MIT Quest for Intelligence

Students participating in MIT Quest for Intelligence-funded UROP projects include: (clockwise from top left) Nicole Thumma, Joanne Yuan, Julian Viera, Andrei Dumitrescu, Pablo Alejo-Aguirre, and Dolapo Adedokun.

Bringing artificial intelligence into the classroom, research lab, and beyond

Through the Undergraduate Research Opportunities Program, students work to build AI tools with impact.

February 13, 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

MIT Schwarzman College of Computing leadership team (left to right) David Kaiser, Daniela Rus, Dan Huttenlocher, Julie Shah, and Asu Ozdaglar

A college for the computing age

With the initial organizational structure in place, the MIT Schwarzman College of Computing moves forward with implementation.

February 4, 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

MIT researchers piloted a new curriculum to teach middle school students about ethics and artificial intelligence at the second annual Mass STEM Week, a statewide event to encourage more young people to explore science, technology, engineering, and math studies and careers.

Bringing artificial intelligence and MIT to middle school classrooms

MIT researchers and collaborators have developed an open-source curriculum to teach young students about ethics and artificial intelligence.

December 30, 2019

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

ObjectNet, a dataset of photos created by MIT and IBM researchers, shows objects from odd angles, in multiple orientations, and against varied backgrounds to better represent the complexity of 3D objects. The researchers hope the dataset will lead to new computer vision techniques that perform better in real life.

This object-recognition dataset stumped the world’s best computer vision models

Objects are posed in varied positions and shot at odd angles to spur new AI techniques.

December 10, 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

In a study using machine-generated art to understand what makes a picture memorable, researchers found that the images people remembered best had bright colors, simple backgrounds, and subjects centered prominently in the frame.

What makes an image memorable? Ask a computer

An artificial intelligence model developed at MIT shows in striking detail what makes some images stick in our minds.

November 1, 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

MIT News | Massachusetts Institute of Technology

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