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.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
Researchers in the Chisholm lab find Prochlorococcus can switch between drifting alone and living in attached communities, a discovery that could reshape estimates of how much carbon the ocean moves to the deep sea.
In six talks, researchers from across MIT described projects aimed at advancing the understanding of and interventions against neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and ALS.
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.
New research can help AI models understand crystalline materials in 3D and reason about their properties.
MIT's Center for Transportation and Logistics finds sustainability commitments are strong, but governance gaps and external disruptions prevent execution.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
The cliffs don’t always collapse in the runaway process that scientists had expected.
Governments and private companies often work together to provide public services online. A new study asks: How do these partnerships affect people’s trust in government?
In a new book, Professor Eric So explores how the technology affects our thinking skills.
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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 approach can program these cells inside the body and shows promise against diseases caused by dysfunctional T cells, including lymphoma and autoimmune hepatitis.
Developed by MIT engineers, this flexible material could be used as a wearable, motion-monitoring “skin” in physical therapy, virtual reality, and gaming.