Toward speech recognition for uncommon spoken languages
Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.
Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.
The Common Ground for Computing Education is facilitating collaborations to develop new classes for students to pursue computational knowledge within the context of their fields of interest.
A National Science Foundation-funded team will use artificial intelligence to speed up discoveries in physics, astronomy, and neuroscience.
Graduate student Nicholas Kamp describes the MicroBooNE experiment and its implications for our understanding of fundamental particles.
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.
Artificial intelligence is top-of-mind as Governor Baker, President Reif encourage students to “see yourself in STEM.”
A new control system, demonstrated using MIT’s robotic mini cheetah, enables four-legged robots to jump across uneven terrain in real-time.
Social robotics and artificial intelligence pioneer will oversee business units and help to guide innovative learning initiatives.
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.
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.