MIT Quest for Intelligence

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

David Pesetsky is the Ferrari P. Ward Professor of Modern Languages and Linguistics at MIT.

3 Questions: What is linguistics?

MIT Professor David Pesetsky describes the science of language and how it sheds light on deep properties of the human mind.

March 27, 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

Students participating in the Undergraduate Research Opportunities Program include: (clockwise from top left) Veronica Boyce, Nicholas Bonaker, Alexandra Dima, Neeraj Prasad, Elizabeth Weeks, and Alexander Denmark.

Exploring the nature of intelligence

Undergraduate research projects show how students are advancing research in human and artificial intelligence, and applying intelligence tools to other disciplines.

February 21, 2019

About 130 undergraduates discussed the progress of their yearlong research projects at during the SuperUROP Showcase poster session.

SuperUROP: Showcasing students' research work in progress

Undergraduate researchers discussed their projects at a well-attended poster session.

January 31, 2019

Using a new cooperative learning technique, MIT-IBM Watson AI Lab researchers cut by half the time it took a pair of robot agents to learn to maneuver to opposite sides of a virtual room.

Learning to teach to speed up learning

An algorithm that teaches robot agents how to exchange advice to complete a task helps them learn faster.

January 29, 2019

Leo Celi, a researcher at MIT and a physician at Beth Israel Deaconess Medical Center, leads a global series of health care hackathons, or datathons, to bring doctors and data scientists together and encourage hospitals to make use of their electronic medical records.

Democratizing artificial intelligence in health care

Hackathons promote doctor-data scientist collaboration and expanded access to electronic medical-records to improve patient care.

January 18, 2019

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