SceneSmith, a new system developed at MIT uses three agents to piece together the objects, walls, and overall look of a 3D scene. Its realistic recreations of indoor spaces help robots practice skills and try out different ways of doing tasks before they’re powered on. 

Most people think of the waterfront as the edge of the city. A team of MIT researchers sees it as a dynamic, LEGO-like construction site.

An increasingly common sight: robots walking down the street, surrounded by astounded onlookers. But these machines aren’t yet the do-it-all assistants you’d want working in a kitchen or factory, and a major bottleneck is data. Much like humans, robots learn best by experience. The challenge is that it’s labor-intensive and time-consuming to physically teach these machines so many actions across different settings. 

Imagine working at a warehouse or office sometime in the near future, and you’re asked to help a new trainee learn the basics of their job. The catch: It’s a robot. To teach them, you might want to play a game of “show and tell” — that is, physically showing how to do something a few different ways, while also explaining what you’re doing.

There’s a delicate art to teaching robots, even when you’re preparing them for predictable environments like factories, where they’ll repeat the same tasks a little differently depending on the obstacles they face. Whether a human is suddenly in their way or there’s new clutter, the machine must closely mimic its operator’s actions by staying on a trajectory (or motion path).

For MIT Professor Armando Solar-Lezama, one of the most common misunderstandings about AI is the notion that it can be dropped into existing human roles like a plug-and-play replacement.

What happens when the team behind PlayStation meets the researchers pushing the boundaries of AI? You get The Nexus of Games and AI, a 12-part MIT Independent Activities Period (IAP) course, now available to stream.

To improve data center efficiency, multiple storage devices are often pooled together over a network so many applications can share them. But even with pooling, significant device capacity remains underutilized due to performance variability across the devices.

Anthropic CEO Dario Amodei has said that AI could surpass “almost all humans at almost everything” shortly after 2027. While AI’s capabilities are certainly improving, such rapid progress might seem at odds with findings that show AI is still failing at 95%+ of remote freelance projects, and continues to struggle with hallucination, long term planning, and forms of abstract reasoning that humans find easy. But recent work from METR has found evidence that LLMs can gain capabilities in rapid surges — jumping from succeeding almost never to almost always in just a few years. If this is true across the economy, it could mean that workers could be blindsided by AI advances.