Imagine a slime-like robot that can seamlessly change its shape to squeeze through narrow spaces, which could be deployed inside the human body to remove an unwanted item.
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.
The allure of whales has stoked human consciousness for millennia, casting these ocean giants as enigmatic residents of the deep seas. From the biblical Leviathan to Herman Melville's formidable Moby Dick, whales have been central to mythologies and folklore. And while cetology, or whale science, has improved our knowledge of these marine mammals in the past century in particular, studying whales has remained a formidable a challenge.
We are developing tools to enable physically-embodied, spatial AI as a "technological superpower" for Robots and Humans. Semantic simultaneous localization and mapping can enable long-lived autonomous systems to navigate in complex dynamic environments. Our dream is to create mobile robots that can build and maintain models of the world through lifelong learning, improving their performance over time, and helping humans to perform difficult tasks.
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.
For nearly a decade, a team of MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have been seeking to uncover why certain images persist in a people's minds, while many others fade. To do this, they set out to map the spatio-temporal brain dynamics involved in recognizing a visual image. And now for the first time, scientists harnessed the combined strengths of magnetoencephalography (MEG), which captures the timing of brain activity, and functional magnetic resonance imaging (fMRI), which identifies active brain regions, to precisely determine when and where the brain processes a memorable image.
To build AI systems that can collaborate effectively with humans, it helps to have a good model of human behavior to start with. But humans tend to behave suboptimally when making decisions.
Daniela Rus is a pioneering roboticist and a professor of electrical engineering and computer science at MIT. She is the director of the Computer Science and Artificial Intelligence Laboratory. She is also a member of the National Academy of Engineering, the American Academy of Arts and Sciences, and a MacArthur Fellow.
