Prof. Daron Acemoglu speaks with Salon reporter Russell Payne to explain how “the calculations made by the current generation of AI are fundamentally different from how humans think.” Acemoglu explains: “The more talk of artificial super intelligence we have, the more of a boost these companies get, especially in terms of being able to raise funding, in terms of being in the spotlight and high status, high ability to convince others.”
A study by researchers at MIT explores “worker attitudes surrounding automation,” reports Sage Lazzaro for Fortune. “A lot of factories and other industrial environments have had data around for a long time and haven’t necessarily known what to do with it,” explains Research Scientist Ben Armstrong. “Now there are new algorithms and new software that’s allowing these companies to be a lot more intelligent with using that data to make work better.”
Guardian reporter Ian Sample highlights how graduate student Alex Kachkine has developed a new approach to restoring age-damaged artwork in hours. “The technique draws on artificial intelligence and other computer tools to create a digital reconstruction of the damaged painting,” explains Sample. “This is then printed on to a transparent polymer sheet that is carefully laid over the work.”
Graduate student Alex Kachkine speaks with Nature reporter Amanda Heidt about his work developing a new restoration method for restoring damaged artwork. The method uses “digital tools to create a ‘mask’ of pigments that can be printed and varnished onto damaged paintings,” explains Heidt. The method “reduces both the cost and time associated with art restoration and could one day give new life to many of the paintings held in institutional collections — perhaps as many as 70% — that remain hidden from public view owing to damage.”
Nature spotlights graduate student Alex Kachkine – an engineer, art collector and art conservator – on his quest to develop a new AI-powered, art restoration method, reports Geoff Marsh for Nature. “My hope is that conservators around the planet will be able to use these techniques to restore paintings that have never been seen by the general public,” says Kachkine. “Many institutions have paintings that arrived at them a century ago, have never been shown because they are so damaged and there are no resources to restore them. And hopefully this technique means we will be able to see more of those publicly.”
Sloan lecturer Mikey Shulman, Colin Angle '89, SM '90, Tye Brady SM '99, Laira Major SM '05, Dharmesh Shah SM '06 have been named to the 2025 Boston Globe Tech Power Players list for their work in the applied AI sector, reports Hiawatha Bray for The Boston Globe.
Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani have been named to The Boston Globe’s 2025 list of Tech Power Players working in the foundational AI sector, reports Aaron Pressman for The Boston Globe. Rus and Hasani are co-founders of Liquid AI, a startup that has developed “an AI technique with fewer software ‘neurons’ than large language models of OpenAI and others,” explains Pressman. This means “Liquid AI requires less computing power (and electricity.)”
MIT researchers have developed Boltz-2, an AI algorithm “that unites protein folding and prediction of small-molecule binding affinity in one package,” reports Laura Howes for Chemical & Engineering News. “The researchers say their new AI model approaches the level of accuracy achieved by traditional computational chemistry—such as methods involving free-energy perturbation calculations—but much more quickly and cheaply,” explains Howes.
A study by researchers at MIT examines how the use of large language models impacts the human brain, reports Elizabeth Hlavinka for Salon. Research scientist Nataliya Kos'myna says the results “suggest large language models could affect our memory, attention and creativity.”
Researchers at MIT and elsewhere have developed Boltz-2, an open-source generative AI model that can help researchers find new medicines faster, reports Alex Knapp for Forbes. The tool “can not only predict the structure of proteins, it can also predict its binding affinity–that is, how well a potential drug is able to interact with that protein,” explains Knapp. “This is crucial in the early stages of developing a new medicine.”
Postdoc Isabella Loaiza speaks with ABC News reporter Max Zahn about her study examining how jobs and tasks across the U.S. economy shifted between 2016-2024. Loaiza and her colleagues found that “rather than dispense with qualities like critical thinking and empathy, workplace technology heightened the need for workers who exhibit those attributes,” Zahn explains. “It is true we’re seeing AI having an impact on white-collar work instead of more blue-collar work,” says Loaiza. “We found that jobs that are very human-intensive are probably more robust.”
Wall Street Journal reporter Dominique Mosbergen spotlights how Prof. James Collins and his lab have built their “own algorithms to trawl chemical databases, such as those of existing pharmaceutical drugs, for potential antibacterial compounds.” Collins’ His lab is “also experimenting with using generative AI to design completely new molecules that could kill bacteria,” writes Mosbergen.
Lecturer Michael Schrage speaks with Forbes reporter Josipa Majic Predin about the shift towards generative AI in business. "AI should not be seen overwhelmingly as just an ethical or a technical or a digital innovation and platform," says Schrage. "It's actually a philosophical capability and resource."
Writing for The Boston Globe, graduate students Manuj Dhariwal SM '17 and Shruti Dhariwal SM '18 highlight new efforts to reframe the language used to describe the ways humans are interacting with AI technologies. “It is a subtle reframing, but one that we urgently need as AI systems become interwoven with our creative, social, and emotional worlds,” they write. “The point is not necessarily to choose one over the other — but to clearly distinguish one from the other.”
Principal Research Scientist Kalyan Veeramachaneni speaks with WBUR On Point host Meghna Chakrabarti about the benefits and risks of training AI on synthetic data. “I think the AI that we have as of today and we are using is largely very small; I don't mean that as in size, but in the tasks that it can do,” says Veeramachaneni. “And as days go by, we are asking more and more of it… that requires us to provide more data, train more models that are much more efficient in reasoning, and can solve problems that we haven't thought of solving.”