Computer Science and Artificial Intelligence Laboratory (CSAIL)

For years, researchers from MIT and Brown University have been developing an interactive system that lets users drag-and-drop and manipulate data on any touchscreen, including smartphones and interactive whiteboards. Now, they’ve included a tool that instantly and automatically generates machine-learning models to run prediction tasks on that data.

Drag-and-drop data analytics

System lets nonspecialists use machine-learning models to make predictions for medical research, sales, and more.

June 27, 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

MIT researchers have developed a system that gleans far more labeled training data from unlabeled data, which could help machine-learning models better detect structural patterns in brain scans associated with neurological diseases. The system learns structural and appearance variations in unlabeled scans, and uses that information to shape and mold one labeled scan into thousands of new, distinct...

From one brain scan, more information for medical artificial intelligence

System helps machine-learning models glean training information for diagnosing and treating brain conditions.

June 19, 2019

“Fiat Cryptography,” a system developed by MIT researchers, automatically generates — and simultaneously verifies — cryptographic algorithms optimized across all hardware platforms. Algorithms generated by the system are already behind most of the secure links opened in Google Chrome.

Automated cryptocode generator is helping secure the web

System automatically writes optimized algorithms to encrypt data in Google Chrome browsers and web applications.

June 17, 2019

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.