MIT-IBM Watson AI Lab

A crash course in deep learning organized and taught by grad students Alexander Amini (right) and Ava Soleimany reaches more than 350 MIT students each year; more than a million other people have watched their lectures online over the past three years.

Bringing deep learning to life

MIT duo uses music, videos, and real-world examples to teach students the foundations of artificial intelligence.

February 24, 2020

Several dozen students participated in the Green AI Hackathon, co-sponsored by the MIT Research Computing Project and MIT-IBM Watson AI Lab.

Brainstorming energy-saving hacks on Satori, MIT’s new supercomputer

Three-day hackathon explores methods for making artificial intelligence faster and more sustainable.

February 11, 2020

A PhD student in the MIT Department of Electrical Engineering and Computer Science, Natalie Lao has co-founded startups aimed at democratizing artificial intelligence and using AI to protect democracy by fighting false and misleading information.

Demystifying artificial intelligence

Doctoral candidate Natalie Lao wants to show that anyone can learn to use AI to make a better world.

January 29, 2020

In a new study, researchers at MIT and IBM combine three popular text-analysis tools — topic modeling, word embeddings, and optimal transport — to compare thousands of documents per second. Here, they show that their method (left) clusters newsgroup posts by category more tightly than a competing method.

Finding a good read among billions of choices

As natural language processing techniques improve, suggestions are getting speedier and more relevant.

December 20, 2019

“We believe that AI plays a key role now, and in the future development of the region, if it’s used in the right way,” says Omar Costilla Reyes, one of four MIT graduate students working to help Latin America adopt artificial intelligence technologies. Pictured here (left to right) are Costilla Reyes, Emilia Simison, Pedro Antonio Colon-Hernandez, and Guillermo Bernal.

Students push to speed up artificial intelligence adoption in Latin America

To help the region catch up, students organize summit to bring Latin policymakers and researchers to MIT.

November 19, 2019

A new tool reveals what AI models leave out in recreating a scene. Here, a GAN, or generative adversarial network, has dropped the pair of newlyweds from its reconstruction (right) of the photo it was asked to draw (left).

Visualizing an AI model’s blind spots

New tool highlights what generative models leave out when reconstructing a scene.

November 8, 2019

A new technique for training video recognition models is up to three times faster than current state-of-the-art methods while improving runtime performance on mobile devices. The work was recently highlighted by Dario Gil (above), director of IBM Research, at the MIT-IBM Watson AI Lab’s AI Research Week in Cambridge, Massachusetts.

Faster video recognition for the smartphone era

MIT and IBM researchers offer a new method to train and run deep learning models more efficiently.

October 11, 2019

MIT researchers are training a pair of generative adversarial networks, or GANs, to mimic the land, sea, and cloud textures seen in satellite images with the goal of eventually visualizing real-world sea-level rise. It’s one of many artificial intelligence research projects made possible by IBM and Google-donated cloud credits.

What a little more computing power can do

Commercial cloud service providers give artificial intelligence computing at MIT a boost.

September 16, 2019

An $11.6 million artificial intelligence computing cluster donated by IBM to MIT will come online this fall at the Massachusetts Green High Performance Computing Center (MGHPCC) in Holyoke, Massachusetts.

IBM gives artificial intelligence computing at MIT a lift

Nearly $12 million machine will let MIT researchers run more ambitious AI models.

August 26, 2019

MIT graduate students Logan Engstrom (left) and Andrew Ilyas have published five papers together exploring the vulnerabilities of computer vision systems, and they have half a dozen more in the pipeline.

Why did my classifier just mistake a turtle for a rifle?

Two longtime friends explore how computer vision systems go awry.

July 31, 2019

A course called Machine Learning for Healthcare introduces students to the technical and ethical challenges of using automated tools to diagnose and treat patients.

Want to know what software-driven health care looks like? This class offers some clues.

A course that combines machine learning and health care explores the promise of applying artificial intelligence to medicine.

July 24, 2019

The GANpaint system developed at MIT can easily add features to an existing image. At left, the original photo of a kitchen; at right, the same kitchen with the addition of a window. Co-author Jun-Yan Zhu believes better understanding of GANs will help researchers be able to better stamp out fakery: “This understanding may potentially help us detect fake images more easily.”

Teaching artificial intelligence to create visuals with more common sense

An MIT/IBM system could help artists and designers make quick tweaks to visuals while also helping researchers identify “fake” images.

July 1, 2019

Users feed Gen relatively short code defining a target task, and the system automatically generates the results.

New AI programming language goes beyond deep learning

General-purpose language works for computer vision, robotics, statistics, and more.

June 26, 2019

Researchers have developed a flexible way of combining deep learning and symbolic reasoning to teach computers to write short computer programs. Here, Armando Solar-Lezama (left), a professor at CSAIL, speaks with graduate student Maxwell Nye.

Toward artificial intelligence that learns to write code

Researchers combine deep learning and symbolic reasoning for a more flexible way of teaching computers to program.

June 14, 2019

Phillip Isola is an expert in generative adversarial networks, a form of deep learning that is changing how we look at images and inspiring applications far beyond computer vision. Isola helped organize GANocracy, a day-long workshop at MIT on the art and science of GANs.

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.

May 30, 2019

MIT News | Massachusetts Institute of Technology

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