In sports training, practice is the key, but being able to emulate the techniques of professional athletes can take a player’s performance to the next level. AI-based personalized sports coaching assistants assist with this by utilizing published datasets. With cameras and sensors strategically placed on the athlete's body, these systems can track everything, including joint movement patterns, muscle activation levels, and gaze movements.

The internet is awash in instructional videos that can teach curious viewers everything from cooking the perfect pancake to performing a life-saving Heimlich maneuver.

Researchers from the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) and Google Research may have just performed digital sorcery — in the form of a diffusion model that can change the material properties of objects in images.

Daniela Rus’s dream is to imbue the power of robotics with the wisdom of humanity. She runs MIT’s Computer Science and Artificial Intelligence Laboratory. As part of his ongoing series on the promise and perils of AI, Globe Ideas Editor Brian Bergstein talks to Rus about her new book “The Heart and the Chip.” She says robots won’t just do our chores and work in our factories; they can teach us how to hit tennis balls like Serena Williams and defy gravity like Iron Man. She says your car won’t just drive you around — it might also be a friend. 

Most people agree that the spread of online misinformation is a serious problem. But there is much less consensus on what to do about it.

CSAIL Director & Liquid  AI Co-Founder Professor Daniela Rus has been named to the third annual Tech Power Players 50, a list of the most influential – and interesting – people in the New England technology scene, as ranked by The Boston Globe’s business journalists and an external advisory.

On Vassar Street, in the heart of MIT’s campus, the MIT Stephen A. Schwarzman College of Computing recently opened the doors to its new headquarters in Building 45. The building’s central location and welcoming design will help form a new cluster of connectivity at MIT and enable the space to have a multifaceted role. 

A user could ask ChatGPT to write a computer program or summarize an article, and the AI chatbot would likely be able to generate useful code or write a cogent synopsis. However, someone could also ask for instructions to build a bomb, and the chatbot might be able to provide those, too.

In our current age of artificial intelligence, computers can generate their own “art” by way of diffusion models, iteratively adding structure to a noisy initial state until a clear image or video emerges. Diffusion models have suddenly grabbed a seat at everyone’s table: Enter a few words and experience instantaneous, dopamine-spiking dreamscapes at the intersection of reality and fantasy. Behind the scenes, it involves a complex, time-intensive process requiring numerous iterations for the algorithm to perfect the image.

Imagine yourself glancing at a busy street for a few moments, then trying to sketch the scene you saw from memory. Most people could draw the rough positions of the major objects like cars, people, and crosswalks, but almost no one can draw every detail with pixel-perfect accuracy. The same is true for most modern computer vision algorithms: They are fantastic at capturing high-level details of a scene, but they lose fine-grained details as they process information.