MIT-IBM Watson AI Lab

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

(L-R) MIT Assistant Professor Michael Carbin and PhD student Jonathan Frankle

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.

May 6, 2019

Three-part illustration: On left is a battery labeled "efficiency"; in center is a robot labeled "defensive quantization"; on right is a lock symbol labeled "security."

Improving security as artificial intelligence moves to smartphones

Researchers unveil a tool for making compressed deep learning models less vulnerable to attack.

April 23, 2019

A new “particle simulator” developed by MIT researchers improves robots’ abilities to mold materials into simulated target shapes and interact with solid objects and liquids. This could give robots a refined touch for industrial applications or for personal robotics— such as shaping clay or rolling sticky sushi rice.

Giving robots a better feel for object manipulation

Model improves a robot’s ability to mold materials into shapes and interact with liquids and solid objects.

April 16, 2019

$Researchers trained a hybrid AI model to answer questions like “Does the red object left of the green cube have the same shape as the purple matte thing?” by feeding it examples of object colors and shapes followed by more complex scenarios involving multi-object comparisons. The model could transfer this knowledge to new scenarios as well as or better than state-of-the-art models using a frac...$

Teaching machines to reason about what they see

Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency.

April 2, 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

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

Aude Oliva (right), a principal research scientist at the Computer Science and Artificial Intelligence Laboratory and Dan Gutfreund (left), a principal investigator at the MIT–IBM Watson AI Laboratory and a staff member at IBM Research, are the principal investigators for the Moments in Time Dataset, one of the projects related to AI algorithms funded by the MIT–IBM Watson AI Laboratory.

Artificial intelligence in action

At the MIT-IBM Watson AI Lab, researchers are training computers to recognize dynamic events.

April 4, 2018

Anantha Chandrakasan, dean of MIT’s School of Engineering, who negotiated the collaboration on behalf of MIT, speaks during the signing of the agreement with representatives of IBM.

3Q: Anantha Chandrakasan on new MIT–IBM Watson AI Lab

Lab seeks to expand the boundaries of research on artificial intelligence.

September 7, 2017

MIT News | Massachusetts Institute of Technology

Browse By

Topics

Departments

Centers, Labs, & Programs

Schools