Cracking open the black box of automated machine learning
Interactive tool lets users see and control how automated model searches work.
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Q&A: Phillip Isola on the art and science of generative models
Image-translation pioneer discusses the past, present, and future of generative adversarial networks, or GANs.
Teaching language models grammar really does make them smarter
Researchers submit deep learning models to a set of psychology tests to see which ones grasp key linguistic rules.
Sensor-packed glove learns signatures of the human grasp
Signals help neural network identify objects by touch; system could aid robotics and prosthetics design.
3 Questions: The social implications and responsibilities of computing
In helping envision the MIT Schwarzman College of Computing, working group is focusing on ethical and societal questions.
3 Questions: Faculty appointments and the MIT Schwarzman College of Computing
Working group studies options for creating a new set of faculty hires for MIT’s new college.
Developing artificial intelligence tools for all
MIT Quest for Intelligence-sponsored undergraduate research projects demystify AI.
Fabrics poised to become the new software
Basic research advance leads to production of more than 250,000 chips embedded within fibers in less than a year.
MIT Policy Hackathon connects data-driven problem solvers
Interdisciplinary teams propose creative policy solutions addressing societal challenges at the Institute for Data, Systems, and Society student-organized event.
MIT and U.S. Air Force sign agreement to launch AI Accelerator
New program will focus on rapid deployment of artificial intelligence innovations in operations, disaster response, and medical readiness.
Artificial intelligence shines light on the dark web
New tools can find patterns in vast online data to track and identify users on illicit forums.
Using AI to predict breast cancer and personalize care
MIT/MGH's image-based deep learning model can predict breast cancer up to five years in advance.
Smarter training of neural networks
MIT CSAIL project shows the neural nets we typically train contain smaller “subnetworks” that can learn just as well, and often faster.
School of Engineering first quarter 2019 awards
Faculty members recognized for excellence via a diverse array of honors, grants, and prizes over the last quarter.
Putting vision models to the test
Study shows that artificial neural networks can be used to drive brain activity.