Machine learning

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

Luis Videgaray PhD ’98, former foreign minister and finance minister of Mexico, is coming to MIT to spearhead an effort that aims to help shape global AI policies, focusing on how such rising technologies will affect people living in all corners of the world.

Visiting lecturer to spearhead project exploring the geopolitics of artificial intelligence

At MIT, Luis Videgaray, alumnus and former foreign minister of Mexico, will launch project to help shape international AI policies.

July 12, 2019

L4DC co-organizer Ali Jadbabaie speaks to a packed room about the future of dynamical and control systems.

IDSS hosts inaugural Learning for Dynamics and Control conference

L4DC explored an emerging scientific area at the intersection of real-time physical data, machine learning, control theory, and optimization.

July 10, 2019

Mingman Zhao, a PhD student in EECS, spoke to the inaugural 6.883/6.S083 class about common issues in using machine learning tools to address problems.

Machine learning for everyone

A new EECS course on applications of machine learning teaches students from a variety of disciplines about one of today’s hottest topics.

July 9, 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

Artist’s impression depicts the conversion of the structure of a protein molecule into a musical passage, as is done in the MIT researchers’ system.

Translating proteins into music, and back

By turning molecular structures into sounds, researchers gain insight into protein structures and create new variations.

June 26, 2019

The MIT Machine Intelligence Community board and executive committee

Want to learn how to train an artificial intelligence model? Ask a friend.

MIT Machine Intelligence Community introduces students to nuts and bolts of machine learning.

June 25, 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

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

$Diagram depicts the sequence of steps used in the new streamlined process to synthesize perovskite-based materials, deposit them on a substrate to create a thin film, and then analyze the film’s characteristics using X-ray diffraction and other observational methods. The new process is about ten times faster than previous methods.$

How to speed up the discovery of new solar cell materials

Streamlined system for creating and analyzing perovskite compounds may cut development time from 20 years to two.

June 5, 2019

Chip design drastically reduces energy needed to compute with light

Simulations suggest photonic chip could run optical neural networks 10 million times more efficiently than its electrical counterparts.