Research

MIT researchers have developed the Probabilistic Bias Correction (PBC) tool, which “uses machine learning to identify systematic bias in a model and make corrections” to improve weather forecasts, reports The Verge’s Justine Calma. “The researchers found that PBC improved the accuracy of a leading physics-based weather model by the European Centre for Medium-Range Weather Forecasts (ECMWF) for 98 percent of precipitation and 92 percent of temperature targets,” notes Calma. “The machine learning framework also doubled the accuracy of ECMWF’s new AI forecasting system in the study’s analysis.” 

Gizmodo’s Ellyn Lapointe spotlights the Probabilistic Bias Correction (PBC) tool developed by MIT researchers to “correct systemic errors in forecast models and improve the accuracy of their subseasonal outlooks.” “Extreme weather has the largest impact on our society and improved forecasts of these events can yield large societal benefits,” says Research Scientist Judah Cohen. “Interestingly, previous bias correction schemes actually made forecasts of extreme weather worse, and our model showed clear improvement of extreme weather forecasts.”  

On The New York Times’ “Ezra Klein Show,” Prof. Sherry Turkle shares her insights on humans seeking companionship with AI chatbots, detailed in her latest book, “Artificial Intimacy: Who We Become When We Talk to Machines.” In relationships with chatbots, “on the one hand, people start to see the chatbots more like people,” says Turkle. “But I think, maybe more significantly, people start to be disappointed with people when they don’t behave like chatbots. They start to think of themselves as if they were computers.”  

MIT researchers have developed an AI system “that not only minimizes forecasting errors, but learns from its inevitable mistakes through a method called postprocessing,” writes reporter Andrew Paul for Popular Science. “Their AI postprocessing integration blew the existing forecasting models out of the water,” notes Paul. “Such massive improvements could soon help the farmers and energy providers who already rely on subseasonal forecasts, but the benefits may even save lives.” 

Research led by Principal Research Scientist Neil Thompson suggests that AI is likely to be upend theoretical scientific fields within a few years, writes Wired’s Will Knight. “As with math, Thompson says, we should expect advances in other areas to have both positive and negative effects,” writes Knight. “He notes that theoretical advances sometimes drive practical advances in science and suggests that greater use of AI may mean that more progress happens this way.” 

MIT spinout Cambridge Heavy Industries is developing a desktop-sized fusion reactor that can enable 50 to 80 experiments per day, writes TechCrunch’s Tim De Chant. “Cambridge Heavy’s fusion reactor uses lasers to compress a fuel target, similar to the groundbreaking experiment that proved controlled fusion reactions could produce more power than they consume,” notes De Chant. “But unlike other inertial confinement designs, Cambridge Heavy’s minimizes the number of lasers required to spark fusion.”  

Prof. Ankur Moitra and Prof. John Urschel share their thoughts with Forbes’ Alex Knapp about OpenAI releasing more than 700 math preprint papers online. “We’re used to landmark results being accompanied by new insights and theories,” writes Moitra. “But these feel different. The claims are spectacular, the papers are incomprehensible and there’s no punchline in sight that explains why the mysteries suddenly unraveled.” Adds Urschel: “It’s obvious that how we do and evaluate mathematics is going to have to change. What I think is constant is the fundamental idea of understanding that underpins our profession. And I’m excited to learn from these breakthroughs.” 

Using a technique based on Raman microscopy, MIT researchers have found a way to detect senescent cells, “zombie cells” that accumulate as tissues age, without destroying them, writes reporter Eric Ralls for Earth.com. “If the approach eventually works in living people, researchers could gain a way to track senescent cells over time without repeatedly removing tissue,” notes Ralls. “That could help scientists understand how these cells contribute to aging and whether treatments designed to eliminate them are working.” 

Boston Globe reporter Jon Chesto spotlights the fall fair event held as part of the MIT x GE Vernova Energy & Climate Alliance, during which, GE Vernova CEO Scott Strazik shared his enthusiasm for hiring MIT graduates. “Something we talk a lot about inside Vernova is the world is changing very quickly but we need to change quicker and even faster than that so we can lead in the world,” says Strazik. “Over the last year-plus on this campus, we see a lot of opportunities for us to get better through bringing you into the organization.”  

Prof. John Urschel speaks with Wall Street Journal reporter Andrew Beaton about how he called an audible and pivoted from the NFL to a career in mathematics, delving into his time playing for the Baltimore Ravens, his graduate studies at MIT and the publication of his latest research, which proves a decades-old mathematical conjecture. “I feel like I bet on myself in a big way,” Urschel says. “I’m living my best life right now.” 

Prof. Virginia Vassilevska Williams speaks to the Scientific American’s Peter Hall and Joseph Howlett about AI assisting in faster matrix multiplications. “Matrix multiplication is needed all over the place,” says Williams. “But also, its complexity is one of the great mysteries in computer science. In a maybe diabolical sense, the advance on matrix multiplication can be seen as an attempt by the LLMs to speed themselves up.”  

The New York Times’ Siobhan Roberts highlights Prof. Frank Wilczek’s 1973 discovery of asymptotic freedom, a physical property that “allowed physicists to better understand the nuclear force,” according to Edward Witten at the Institute for Advanced Study, that may help solve the Yang-Mills Millennium Prize Problem. The discovery “indicated that proving the existence of quantum Yang-Mills theory in four-dimensional spacetime was central to understanding the mathematical framework of modern subatomic physics.”  

Using prime editing, Prof. Jonathan Weissman led a team of MIT researchers that “built lineage maps for embryos between 7.5 days and 10 days old,” writes Science’s Mitch Leslie. “The ultimate goal,” writes Leslie, “a family tree tracing the ancestry of every cell in the mammalian body, could help researchers delve into how organs arise, probe the origins of birth defects, grow better replacement cells for patients, and study evolution.” 

Prof. John Urschel found that the Gaussian elimination algorithm, used to calculate shared solutions to a group of equations, specifically linear equations, is close to 100 percent reliable, writes reporter Miriam Waldvogel for The Washington Post. “It just seems so interesting that the most fundamental algorithm we have in the field, we do not fully understand,” says Urschel. “I really love this question of why versus how, and this idea of understanding what’s going on.” Remarking on his previous career as an NFL player, he shares: “If a student thinks that’s cool and [it] gets them to come take my class, and they learn a lot of cool, numerical math, that sounds great to me.”

With help from an artificial intelligence algorithm, MIT researchers have formulated heat-resistant mRNA COVID vaccines that “remain stable even when stored at room temperature for up to a year, or at nearly 100 degrees Fahrenheit for two months,” writes Nancy Lapid for Reuters. The heat resistance shots “would lower costs and make them [COVID vaccines] available in regions where ultra-cold storage is not possible,” Lapid notes.