Machine learning

New research describes a single neural network that can collect missing data from a patient and add it to their chart.

Filling the gaps in a patient’s medical data

Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information.

January 25, 2019

A model by MIT and Microsoft researchers identifies instances where autonomous systems have “learned” from training examples that don’t match what’s actually happening in the real world, which can be used to identify which learned actions could cause costly errors.

Identifying artificial intelligence “blind spots”

Model identifies instances when autonomous systems have learned from examples that may cause dangerous errors in the real world.

January 24, 2019

3Q: Judah Cohen on improving seasonal weather forecasting

Machine learning could help improve the accuracy of long-term forecasts, MIT climatologist argues.

January 22, 2019

Professor Antonio Torralba, MIT director of the MIT—IBM Watson AI Lab and the inaugural director of the MIT Quest for Intelligence, addresses the audience at the MIT AI Policy Congress on Jan. 15.

AI, the law, and our future

MIT “Policy Congress” examines the complex terrain of artificial intelligence regulation.

January 18, 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

Vinod Vaikuntanathan is using number theory and other mathematical concepts to fortify encryption so it can be used for new applications and stand up to even the toughest adversaries.

Fortifying the future of cryptography

Vinod Vaikuntanathan aims to improve encryption in a world with growing applications and evolving adversaries.

January 16, 2019

A new doctoral field offering a computational specialty within MIT's Department of Earth, Atmospheric and Planetary Sciences resonates with the Institute’s growing awareness of the advantages provided by education based in computer science and engineering.

MIT adds computational Earth, atmospheric, and planetary sciences to its PhD offerings

New EAPS thesis field is the most recent to join the computational science and engineering doctoral program within the Center for Computational Engineering.

January 9, 2019

First row, left to right: Suvrit Sra, Frances Ross, Pulkit Agrawal, Kevin O’Brien, Richard Linares, and Cathy Wu. Second row, left to right: Negar Reiskarimian, Jacob Andreas, Giovanni Traverso, Manya Ghobadi, and Ashwin Gopinath.

School of Engineering welcomes new faculty

Eleven new professors join the MIT community.

January 9, 2019

Building site identified for MIT Stephen A. Schwarzman College of Computing

Headquarters would replace Building 44, forming an “entrance to computing” near the intersection of Vassar and Main streets.

December 19, 2018

Aleksander Madry is a leader in the emerging field of building guarantees into artificial intelligence, which has nearly become a branch of machine learning in its own right.

3Q: Aleksander Madry on building trustworthy artificial intelligence

A recent MIT symposium explores methods for making artificial intelligence systems more reliable, secure, and transparent.

December 14, 2018

Yoel Fink, CEO of AFFOA, holds up a fabric with optical communications capabilities during a presentation on what he calls “fabrics as a service” at the annual MIT Industrial Liaison Program (ILP) Research and Development Conference on Nov. 15.

Opportunities for materials innovation abound

Faculty researchers share insights into new capabilities at the annual Industrial Liaison Program Research and Development Conference.

December 14, 2018

From an original transparent etching (far right), engineers produced a photograph in the dark (top left), then attempted to reconstruct the object using first a physics-based algorithm (top right), then a trained neural network (bottom left), before combining both the neural network with the physics-based algorithm to produce the clearest, most accurate reproduction (bottom right) of the original ...

Deep-learning technique reveals “invisible” objects in the dark

Method could illuminate features of biological tissues in low-exposure images.

December 12, 2018

The RePaint system reproduces paintings by combining two approaches called color-contoning and halftoning, as well as a deep learning model focused on determining how to stack 10 different inks to recreate the specific shades of color.

Reproducing paintings that make an impression

CSAIL's new RePaint system aims to faithfully recreate your favorite paintings using deep learning and 3-D printing.

November 29, 2018

At a recent meeting, the MIT AI Ethics Reading Group debated options for teaching ethics in a traditional computer science curriculum. Postdoc Abby Jaques (left) is developing ethics modules that can be incorporated into existing coursework.

Student group explores the ethical dimensions of artificial intelligence

MIT AI Ethics Reading Group was founded by students who saw firsthand how technology developed with good intentions could be problematic.

November 27, 2018

Jessy Lin, an MIT senior double-majoring in electrical engineering and computer science and in philosophy.

I think, therefore I code

Senior Jessy Lin, a double major in EECS and philosophy, is programming for social good.

November 15, 2018

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