Computer graphics and geometry processing research provide the tools needed to simulate physical phenomena like fire and flames, aiding the creation of visual effects in video games and movies as well as the fabrication of complex geometric shapes using tools like 3D printing.

On a research cruise around Hawaii in 2018, Yuening Zhang SM ’19, PhD ’24 saw how difficult it was to keep a tight ship. The careful coordination required to map underwater terrain could sometimes led to a stressful environment for team members, who might have different understandings of which tasks must be completed in spontaneously changing conditions. During these trips, Zhang considered how a robotic companion could have helped her and her crewmates achieve their goals more efficiently.

As artificial intelligence agents become more advanced, it could become increasingly difficult to distinguish between AI-powered users and real humans on the internet. In a new white paper, researchers from MIT, OpenAI, Microsoft, and other tech companies and academic institutions propose the use of personhood credentials, a verification technique that enables someone to prove they are a real human online, while preserving their privacy.

The phrase “practice makes perfect” is usually reserved for humans, but it’s also a great maxim for robots newly deployed in unfamiliar environments.

At the top of many automation wish lists is a particularly time-consuming task: chores.

As a child, I often accompanied my mother to the grocery store. As she pulled out her card to pay, I heard the same phrase like clockwork: "Go bag the groceries." It was not my favorite task.

You’ve likely heard that a picture is worth a thousand words, but can a large language model (LLM) get the picture if it’s never seen images before?

Large language models like those that power ChatGPT have shown impressive performance on tasks like drafting legal briefs, analyzing the sentiment of customer reviews, or translating documents into different languages.

Mark Hamilton, an MIT PhD student in electrical engineering and computer science and affiliate of MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), wants to use machines to understand how animals communicate. To do that, he set out first to create a system that can learn human language “from scratch.”

When robots come across unfamiliar objects, they struggle to account for a simple truth: Appearances aren’t everything. They may attempt to grasp a block, only to find out it’s a literal piece of cake. The misleading appearance of that object could lead the robot to miscalculate physical properties like the object’s weight and center of mass, using the wrong grasp and applying more force than needed.