Diffusion models like OpenAI’s DALL-E are becoming increasingly useful in helping brainstorm new designs. Humans can prompt these systems to generate an image, create a video, or refine a blueprint, and come back with ideas they hadn’t considered before.

Imagine a radiologist examining a chest X-ray from a new patient. She notices the patient has swelling in the tissue but does not have an enlarged heart. Looking to speed up diagnosis, she might use a vision-language machine-learning model to search for reports from similar patients.

A human clearing junk out of an attic can often guess the contents of a box simply by picking it up and giving it a shake, without the need to see what’s inside. Researchers from MIT, Amazon Robotics, and the University of British Columbia have taught robots to do something similar.

What would a behind-the-scenes look at a video generated by an artificial intelligence model be like? You might think the process is similar to stop-motion animation, where many images are created and stitched together, but that’s not quite the case for “diffusion models” like OpenAl's SORA and Google's VEO 2.

When the Venice Biennale’s 19th International Architecture Exhibition launches on May 10, its guiding theme will be applying nimble, flexible intelligence to a demanding world — an ongoing focus of its curator, MIT faculty member Carlo Ratti.

The ambiguity in medical imaging can present major challenges for clinicians who are trying to identify disease. For instance, in a chest X-ray, pleural effusion, an abnormal buildup of fluid in the lungs, can look very much like pulmonary infiltrates, which are accumulations of pus or blood.

Fish are masters of coordinated motion. Schools of fish have no leader, yet individuals manage to stay in formation, avoid collisions, and respond with liquid flexibility to changes in their environment. Reproducing this combination of robustness and flexibility has been a long-standing challenge for human engineered systems like robots. Now, using virtual reality for freely-moving fish, a research team based in Konstanz has taken an important step towards that goal.

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel artificial intelligence (AI) model inspired by neural oscillations in the brain, with the goal of significantly advancing how machine learning algorithms handle long sequences of data. 

An estimated 20% of every dollar spent on manufacturing is wasted, totaling up to $8 trillion a year, more than the entire annual budget for the U.S. federal government. While industries like healthcare and finance have been rapidly transformed by digital technologies, manufacturing has relied on traditional processes that lead to costly errors, product delays, and an inefficient use of engineers’ time.

MIT researchers have created a periodic table that shows how more than 20 classical machine-learning algorithms are connected. The new framework sheds light on how scientists could fuse strategies from different methods to improve existing AI models or come up with new ones.