Ushering in a new era of computing
Dan Huttenlocher is a professor of electrical engineering and computer science and the inaugural dean at MIT Schwarzman College of Computing.
Busy GPUs: Sampling and pipelining method speeds up deep learning on large graphs
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.
Breaking the scaling limits of analog computing
New technique could diminish errors that hamper the performance of super-fast analog optical neural networks.
Teresa Gao named 2024 Mitchell Scholar
The MIT senior will pursue postgraduate studies in computer science in Ireland.
A simpler path to better computer vision
New research reveals a scalable technique that uses synthetic data to improve the accuracy of AI models that recognize images.
A far-sighted approach to machine learning
New system can teach a group of cooperative or competitive AI agents to find an optimal long-term solution.
Solving brain dynamics gives rise to flexible machine-learning models
MIT CSAIL researchers solve a differential equation behind the interaction of two neurons through synapses to unlock a new type of speedy and efficient AI algorithm.
Ensuring AI works with the right dose of curiosity
Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.
A whole new world of learning via MIT OpenCourseWare videos
“I get the chance to not only watch the future happen, but I can actually be a part of it and create it,” says Ugandan entrepreneur Emmanuel Kasigazi.
Video on the record
MIT’s inaugural Bearing Witness, Seeking Justice conference explores video’s role in the struggle over truth and civil liberties.
In machine learning, synthetic data can offer real performance improvements
Models trained on synthetic data can be more accurate than other models in some cases, which could eliminate some privacy, copyright, and ethical concerns from using real data.
Study urges caution when comparing neural networks to the brain
Computing systems that appear to generate brain-like activity may be the result of researchers guiding them to a specific outcome.
Machine learning facilitates “turbulence tracking” in fusion reactors
A new approach sheds light on the behavior of turbulent structures that can affect the energy generated during fusion reactions, with implications for reactor design.
Using sound to model the world
This machine-learning system can simulate how a listener would hear a sound from any point in a room.
3 Questions: How AI image generators could help robots
Yilun Du, a PhD student and MIT CSAIL affiliate, discusses the potential applications of generative art beyond the explosion of images that put the web into creative hysterics.