the areas of neuroscience, neurotechnology, artificial intelligence, brain-body connections, and therapeutics.
Read MoreThe programmable proteins are compact, modular, and can be directed to modify DNA in human cells.
Read MoreFor healthy hearing, timing matters
Machine-learning models let neuroscientists study the impact of auditory processing on real-world hearing.
Read MoreFour from MIT named 2025 Rhodes Scholars
Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.
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