Artificial intelligence

Prof. Daniela Rus, director of CSAIL, emphasizes the central role universities play in fostering innovation and the importance of ensuring universities have the computing resources necessary to help tackle major global challenges. Rus writes, “academia needs a large-scale research cloud that allows researchers to efficiently share resources” to address hot-button issues like generative AI. “It would provide an integrated platform for large-scale data management, encourage collaborative studies across research organizations, and offer access to cutting-edge technologies, while ensuring cost efficiency,” Rus explains.

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.

A study by researchers from MIT and Stanford University explored how customer service agents use generative AI chatbots in their response to tech support questions, reports Meghan McCarty for Marketplace. “The most experienced workers see no to even small negative effects of the AI being turned on for them, while the less-experienced and least-productive workers see on average 30 to 35% improvements in productivity,” explains graduate student Lindsey Raymond.

Forbes reporter Stuart Anderson spotlights a number of international students who became founders of top U.S. AI companies, including MIT alumni Sébastien Boyer MS '16 and Aditya Khosla PhD '16. Boyer co-founded “FarmWise, which employs AI for precision weeding on farms,” and Khosla co-founded PathAI, a biotech startup that uses AI to “optimize the analysis of patient tissue samples and for other clinical and diagnostic purposes,” writes Anderson.

Prof. Regina Barzilay speaks with Nicole Estephan of WCVB-TV’s Chronicle about her work developing new AI systems that could be used to help diagnose breast and lung cancer before the cancers are detectable to the human eye.

In conversation with Matthew Huston at Science, Prof. John Horton discusses the possibility of using chatbots in research instead of humans. As he explains, a change like that would be similar to the transition from in-person to online surveys, “"People were like, ‘How can you run experiments online? Who are these people?’ And now it’s like, ‘Oh, yeah, of course you do that.’”

Researchers from MIT have found that using generative AI chatbots can improve the speed and quality of simple writing tasks, but often lack factual accuracy, reports Richard Nieva for Forbes. “When we first started playing with ChatGPT, it was clear that it was a new breakthrough unlike anything we've seen before,” says graduate student Shakked Noy. “And it was pretty clear that it was going to have some kind of labor market impact.”

Prof. Daron Acemoglu and graduate student Todd Lensman have created “the first economic model of how to regulate transformative technologies,” like artificial intelligence, reports Tim Fernholz for Quartz. “Their tentative conclusion is that slower deployments is likely better, and that a machine learning tax combined with sector-specific restrictions on the use of the technology could provide the best possible outcomes,” writes Fernholz.

Lecturer Guadalupe Hayes-Mota SB '08, MS '16, MBA '16 writes for Forbes about the ethical framework needed to mitigate risks in artificial intelligence. “[A]s we continue to unlock AI's capabilities, it is crucial to address the ethical challenges that emerge,” writes Hayes-Mota. “By establishing a comprehensive ethical framework grounded in beneficence, non-maleficence, autonomy, justice and responsibility, we can ensure that AI's deployment in life sciences aligns with humanity's best interests.”

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.”

Prof. Max Tegmark speaks with Guardian reporter Steve Rose about the potential of artificial intelligence. “The positive, optimistic scenario is that we responsibly develop superintelligence in a way that allows us to control it and benefit from it,” says Tegmark. “If we can build and control superintelligence, we can quickly go from being limited by our own stupidity to being limited by the laws of physics. It could be the greatest empowerment moment in human history.”

Former MIT Prof. Edward Fredkin, “a pioneer in artificial intelligence and a maverick theorist,” has died at 88, reports Alex Williams for The New York Times. Williams notes that Fredkin, who worked on Project MAC during his time at MIT, was “fueled by a seemingly limitless scientific imagination and a blithe indifference to conventional thinking.” Prof. Gerald Sussman recalls that “Ed Fredkin had more ideas per day than most people have in a month.”

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.”