Computer Science and Artificial Intelligence Laboratory (CSAIL)

A new MIT-developed model automates a critical step in using AI for medical decision making, where experts usually identify important features in massive patient datasets by hand. The model was able to automatically identify voicing patterns of people with vocal cord nodules (shown here) and, in turn, use those features to predict which people do and don’t have the disorder.

Automating artificial intelligence for medical decision-making

Model replaces the laborious process of annotating massive patient datasets by hand.

August 5, 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

Prime Minister Mark Rutte of the Netherlands, second from right, front, along with a large Dutch delegation, visits MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Ray and Maria Stata Center on July 19. MIT Professor Frans Kaashoek of CSAIL is second from the left.

Prime Minister Mark Rutte of the Netherlands tours MIT

Dutch delegation visits the Institute for a tour focused on computing, robotics, and health care innovation.

July 21, 2019

“I hope to save a lot of people a lot of time doing boring, repetitive work by automating programming work as well as decreasing the cost of building secure, reliable systems,” says Associate Professor Adam Chlipala.

Making it easier to program and protect the web

Professor Adam Chlipala builds tools to help programmers more quickly generate optimized, secure code.

July 20, 2019

A devoted teacher and cherished colleague, Patrick Winston led CSAIL’s Genesis Group, which focused on developing AI systems that have human-like intelligence, including the ability to tell, perceive, and comprehend stories.

Professor Patrick Winston, former director of MIT’s Artificial Intelligence Laboratory, dies at 76

Beloved professor conducted pioneering research on imbuing machines with human-like intelligence, including the ability to understand stories.

July 19, 2019

Fernando "Corby" Corbató led the development of early “time-sharing” operating systems, including Multics and the Compatible Time-Sharing System (CTSS).

Professor Emeritus Fernando Corbató, MIT computing pioneer, dies at 93

Longtime MIT professor developed early “time-sharing” operating systems and is widely credited as the creator of the world’s first computer password.

July 15, 2019

A new MIT-invented system automatically designs and 3-D prints complex robotic actuators optimized according to an enormous number of specifications, such as appearance and flexibility. To demonstrate the system, the researchers fabricated floating water lilies with petals equipped with arrays of actuators and hinges that fold up in response to magnetic fields run through conductive fluids.

Automated system generates robotic parts for novel tasks

When designing actuators involves too many variables for humans to test by hand, this system can step in.

July 12, 2019

Clockwise from top left: William Detmold, Semyon Dyatlov, Mary Gehring, David McGee, Ankur Moitra, Matthew Shoulders, Tracy Slatyer, and Michael Williams.

Meet the 2019 tenured professors in the School of Science

Eight faculty members are granted tenure in five science departments.

July 10, 2019

Dean of Digital Learning Krishna Rajagopal (center) with winners of the MITx Prize for Teaching and Learning in MOOCs, Jessica Sandland (left) and Martin Bazant. Not pictured: Polina Anikeeva, who also received the MITx Prize, and John Belcher, Amy Carleton, Jared Curhan, and Erik Demaine, who received Teaching with Digital Technology Awards.

Seven MIT educators honored for digital learning innovation

Educators recognized for improving classroom instruction and student engagement through innovative uses of digital technology.

July 2, 2019

MIT professors Dina Katabi (left) and Angelika Amon are among the 2019 Carnegie Corporation "Great Immigrants, Great Americans."

Angelika Amon and Dina Katabi named Carnegie Corporation “Great Immigrants”

MIT biologist and electrical engineer are two of 38 naturalized U.S. citizens honored for contributions to American society.

July 2, 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

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

MIT News | Massachusetts Institute of Technology

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