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.
C. Gordon Bell ’57, SM 57 was a “computer pioneer always looking ten steps ahead and building that version of the world,” writes Gizmodo’s Matt Novak. Bell was, “a true visionary in the world of computing who helped design some of the first minicomputers in the 1960s," Novak adds.
Called the “Frank Lloyd Wright of computers,” technology visionary C. Gordon Bell ’57, SM '57, “the master architect in the effort to create smaller, affordable, interactive computers that could be clustered into a network,” has died. “He was among a handful of influential engineers whose designs formed the vital bridge between the room-size models of the mainframe era and the advent of the personal computer,” notes Glenn Rifkin for The New York Times.
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.
Researchers at CSAIL have created three “libraries of abstraction” – a collection of abstractions within natural language that highlight the importance of everyday words in providing context and better reasoning for large language models, reports Darren Orf for Popular Mechanics. “The researchers focused on household tasks and command-based video games, and developed a language model that proposes abstractions from a dataset,” explains Orf. “When implemented with existing LLM platforms, such as GPT-4, AI actions like ‘placing chilled wine in a cabinet' or ‘craft a bed’ (in the Minecraft sense) saw a big increase in task accuracy at 59 to 89 percent, respectively.”
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.
In an article for Fast Company, Lecturer Guadalupe Hayes-Mota offers five takeaways concerning the potential impact of AI on healthcare. Understanding AI’s healthcare potential “is crucial for business leaders and policymakers to foster an environment where AI and other analytics tools enhance rather than complicate societal outcomes,” Hayes-Mota writes.
Researchers from the Department of Air Force-MIT Artificial Intelligence Accelerator are applying new AI technologies to assist with everything from pilot communication to developing an AI alternative to GPS satellite-dependent navigation, reports Associated Press reporter Tara Copp.
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.
Researchers from MIT and elsewhere have found that “showing AI-generated images of a less car-reliant American city boosted support for sustainable transportation policies,” reports Linda Poon for Bloomberg. “Let’s help them imagine what it would actually be like to live in a car-less neighborhood, and a car-less city,” says postdoctoral associate Rachit Dubey.
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.
Researchers from MIT and elsewhere have used an AI model to develop a “new approach to finding money laundering on Bitcoin’s blockchain,” reports Andy Greenberg for Wired. “Rather than try to identify cryptocurrency wallets or clusters of addresses associated with criminal entities such as dark-web black markets, thieves, or scammers, the researchers collected patterns of bitcoin transactions that led from one of those known bad actors to a cryptocurrency exchange where dirty crypto might be cashed out,” explains Greenberg.
A new report by Principal Research Scientist Andrew McAfee explores the “implications of generative AI in economic growth, looking at everything from its possible effects on job skills and wages to how it may transform entire industries to its potential risks and pitfalls,” reports Sheryl Estrada for Fortune.
Researchers for MIT and Google are providing a free “Generative AI for Educators Course,” with the aim of helping middle and high school teachers use generative AI tools in the classroom. “MIT RAISE believes knowledge of generative AI is a key factor in creating a more equitable future for education,” says Cynthia Breazeal, director of MIT RAISE. “We’re thrilled to collaborate with Google to offer the Generative AI for Educators Course – providing middle and high school teachers with no-cost AI training. This course empowers educators to confidently integrate AI into their teaching, creating richer and more accessible learning experiences for all students.”