The dedicated teacher and academic leader transformed research in computer architectures, parallel computing, and digital design, enabling faster and more efficient computation.
The program focused on AI in health care, drawing on Takeda’s R&D experience in drug development and MIT’s deep expertise in AI.
Graduate engineering program is No. 1 in the nation; MIT Sloan is No. 5.
LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.
The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.
Combining natural language and programming, the method enables LLMs to solve numerical, analytical, and language-based tasks transparently.
The method uses language-based inputs instead of costly visual data to direct a robot through a multistep navigation task.
DenseAV, developed at MIT, learns to parse and understand the meaning of language just by watching videos of people talking, with potential applications in multimedia search, language learning, and robotics.
In the new economics course 14.163 (Algorithms and Behavioral Science), students investigate the deployment of machine-learning tools and their potential to understand people, reduce bias, and improve society.
Smaller than a coin, this optical device could enable rapid prototyping on the go.
During the MIT Science Policy Initiative’s Congressional Visit Days, PhD students and postdocs met with legislators to share expertise and advocate for science agency funding.
Ranking at the top for the 13th year in a row, the Institute also places first in 11 subject areas.
The fellowships provide five years of funding to doctoral students in applied science, engineering, and mathematics who have “the extraordinary creativity and principled leadership necessary to tackle problems others can’t solve.”
MIT CSAIL’s frugal deep-learning model infers the hidden physical properties of objects, then adapts to find the most stable grasps for robots in unstructured environments like homes and fulfillment centers.
With generative AI models, researchers combined robotics data from different sources to help robots learn better.