MIT Schwarzman College of Computing

Writing for Forbes, research affiliate John Werner spotlights Prof. Stefanie Mueller’s presentation at the CSAIL Imagination in Action event on her work developing a new type of paint that allows users to change the color and pattern of different objects. “The long-term vision here, really, is to give those physical objects the same capabilities as we have in digital,” said Mueller. “I hope in the future we will all get some free stuff, and we would just have an [app] where we can download different textures we can apply, and change our outfits.”

During her talk at CSAIL’s Imagination in Action event, Prof. Daniela Rus, director of CSAIL, explored the promise of using liquid neural networks “to solve some of AI’s notorious complexity problems,” writes research affiliate John Werner for Forbes. “Liquid networks are a new model for machine learning,” said Rus. “They're compact, interpretable and causal. And they have shown great promise in generalization under heavy distribution shifts.”

In an article for Forbes, research affiliate John Werner spotlights Prof. Dina Katabi and her work showcasing how AI can boost the capabilities of clinical data. “We are going to collect data, clinical data from patients continuously in their homes, track the symptoms, the evolution of those symptoms, and process this data with machine learning so that we can get insights before problems occur,” says Katabi.

Researchers from MIT have found that since the Covid-19 pandemic, there has been a decrease in the number of interactions between people from different socioeconomic backgrounds, reports Danny Westneat for The Seattle Times. “It’s creating an urban fabric that is actually more brittle, in the sense that we are less exposed to other people,” says research scientist Esteban Moro. “We don’t get to know other people in the city … to know other people’s needs. If we don’t see them around the city, that will be impossible.”

Writing for Forbes, Prof. Daniela Rus, director of CSAIL, makes the case that liquid neural networks “offer an elegant and efficient computational framework for training and inference in machine learning. With their compactness, adaptability, and streamlined computation, these networks have the potential to reshape the landscape of artificial intelligence and drive further breakthroughs in the field.”

MIT has been selected as the world’s best university in the 2024 QS World University Rankings, reports Cecilia Rodriguez for Forbes. MIT has secured “the top position for the 12^th consecutive year,” writes Rodriguez.

MIT Schwarzman College of Computing Dean Daniel Huttenlocher discusses how artificial intelligence has impacted print media at the Aspen Ideas Festival, reports John Frank for Axios. “Most of us grew up in a world where the word print was something that was authoritative,” says Huttenlocher, of how people will need to be on the lookout for misinformation.

MIT Schwarzman College of Computing Dean Daniel Huttenlocher speaks at the Aspen Ideas Festival on how to regulate AI while maximizing its positive impact, reports NBC. “I think when we think about regulation [of artificial intelligence] we need to think about this in the ways we’ve traditionally thought about things – risk, reward, tradeoffs – and that tends to be domain specific,” says Huttenlocher. “It’s hard to have sort of an abstract notion of this new technology and what the risk [and] reward is across all domains.”

Prof. Marzyeh Ghassemi speaks with Yahoo News reporter Rebecca Corey about the benefits and risks posed by the use of AI tools in health care. “I think the problem is when you try to naively replace humans with AI in health care settings, you get really poor results,” says Ghassemi. “You should be looking at it as an augmentation tool, not as a replacement tool.”

Researchers at MIT have developed a new artificial intelligence system aimed at helping autopilot avoid obstacles while maintaining a desirable flight path, reports Kyle Wiggers for TechCrunch. “Any old algorithm can propose wild changes to direction in order to not crash, but doing so while maintaining stability and not pulping anything inside is harder,” writes Wiggers.

New York Times reporter Natasha Singer spotlights the Day of AI, an MIT RAISE program aimed at teaching K-12 students about AI. “Because AI is such a powerful new technology, in order for it to work well in society, it really needs some rules,” said MIT President Sally Kornbluth. Prof. Cynthia Breazeal, MIT’s dean of digital learning, added: “We want students to be informed, responsible users and informed, responsible designers of these technologies.”

Neil Thompson, director of the FutureTech research project at MIT CSAIL and a principal investigator MIT’s Initiative on the Digital Economy, speaks with Politico reporter Mohar Chatterjee about generative AI, the pace of computer progress and the need for the U.S. to invest more in developing the future of computing. “We need to make sure we have good secure factories that can produce cutting-edge semiconductors,” says Thompson. “The CHIPS Act covers that. And people are starting to invest in some of these post-CMOS technologies — but it just needs to be much more. These are incredibly important technologies.”

Institute Prof. Daron Acemoglu and Prof. Aleksander Mądry join GBH’s Greater Boston to explore how AI can be regulated and safely integrated into our lives. “With much of our society driven by informational spaces — in particular social media and online media in general — AI and, in particular, generative AI accelerates a lot of problems like misinformation, spam, spear phishing and blackmail,” Mądry explains. Acemoglu adds that he feels AI reforms should be approached “more broadly so that AI researchers actually work in using these technologies in human-friendly ways, trying to make humans more empowered and more productive.”

MIT researchers have developed a new method to make chatbots more factual, reports Gerrit De Vynck for The Washington Post. “The researchers proposed using different chatbots to produce multiple answers to the same question and then letting them debate each other until one answer won out,” explains Vynck. “The researchers found using this ‘society of minds’ method made them more factual.”  

MIT researchers have developed a new machine-learning technique that can identify which pixels in an image represent the same material, which could help with robotic scene understanding, reports Kyle Wiggers for TechCrunch. “Since an object can be multiple materials as well as colors and other visual aspects, this is a pretty subtle distinction but also an intuitive one,” writes Wiggers.