Solving brain dynamics gives rise to flexible machine-learning models
MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.
MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.
Students describe what it’s like to compete at the very top tiers of computing.
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.
This machine-learning system can simulate how a listener would hear a sound from any point in a room.
Yilun Du, a PhD student and MIT CSAIL affiliate, discusses the potential applications of generative art beyond the explosion of images that put the web into creative hysterics.
The technique could be used to fabricate computer chips that won’t get too hot while operating, or materials that can convert waste heat to energy.
A new technique enables AI models to continually learn from new data on intelligent edge devices like smartphones and sensors, reducing energy costs and privacy risks.
A machine-learning method finds patterns of health decline in ALS, informing future clinical trial designs and mechanism discovery. The technique also extends to Alzheimer’s and Parkinson’s.
Aleksander Madry, Asu Ozdaglar, and Luis Videgaray, co-chairs of the AI Policy Forum, discuss key issues facing the AI policy landscape today.
Researchers created a system that lets robots effectively use grasped tools with the correct amount of force.
MIT professor to share $3 million prize with three others; Daniel Spielman PhD ’95 wins Breakthrough Prize in Mathematics.
By continuously monitoring a patient’s gait speed, the system can assess the condition’s severity between visits to the doctor’s office.
Mayor’s youth employment program brought local high schoolers to MIT this summer.
An interdisciplinary team is developing a mobile health platform that uses AI to detect infection in Cesarean section wounds.
Researchers develop a new method that uses multiple models to create more complex images with better understanding.