Prof. Regina Barzilay, graduate student Aziz Ayed and alumna Sydney Pham ‘24 are featured in an article about AI models and the workforce, written by Nature’s Ben Deighton. “Sometimes models can make sense of unexpected observations, which can guide further iterations of studies,” Pham says. “It’s helpful to understand how the models generate their results.”
Prof. Daniela Rus, Director of MIT CSAIL, and Prof. David Autor, department head of Economics, are featured on Time’s “TIME100 AI 2026” list of 100 innovators, leaders, and thinkers reshaping the world through their advances in AI. “Over three decades, [Rus] has conducted pioneering work in robotics, extending the understanding of what form robots can take,” writes Tharin Pillay, while Steven Freiss describes Autor as “one of the nation’s most vocal and prominent economists aiming to ascertain AI’s potential impacts on labor.”
Prof. Emeritus Rodney Brooks speaks to Scientific American’s Mary Randolph about humanoid robots interacting with the real world. “When you see a performance of an [artificial intelligence] system or a robot on one thing,” says Brooks, “that fools us into thinking that it has the same general competence as a human. And that’s a mistake people make.”
A new study by MIT researchers found that it is impossible to draw a direct link between AI diffusion model outputs and an artist’s body of work, writes Gizmodo’s Webb Wright. “If a [diffusion] model generates something, you want to be able to say, ‘Oh, this part of the training data was responsible,'” says Zheng Dai, PhD ’24, lead author on the study. “It’s important for us to understand how these models work to properly study them or regulate them.”
Prof. David Gifford and Zheng Dai PhD ’24 discovered that AI diffusion models can replicate the style of an artist even when their work was omitted from datasets, leading to ‘attribution decay,’ writes Fast Company’s Jesus Diaz. “This trend, what the researchers call ‘attribution decay,’ means that artificial intelligence can produce an image that resembles a particular artist’s work, while having no provable causal link to that artist’s actual contribution to the training data,” explains Diaz.
MIT researchers have discovered that when individual images are removed from large AI diffusion models, the models are still able to produce like images, writes Digital Trends’ Varun Mirchandani. “The more interesting takeaway is that this connection becomes increasingly difficult to trace as datasets grow,” writes Mirchandani. “An AI-generated image may draw on patterns learned from an enormous pool of material without having a clear, identifiable source image behind it.”
The Register’s Thomas Claburn highlights a new study by MIT researchers that found large AI models can still reproduce an artist’s style, even when the artist’s work is removed from its training data, making attribution more difficult. “[G]iven the contemporary adoption of these models for creative and commercial purposes, attributability also carries ethical, policy, financial, and legal implications,” the researchers note.
MIT researchers found the more data an AI model is trained on could complicate AI copyright, auditing, and governance, writes Computer World’s Taryn Plumb. “[S]ingle artworks by specific artists, or photographs of certain people, could be entirely removed from datasets, and the model could still reproduce that image or style,” writes Plumb. “Essentially, tangible connections are lost, and linking to specific data points responsible for generated samples is ‘practically impossible,’ or can even vanish, the researchers explained.”
Forbes’ “50 Over 50” list spotlights four MIT faculty leading innovation in science and technology: Prof. Paula Hammond, dean of the MIT School of Engineering; Prof. Dina Katabi; Prof. Nergis Mavalvala, dean of the MIT School of Science; and Aude Oliva, director of MIT-IBM Computing Research Lab. The 50 “science and technology standouts on this list are advancing how we use AI, fight osteoporosis and deploy clean energy.”
Bloomberg’s Gautam Mukunda highlights Prof. Scott Stern’s finding that 0.07% of business registrations in the 2010s reached meaningful acquisition within six years, and Prof. Danielle Li and Prof. Lindsey Raymond’s finding that AI technologies increased customer support agent productivity by 15%, to predict AI’s role in the influx of business applications filed in June.
Researchers from MIT and Toyota Research Institute developed SceneSmith, a system that allows robots to practice everyday tasks and action plans in over 1,300 AI generated environments before entering a real home or workplace, writes Fox News’ Kurt Knutsson. “SceneSmith's ability to combine realistic design with usable physics gives the project its strongest advantage,” notes Knutsson.
Graduate student Peter Zhi Xuan Li speaks to Tech Briefs’ Andrew Corselli about his team’s work developing a new chip that allows small, autonomous robots and other battery-limited devices to construct detailed 3D maps of their environments using a fraction of the power required by other systems. “Today, a robot uses separate, incompatible internal representations for different jobs: one for mapping, another for tracking its own motion, another for rendering what it sees, and every conversion adds memory footprint and energy,” says Li. “Our goal is a single shared representation.”
Prof. Daniela Rus, director of CSAIL, joins Hakeem Oluseyi, host of GBH’s “Particles of Thought,” to discuss her group’s work developing liquid neural networks, AI that runs locally on a given device rather than a data center to increase privacy and improve energy efficiency. By using liquid AI, "you avoid privacy concerns and you avoid the security concerns associated with accessing the cloud,” says Rus. “Furthermore, the cost of running your models on a device is much, much lower than running a huge model in the cloud.”
Chronicle of Philanthropy reporter Maria Di Mento spotlights how the creation of the MIT Schwarzman College of Computing allowed MIT to develop new “interdisciplinary programs to prepare students for an AI-saturated world and help them understand the social and ethical implications of digital technologies.” Prof. Daniel Huttenlocher, dean of the Schwarzman College of Computing, explains that: “MIT realized that effective education in the age of AI has to look different than it has in the past. Traditional siloing of expertise won’t work when AI is expected to touch nearly every part of people’s lives and is changing the way people in disciplines outside of computing are advancing their work.”
Writing for Forbes about efforts to improve air travel safety, Tanya Eves highlights the Air-Guardian system, an eye-tracking monitor for pilots developed by CSAIL researchers that assists when attention wavers. “In tests, it reduced flight risk and improved navigation success rates,” writes Eves. “It's a model for how the virtual co-pilot relationship should work: not replacement, but a seamless, intelligent partnership that understands when to act and when to stay silent.”