Vertical Semiconductor, an MIT spinoff, is working to “commercialize chip technology that can deliver electricity to artificial intelligence servers more efficiently,” reports Stephen Nellis for Reuters. “We do believe we offer a compelling next-generation solution that is not just a couple of percentage points here and there, but actually a step-wise transformation,” says Cynthia Liao MBA '24.
A new study by researchers at MIT suggests that “the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models,” reports Will Knight for Wired. “By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade.”
Researchers at MIT have developed a new method that can predict how plasma will behave in a tokamak reactor given a set of initial conditions, reports Gayoung Lee for Gizmodo. The findings “may have lowered one of the major barriers to achieving large-scale nuclear fusion,” explains Lee.
Writing for Forbes, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 emphasizes the importance of implementing ethical frameworks when developing AI systems designed for use in healthcare. “The future of AI in healthcare not only needs to be intelligent,” writes Hayes-Mota. “It needs to be trusted. And in healthcare, trust is the ultimate competitive edge.”
Researchers at MIT have studied how chatbots perceived the political environment leading up to the 2024 election and its impact on automatically generated election-related responses, reports Patrick Kulp for Tech Brew. The researchers “fed a dozen leading LLMs 12,000 election-related questions on a nearly daily basis, collecting more than 16 million total responses through the contest in November,” explains Kulp.
Prof. Daron Acemoglu speaks with Financial Times reporters Claire Jones and Melissa Heikkilä about the economic implications of the AI boom. “There is a lot of pressure on managers to do something with AI… and there is the hype that is contributing to it,” says Acemoglu. “But not many people are doing anything super creative with it yet.”
In an effort to better understand how protein language models (PLMs) think and better judge their reliability, MIT researchers applied a tool called sparse autoencoders, which can be used to make large language models more interpretable. The findings “may help scientists better understand how PLMs come to certain conclusions and increase researchers’ trust in them," writes Andrea Luis for The Scientist.
Noman Bashir, a fellow with MIT’s Climate and Sustainability Consortium, speaks with Smithsonian Magazine reporter Amber X. Chen about the impact of AI data centers on the country’s electric grid and infrastructure. Bashir notes “that the industry’s environmental impacts can also be seen farther up the supply chain,” writes Chen. “The GPUs that power A.I. data centers are made with rare earth elements, the extraction of which Bashir notes is resource intensive and can cause environmental degradation.”
Institute Prof. Daron Acemoglu participated in a “global dialogue on artificial intelligence governance” at the United Nations, reports Steve Lohr for The New York Times. “The AI quest is currently focused on automating a lot of things, sidelining and displacing workers,” says Acemoglu.
Researchers from MIT and Stanford tracked 11 large language models during the 2024 presidential campaign, and found that “AI models answered differently overtime… [and] they changed in response to events, prompts, and even demographic cues,” reports Ron Schmelzer for Forbes.
Vana, an MIT startup, is developing an app “that works like a wallet for personal data that can be used to train AI,” reports Megan Morrone for Axios. “Vana hopes people will use the app to control and pool their own data with others, shape how it’s used and share in the value it creates,” writes Morrone.
President Emeritus L. Rafael Reif joins Bloomberg’s Wall Street Week to highlight the importance of university research for the U.S. economy. “The federal government funds research at universities,” begins Reif. “Scientific research advances knowledge. And we do it here. And at the same time we educate the leaders of the future, who bring that advanced knowledge into the marketplace. That has been at the heart of the terrific ecosystem of innovation in this country.” He adds: “We have benefitted in the past 80 years from this terrific system, and not having access to that is going to basically kill the source of ideas that will power our economy for the next 80 years.”
Financial Times reporter Melissa Heikkilä spotlights how MIT researchers have uncovered evidence that increased use of AI tools by medical professionals risks “leading to worse health outcomes for women and ethnic minorities.” One study found that numerous AI models “recommended a much lower level of care for female patients,” writes Heikkilä. “A separate study by the MIT team showed that OpenAI’s GPT-4 and other models also displayed answers that had less compassion towards Black and Asian people seeking support for mental health problems.”
Prof. Dimitris Bertsimas, vice provost for MIT Open Learning, speaks with Forbes contributor Aviva Legatt about AI usage among university students. “Universities have a responsibility to ensure students, faculty, and staff gain a strong foundation in AI’s concepts, opportunities, and risks so they can help solve society’s biggest challenges,” says Bertsimas.
Edwin Chen '08 speaks with Forbes reporter Pheobe Liu about his journey to founding Surge AI, a startup that “helps tech companies get the high-quality data they need to improve their AI models.”