Q&A: How AI came to model the “messy” human mind
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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Research
Why teaching AI about symmetry could make prenatal diagnosis more reliable
A model developed by researchers at MIT and Boston Children’s Hospital adjusts scans on the fly to track fetal movement, potentially sparing families repeat hospital visits.
Read MoreGiving AI a “crystal clear” view of the materials world
New research can help AI models understand crystalline materials in 3D and reason about their properties.
Read MoreState of Supply Chain Sustainability Report finds corporate commitment at record high, but governance lags behind
MIT’s Center for Transportation and Logistics finds sustainability commitments are strong, but governance gaps and external disruptions prevent execution.
Read MoreAI model decodes the language cells use to communicate
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Read MoreQ&A: The “collision” between AI, your brain, and society
In a new book, Professor Eric So explores how the technology affects our thinking skills.
Read MoreResearchers improve weather forecasts looking two to six weeks out
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Read MoreHow 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.
Read MoreSupercomputing researchers document evolution of AI hardware
An ongoing survey tracks the latest AI accelerator systems to keep hardware relevant for Lincoln Laboratory staff and sponsors.
Read MoreMIT Transit Lab to develop an AI platform for public transit agencies
With $2.1 million funding from Google.org, the open-source Public Transit Intelligence Hub will unify public transit monitoring, operations, and passenger communication.
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