Manipulating the future
A new robotic manipulation course provides a broad survey of state-of-the-art robotics, equipping students to identify and solve the field’s biggest problems.
A new robotic manipulation course provides a broad survey of state-of-the-art robotics, equipping students to identify and solve the field’s biggest problems.
When artificial intelligence is tasked with visually identifying objects and faces, it assigns specific components of its network to face recognition — just like the human brain.
A new technique compares the reasoning of a machine-learning model to that of a human, so the user can see patterns in the model’s behavior.
MIT researchers design a robot that has a trick or two up its sleeve.
For individuals who communicate using a single switch, a new interface learns how they make selections, and then self-adjusts accordingly.
For the MIT Schwarzman College of Computing dean, bringing disciplines together is the best way to address challenges and opportunities posed by rapid advancements in computing.
An efficient machine-learning method uses chemical knowledge to create a learnable grammar with production rules to build synthesizable monomers and polymers.
A new technique could enable a robot to manipulate squishy objects like pizza dough or soft materials like clothing.
Brent Minchew leads two proposals to better understand glacial physics and predict sea-level rise as part of MIT's Climate Grand Challenges competition.
A new technique for removing bias in datasets can enable machine-learning models to make loan approval predictions that are both fair and accurate.
Associate professor and principal investigator with the MIT Schwarzman College of Computing’s Science Hub discusses the future of robotics and the importance of industry-academia collaborations.
“Privid” could help officials gather secure public health data or enable transportation departments to monitor the density and flow of pedestrians, without learning personal information about people.
An MIT team incorporates AI to facilitate the detection of an intriguing materials phenomenon that can lead to electronics without energy dissipation.
Researchers have developed a technique for making quantum computing more resilient to noise, which boosts performance.
CSAIL scientists came up with a learning pipeline for the four-legged robot that learns to run entirely by trial and error in simulation.