Researchers created a new technique that accurately and rapidly matches X-rays captured during surgery with a patient’s preoperative 3D medical scan. This method could make it easier for clinicians to precisely pilot minimally invasive surgical tools, leading to faster and safer procedures.

As a child, you likely saw a few Disney movies depicting inanimate objects, such as clocks, cups, and toys, as interactive companions to humans — an act of pure magic, seemingly. But scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are now doing something similar: transforming stationary items into self-aware tools that perceive and respond to human motion to complete a task.

“What you see is what you get” is a guiding principle for many software engineers — create programs where the content you’re editing looks the same as the final product. But when you’re using generative AI (genAI) systems to 3D print, say, a mug, you’ll likely get a cup that can’t hold your coffee. Why is that?

Self-driving cars are often controlled by deep learning models that sometimes fail in unexpected situations. For instance, the car might inexplicably brake and block the path of an oncoming emergency vehicle. A human driver or passenger may need to react rapidly to prevent a collision.

A region of the brain called the striatum is critical for many cognitive and motor functions, including decision-making, control of movement, habit formation, and processing of reward. It also plays a role in addiction and is significantly affected by Huntington’s disease, schizophrenia, and other disorders.

It all started with some exasperated emails. Back in 2009, a group of researchers began venting their frustration with the programming languages designed to help scientists and other researchers perform complex mathematical operations and statistical simulations without learning how to code. These programming languages were rigid and slow. If scientists built something that really worked, they’d need to rewrite the entire program in another language just to run it more quickly.

When an AI image generator produces a portrait, whose work went into it? The question sits at the center of lawsuits, licensing deals, and proposed regulations worldwide. Artists want credit. Companies want clarity. Policymakers want a way to assign responsibility.

In the last decade, scientists have shown that fusion energy can work, as a physical process. Next question: Can it work economically? 

Artificial intelligence models are jacks of many trades, including writing, generating images, and creating 3D models. But they aren’t as helpful when it comes to testing robots or designs for vehicles in diverse environments, since they don’t understand physics as well as they do pixels or text.

Modern processors are fast in part because they guess. Rather than waiting to find out which way a program will branch, a chip predicts the likely path and races ahead. When the guess is right, time is saved. When it's wrong, the work is discarded, but traces of it linger, and since the Spectre vulnerability was disclosed in 2018, attackers have known how to read those traces to pull secrets out of memory they should never see.