Quest for Intelligence

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

The MIT Machine Intelligence Community board and executive committee

Want to learn how to train an artificial intelligence model? Ask a friend.

MIT Machine Intelligence Community introduces students to nuts and bolts of machine learning.

June 25, 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

In a pair of studies, researchers show that grammar-enriched deep learning models understand some key rules about language use. Peng Qian (left) and Ethan Wilcox, graduate students at MIT and Harvard University respectively, presented the work at a recent MIT-IBM Watson AI Lab poster session.

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.

May 29, 2019

Students participating in Quest for Intelligence-funded UROP projects this spring include: (clockwise from top left) Amanda Li, Maya Nigrin, Yuria Utsumi, Kika Arias, Marco Fleming, and Shreya Pandit.

Developing artificial intelligence tools for all

MIT Quest for Intelligence-sponsored undergraduate research projects demystify AI.

May 23, 2019

J-Clinic names 18 grant recipients from across Institute

Projects will develop new AI technologies that detect and prevent diseases.

May 1, 2019

MIT and Liberty Mutual Insurance announced a $25 million, five-year collaboration to support intelligence research at a meeting on Tuesday attended by Liberty Mutual Chairman and CEO David Long (left) and MIT President L. Rafael Reif.

Liberty Mutual Insurance joins MIT's Quest for Intelligence

Company announces $25 million, five-year collaboration.

April 30, 2019

MIT researchers have developed an efficient algorithm that could provide a “push-button” solution for automatically designing fast-running neural networks on specific hardware.

Kicking neural network design automation into high gear

Algorithm designs optimized machine-learning models up to 200 times faster than traditional methods.

March 21, 2019

MIT News | Massachusetts Institute of Technology

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