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

As artificial intelligence models become increasingly prevalent and are integrated into diverse sectors like health care, finance, education, transportation, and entertainment, understanding how they work under the hood is critical. Interpreting the mechanisms underlying AI models enables us to audit them for safety and biases, with the potential to deepen our understanding of the science behind intelligence itself.

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?

Someday, you may want your home robot to carry a load of dirty clothes downstairs and deposit them in the washing machine in the far-left corner of the basement. The robot will need to combine your instructions with its visual observations to determine the steps it should take to complete this task.

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.”

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.

Large language models (LLMs) are becoming increasingly useful for programming and robotics tasks, but for more complicated reasoning problems, the gap between these systems and humans looms large. Without the ability to learn new concepts like humans do, these systems fail to form good abstractions — essentially, high-level representations of complex concepts that skip less-important details — and thus sputter when asked to do more sophisticated tasks.