Computer Science and Artificial Intelligence Laboratory (CSAIL)

Yunzhu Li is a PhD student at the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL).

Teaching artificial intelligence to connect senses like vision and touch

MIT CSAIL system can learn to see by touching and feel by seeing, suggesting future where robots can more easily grasp and recognize objects.

June 17, 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

A new algorithm helps robots predict the paths people take in structured environments like the factory floor, which may further enable close collaboration between humans and machines.

Algorithm tells robots where nearby humans are headed

A new tool for predicting a person’s movement trajectory may help humans and robots work together in close proximity.

June 10, 2019

MIT researchers have given their fleet of autonomous “roboats” the ability to automatically target and clasp onto each other — and keep trying if they fail. The roboats are being designed to transport people, collect trash, and self-assemble into floating structures in the canals of Amsterdam.

Autonomous boats can target and latch onto each other

Fleet of “roboats” could collect garbage or self-assemble into floating structures in Amsterdam’s many canals.

June 5, 2019

Inkbit, a startup out of MIT, has created a multimaterial 3-D printer capable of printing extremely flexible materials more accurately than competitors, according to the founders.

A 3-D printer powered by machine vision and artificial intelligence

MIT startup Inkbit is overcoming traditional constraints to 3-D printing by giving its machines “eyes and brains.”

June 4, 2019

Researchers from MIT and elsewhere have developed an interactive tool that, for the first time, lets users see and control how increasingly popular automated machine-learning (AutoML) systems work.

Cracking open the black box of automated machine learning

Interactive tool lets users see and control how automated model searches work.

May 31, 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 researchers have developed a low-cost, sensor-packed glove that captures pressure signals as humans interact with objects. The glove can be used to create high-resolution tactile datasets that robots can leverage to better identify, weigh, and manipulate objects.

Sensor-packed glove learns signatures of the human grasp

Signals help neural network identify objects by touch; system could aid robotics and prosthetics design.

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

To bring more human-like reasoning to autonomous vehicle navigation, MIT researchers have created a system that enables driverless cars to check a simple map and use visual data to follow routes in new, complex environments.

Bringing human-like reasoning to driverless car navigation

Autonomous control system “learns” to use simple maps and image data to navigate new, complex routes.

May 22, 2019

Lead author Joseph DelPreto demonstrates the system's ability to mirror his movements by monitoring muscle activity.

This robot helps you lift objects — by looking at your biceps

CSAIL system can mirror a user's motions and follow nonverbal commands by monitoring arm muscles.

May 22, 2019

MIT and U.S. Air Force sign agreement to launch AI Accelerator

New program will focus on rapid deployment of artificial intelligence innovations in operations, disaster response, and medical readiness.

May 20, 2019

MIT researchers have developed a computational model that could enhance video game simulations designed to facilitate training for teachers and students who might encounter racial issues in the classroom.

Virtual reality game simulates experiences with race

A novel computational model that considers how users have been conditioned to think about race might facilitate training for teachers and students.

May 16, 2019

Dharavi Tech Girls learn to create apps to help people in their community.

3Q: Hal Abelson on empowering kids through mobile technology

Through MIT App Inventor, Abelson aims to show children how they can use technology to shape their world.

May 15, 2019

Adversarial examples are slightly altered inputs that cause neural networks to make classification mistakes they normally wouldn’t, such as classifying an image of a cat as a dog.

How to tell whether machine-learning systems are robust enough for the real world

New method quickly detects instances when neural networks make mistakes they shouldn’t.

May 10, 2019

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