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.
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.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
Read MorePatricia and James Poitras ’63 provide fellowships for graduate students and postdocs who will shape the future of mental health research.
Read MoreFounded by Jake Donoghue PhD ’19 and former MIT researcher Jarrett Revels, the company is creating an AI-driven platform to help diagnose and treat disease.
Read MoreOpening a new window on the brainstem, a new tool reliably and finely resolves distinct nerve bundles in live diffusion MRI scans, revealing signs of injury or disease.
Read MoreWith support from the Siegel Family Endowment, the newly renamed MIT Siegel Family Quest for Intelligence investigates how brains produce intelligence and how it can be replicated to solve problems.
Read MoreCSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
Read MoreMIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.
Read MoreMedia Lab PhD student Kimaya Lecamwasam researches how music can shape well-being.
Read MoreTools build on years of research at Lincoln Laboratory to develop a rapid brain health screening capability and may also be applicable to civilian settings such as sporting events and medical offices.
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