Writing for Fast Company, Moshe Tanach highlights how researchers from the MIT Lincoln Laboratory Supercomputing Center are developing new technologies to reduce AI energy costs, such as power-capping hardware and tools that can halt AI training.
MIT scientists have developed a “four-fingered robotic hand which is capable of rotating balls and toys in any direction and orientation,” reports Maisie Lillywhite for BBC News. “The improvement in dexterity could have significant implications for automating tasks such as handling goods for supermarkets or sorting through waste for recycling,” Lillywhite writes.
Writing for Forbes, lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 explores the role of artificial intelligence and biotechnology in transforming the healthcare industry specifically for venture capitalists (VCs). “The fusion of AI and biotechnology presents a wealth of opportunities for venture capitalists,” writes Hayes-Mota. “By staying attuned to emerging trends and adopting strategies for impactful investments, VCs can drive innovation and create transformative changes in healthcare.”
MIT researchers have improved upon the diffusion models used in AI image generation, reports Alok Jha for The Economist. Working with electrically charged particles, the team created “Poisson flow generative models,” which “generate images of equal or better quality than state-of-the-art diffusion models, while being less error-prone and requiring between ten and 20 times fewer computational steps,” Jha explains.
Prof. Regina Barzilay spoke at The Futurist Summit: The Age of AI – an event hosted by The Washington Post – about the influence of AI in medicine. “When we're thinking today how many years it takes to bring new technologies [to market], sometimes it's decades if we’re thinking about drugs, and very, very slow,” Barzilay explains. “With AI technologies, you've seen how fast the technology that you're using today is changing.”
Washington Post reporter Carolyn Johnson spotlights how Prof. Laura Schulz and her colleagues have been exploring why ChatGPT-4 performs well on conversation and cognitive tests, but flunks reasoning tests that are easy for young children. Schulz makes the case that to understand intelligence and create it, childhood learning processes should not be discounted. “That’s the kind of intelligence that really might give us a big picture,” Schulz explains. “The kind of intelligence that starts not as a blank slate, but with a lot of rich, structured knowledge — and goes on to not only understand everything we have ever understood, across the species, but everything we will ever understand.”
Researchers at MIT have designed an “AI-powered chatbot that simulates a user’s older self and dishes out observations and pearls of wisdom,” reports Ian Sample for The Guardian. “The goal is to promote long-term thinking and behavior change,” says graduate student Pat Pataranutaporn. “This could motivate people to make wiser choices in the present that optimize for their long-term wellbeing and life outcomes.”
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.