Artificial intelligence

Fast Company’s Adele Peters highlights Firma Power, a clean energy developer co-founded by MIT alumni aimed at working with data centers to use clean alternatives to gas power. “We started the company with the hypothesis that despite the fact that 98% of what’s in the queue is wind, solar and storage, that’s not getting tapped as the default path to power for large loads—so there’s got to be some market frictions and barriers there,” says Jesse Jenkins SM ’14, PhD ’18.  

Prof. Jacob Andreas speaks with CNN’s Lisa Eadicicco about deciphering the chain-of-thought behind complex AI neural networks. “I think we’re still very far from being able to claim that we have any sort of fine-grained understanding of how the model is actually arriving at a solution,” says Andreas.  

Prof. Emilio Castilla discusses the factors companies must consider when deploying AI to help in the hiring process, in an article by USA Today’s Greta Reich. “We should evaluate not only whether the algorithm predicts historical hiring decisions, but whether it helps identify candidates who subsequently perform well in the job,” writes Castilla. “The most exciting possibility is not simply that AI might help us find the best talent more efficiently. It is that AI might help us recognize talent that our traditional hiring practices have systematically overlooked.”  

Research Affiliate Paul Kedrosky joins The New York Times’ “The Ezra Klein Show” to discuss how the development of AI is creating an economic bubble, where asset prices are detached from economic reality. “We’re building it [AI] out like we were the railroads, like we were electrification, like we were the canals, like we were the interstate highways. And every one of those moments has led to overbuild,” says Kedrosky. “The reality is we’ve never seen a general-purpose technology that had such broad applicability, was adopted so quickly and was repricing labor at the same time.”  

In The Boston Globe’s “Starting Point Newsletter,” reporter Anna Kusmer features Prof. Sherry Turkle’s insights from her latest book “Artificial Intimacy: Who We Become When We Talk to Machines,” including the risks that AI companionship poses to school-aged children. “Children are bringing the world inside; they’re bringing the people around them inside them to create an inner life,” says Turkle. “And when you talk to a chatbot, there is nothing to bring inside. There is no caring person.” 

In an interview with Wired’s Steven Levy, Prof. Sherry Turkle discusses how she witnessed chatbots with human-like features becoming “dangerously enmeshed” with people, during research for her latest book “Artificial Intimacy: Who We Become When We Talk to Machines.” “In every serious interview I do, people invoke this myth about practice—that you use chatbots to practice for talking to your boss, your lover, your teacher,” says Turkle. “But when you’re bathing in the warm soup of total acceptance, talking to chatbots becomes the endgame.” 

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.”