Machine learning

Boston Globe reporter James McCown highlights the architectural design of the new MIT Schwarzman College of Computing, noting that it is, “the most exciting work of academic architecture in Greater Boston in a generation.”Dean Daniel Huttenlocher adds: “The building was designed to be the physical embodiment of the college’s mission of fortifying studies in computer science and artificial intelligence. The building’s transparent and open design is already drawing a mix of people from throughout the campus and beyond.”

Prof. Regina Barzilay joins The Economist’s “Babbage” podcast to discuss how artificial intelligence could enable health care providers to understand and treat diseases in new ways. Host Alok Jha notes that Barzilay is determined to “overcome those challenges that are standing in the way of getting AI models to become useful in health care.” Barzilay explains: “I think we really need to change our mindset and think how we can solve the many problems for which human experts were unable to find a way forward.”  

Current AI models require enormous resources and often provide unpredictable results. But graduate student Ziming Liu and colleagues have developed an approach that surpasses current neural networks in many respects, reports Manion Bischoff for Scientific American. “So-called Kolmogorov-Arnold networks (KANs) can master a wide range of tasks much more efficiently and solve scientific problems better than previous approaches,” Bischoff explains.

Financial Times reporter Robin Wigglesworth spotlights Prof. Daron Acemoglu’s new research that predicts relatively modest productivity growth from AI advances. On generative AI specifically, Acemoglu believes that gains will remain elusive unless industry reorients “in order to focus on reliable information that can increase the marginal productivity of different kinds of workers, rather than prioritizing the development of general human-like conversational tools,” he says.

Writing for the Financial Times, Jon Hilsenrath revisits lessons from the occupational shifts of the early 2000s when probing AI’s potential impact on the workplace. He references Prof. David Autor’s research, calling him “an optimist who sees a future for middle-income workers not in spite of AI, but because of it…creating work and pay gains for large numbers of less-skilled workers who missed out during the past few decades.”

On NPR’s Short Wave, climate correspondent Lauren Sommer reports on MIT researchers using artificial intelligence to decode the secret language of sperm whales. Prof. Daniela Rus says, “it really turned out that sperm whale communication was indeed not random or simplistic but rather structured in a very complex, combinatorial manner.”

Prof. David Autor is a guest of Meghna Chakrabarti on WBUR’s On Point, discussing his research on the potential impact of AI on the workforce. Autor says “AI is a tool that can enable more people with the right foundational training and judgment to do more valuable work.”

Researchers at MIT and elsewhere have developed a new machine-learning model capable of “predicting a physical system’s phase or state,” report Kyle Wiggers and Devin Coldewey for TechCrunch. 

Nature reporter Andrew Robinson reviews “The Heart and the Chip,” a new book by Prof. Daniela Rus and science writer Gregory Mone. The book “focuses on combining human and robotic strengths to pair ‘the heart and the chip’ in three interlinked fields: robotics, artificial intelligence and machine learning,” explains Robinson. 

The Hill reporter Tobias Burns spotlights the efforts of a number of MIT researchers to better understand the impact of generative AI on productivity in the workforce. One research study “looked as cases where AI helped improved productivity and worker experience specifically in outsourced settings, such as call centers,” explains Burns. Another research study explored the impact of AI programs, such as ChatGPT, among employees. 

Forbes selects innovators for the list’s Healthcare & Science category, written by senior contributor Yue Wang. On the list is MIT PhD candidate Yuzhe Yang, who studies AI and machine learning technologies capability to monitor and diagnose illnesses such as Parkinson's disease.

Researchers from MIT and elsewhere have used quantitative and computational methods to analyze animal communication, reports Emily Anthes for The New York Times.

Writing for The Architect’s Newspaper, James McCown describes the glass curtain wall at the new MIT Schwarzman College of Computing. “Artificial intelligence will be one of the chief research initiatives taking place at Schwarzman,” McCown notes. With all of its transparency, here MIT and SOM have created a powerful gesture of openness and accountability—a crucial perspective as AI technology advances in ways that are both exciting and foreboding.” 

An analysis by MIT researchers has identified “wide-ranging instances of AI systems double-crossing opponents, bluffing and pretending to be human,” reports Hannah Devlin for The Guardian. “As the deceptive capabilities of AI systems become more advanced, the dangers they pose to society will become increasingly serious,” says postdoctoral associate Peter Park. 

MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine. “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis.