AI system can generate novel proteins that meet structural design targets
These tunable proteins could be used to create new materials with specific mechanical properties, like toughness or flexibility.
These tunable proteins could be used to create new materials with specific mechanical properties, like toughness or flexibility.
“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.
Researchers create a trajectory-planning system that enables drones working together in the same airspace to always choose a safe path forward.
Annual award honors early-career researchers for creativity, innovation, and research accomplishments.
A new experiential learning opportunity challenges undergraduates across the Greater Boston area to apply their AI skills to a range of industry projects.
MIT researchers are discovering which parts of the brain are engaged when a person evaluates a computer program.
New technique significantly reduces training and inference time on extensive datasets to keep pace with fast-moving data in finance, social networks, and fraud detection in cryptocurrency.
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.
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.
On its own, a new machine-learning model discovers linguistic rules that often match up with those created by human experts.
Engineers working on “analog deep learning” have found a way to propel protons through solids at unprecedented speeds.
Recent MEng graduates reflect on their application-focused research as affiliates of the MIT-IBM Watson AI Lab.
Studying a powerful type of cyberattack, researchers identified a flaw in how it’s been analyzed before, then developed new techniques that stop it in its tracks.
A machine-learning method imagines what a sentence visually looks like, to situate and ground its semantics in the real world, improving translation, like humans can.