Taming the data deluge
A National Science Foundation-funded team will use artificial intelligence to speed up discoveries in physics, astronomy, and neuroscience.
A National Science Foundation-funded team will use artificial intelligence to speed up discoveries in physics, astronomy, and neuroscience.
A visual analytics tool helps child welfare specialists understand machine learning predictions that can assist them in screening cases.
Neuroscientists find the internal workings of next-word prediction models resemble those of language-processing centers in the brain.
PhD candidate Charlene Xia is developing a low-cost system to monitor the microbiome of seaweed farms and identify diseases before they spread.
A new control system, demonstrated using MIT’s robotic mini cheetah, enables four-legged robots to jump across uneven terrain in real-time.
When asked to classify odors, artificial neural networks adopt a structure that closely resembles that of the brain’s olfactory circuitry.
Cardiologist Demilade Adedinsewo is using her MIT Professional Education experience to advance cardiovascular care at the Mayo Clinic.
A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods.
A certain type of artificial intelligence agent can learn the cause-and-effect basis of a navigation task during training.
Wise Systems has grown from an MIT class project to a company helping multinationals improve last-mile logistics.
With a double major in linguistics and computer science, senior Rujul Gandhi works to surmount language and cultural barriers, globally and on campus.
A deep model was trained on historical crash data, road maps, satellite imagery, and GPS to enable high-resolution crash maps that could lead to safer roads.
Researchers find blind and sighted readers have sharply different takes on what content is most useful to include in a chart caption.
Secure AI Labs, founded by alumna Anne Kim and MIT Professor Manolis Kellis, anonymizes data for AI researchers.
Researchers hope more user-friendly machine-learning systems will enable nonexperts to analyze big data — but can such systems ever be completely autonomous?