Artificial intelligence

Maxime Cohen PhD ‘15 speaks to Forbes’ Karl Moore about his latest book “Pricing in the Age of AI,” that explores how AI is transforming pricing decisions for businesses, consumers and society. “We may be entering an era of agent-to-agent commerce, in which AI systems negotiate directly with one another on behalf of consumers and businesses,” says Cohen. “This could fundamentally reshape how prices are determined and how transactions take place.” 

Prof. Daron Acemoglu joins CNBC’s “Closing Bell” to discuss how AI could cause mass job displacement due to companies deploying rapidly advancing technologies. “AI’s capabilities are real and powerful, but human jobs are also complex. I don’t think you want AI to take over air traffic controller jobs right now; you don’t want AI to do CEO jobs,” says Acemoglu. “What we are seeing when these models escape their sandboxes and create havoc—it's not that they are necessarily super intelligent, it is that they don’t have judgement and they are optimized for certain narrow metrics and when you let them loose, they are going to do lots of unforeseeable things. You don’t want that in many jobs.”  

Washington Post reporter Ariana Eunjung Cha spotlights Prof. Pattie Maes’ research at the intersection of AI and memory, including her wearable AI devices designed to assist older adults that “capture pieces of everyday experience and make them retrievable later.” Maes’ research “takes on particular urgency for people with Alzheimer’s disease and other forms of dementia, diseases defined by memory loss that can rob a person’s ability to navigate everyday life independently,” writes Eunjung Cha.  

Prof. Sherry Turkle joins The Boston Globe’s Anna Kusmer, host of the “Say More” podcast, to discuss how AI chatbots can significantly deskill school-aged children, as detailed in her latest book, “Artificial Intimacy: Who We Become When We Talk to Machines.” “People in the thick of this know it is not good for children, it’s a deskilling,” says Turkle. “If your chatbot reads for you, you don’t learn to read, you learn to listen to a podcast about a book, if your chatbot writes for you, you don’t learn to think, because thinking is writing, you learn to sort of edit your chatbots ideas: you learn to prompt.”  

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.”  

In an opinion piece for The Boston Globe, graduate student Jonah Prousky shares his observations that students who use AI minimally, or not at all, for their writing, tend to score higher on their written work. “In this environment, where a professor or teaching assistant reads dozens of essays, one after another, AI-generated writing is not only easy to detect but can be terribly monotonous to read,” writes Prousky. “It’s the students who sound most like themselves, by contrast, who delight all the more.”  

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.” 

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.” 

Forbes’ Lisa Chambers highlights MIT as one of the “12 top colleges reinventing education for the AI era.” “At Forbes’ No. 1-ranked college in the nation—and one of the world’s leading technology schools—AI is hardly confined to a single degree,” writes Chambers. “MIT’s undergraduate offerings range from machine learning, computer vision and natural language processing to robotics, deep-learning hardware and AI applications in biology and medicine.”  

Prof. Pat Pataranutaporn and graduate student Manuel Cherep speak to the Scientific American’s Mary Randolph about companies creating AI agents to assist users in personal tasks. “We have this narrative crisis about AI right now. There’s two opposite visions of the future, and while people worry about AI,” says Pataranutaporn, “there’s this cute AI tool that’s being released.” 

In an interview with Bloomberg, Prof. Justin Solomon discusses how mathematicians are responding to advances in AI in the wake of an AI platform solving the Navier-Stokes problem. “The really interesting part [of proof writing] is the modeling and figuring out which questions to ask and what insights they bring us to the universe, and the AI tools are not doing that,” says Solomon. “Writing the prompts is very much what we’ve been teaching our students to do for centuries now, and that skill, I think, persists.” 

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.”