Artificial intelligence

The conversational AI models developed by MIT spinout Liquid AI are being introduced in Mercedes-Benz cars, writes Isabelle Bousquette for The Wall Street Journal. “Liquid AI invented a new architecture known as liquid neural networks, adaptive and efficient systems inspired by the neural activity of worms known scientifically as C. elegans,” writes Bousquette. Using the Liquid AI models in vehicles instead of cloud-based AI systems could allow for more “privacy, speed and the ability to run without constant network connectivity.” 

Prof. Daron Acemoglu shares his insights on artificial intelligence and the future of work and education during “Ask an Economist,” a virtual event hosted by NPR’s “The Indicator from Planet Money.” “I think there are a lot of pro-teacher AI opportunities where you give better tools to teachers, but teachers still remain central to the learning experience of students,” says Acemoglu. AI “can individualize the teaching experience much more effectively and at low cost in a way that wasn't possible before. And that expands teacher expertise, expands teacher capabilities, enables them to perform new tasks, doesn't replace them, doesn't sideline them.” 

In an interview with Nature’s David Adam, Prof. Carlo Ratti discusses design elements he believes architects should focus on in the age of AI, emphasizing how citizens, not algorithms, should be the architects  of the future. “One important thing that AI can do is show us on a large scale where a city fails people. Combining the computer vision used to identify problems with public images gives us the chance to do something about them,” Ratti explains. He adds: “Many of the best ideas about cities have come from people disagreeing with one another in the same room—from cafes to council chambers. If algorithms learn what people like and simply give us more of it, or if software agents start negotiating with other agents on our behalf, we could lose those encounters.” 

Prof. Gary Gensler joins “Bloomberg Open Interest” to discuss what a change in the control of Congress could mean for financial regulation. “I think it will be a wide-ranging look at what the Trump administration has done to roll back protections for investors in our capital markets,” says Gensler. “Going further than [examining the shuttering of] the Consumer Protection Bureau, issues around housing and housing affordability I think are very much on Democrats’ minds. And then financial stability issues as well, whether it’s a close look at private credit and then what we might call the big AI trade—how everything is interconnected.”  

CSAIL researchers have discovered that a large frontier AI model can, without any specialized training, understand and fine-tune another AI model’s capabilities, reports Fortune’s Jeremy Kah. The research “could be a big breakthrough in interpretability—our ability to understand the internal ‘thought’ processes of large language models,” writes Kah. “That’s critical because interpretability is one of the ways we are hoping to keep AI agents under human control.”   

Los Angelos Times reporter Mark Swed spotlights Prof. Tod Machover’s “already and not yet,” a new symphony that “handles democracy not as a done deal, but a series of scary questions — and doesn’t trust the heartless machines for answers.” “Already and not yet” challenges us to make the world more beautiful by looking deeply into ourselves,” writes Swed. The orchestral score treats “musicians as creative artists and the audience as capable of opening its collective mind.”  

CNN reporter Hadas Gold meets with Prof. Adam Berinsky and Prof. Chara Podimata to learn more about their new tool that tracks how popular AI chatbots source their information and respond to questions related to the midterm elections. “Even if people do not necessarily say that they trust AI, they like what AI gives them, which is a single answer,” says Berinsky. “What worries me is that the average voter, someone who might know a little about politics, but doesn’t know the details, is going to get this definitive answer [from an AI chatbot] that might be based on a different stew of information.”

Prof. John Urschel speaks to The New Yorker’s Rivka Galchen about AI being used as a tool for teaching and research in the field of mathematics. “I would say what I call the more scientist approach [to new technology] is being focused on trying to push the frontier of things as far as we can as quickly as we can,” says Urschel. “And the artist perspective is, ‘let me do something beautiful and enjoyable and manipulate these things,’ and the act of doing this is really, you know, a joyful thing.” 

Forbes has named MIT Professional Education’s Applied AI for Digital Transformation as the best professional development course available, writes Doug Wintemute and Veronica Beagle. “Designed for senior executives and tech leaders, this course explores AI adoption frameworks and strategies. Upon completion, graduates receive 6.4 continuing education units (CEUs) and a certificate of completion from the renowned MIT Professional Education.”  

Writing for Forbes, alumnus Paul Hsieh discusses radiologists employing rapidly evolving AI tools to help deliver diagnoses. “Radiologists are willing to employ AI tools that have been statistically validated to provide reliable diagnoses, even if we don’t yet fully understand how those tools work,” writes Hsieh. “But radiologists are not content to stop there. We also continually seek to improve genuine human understanding of how AI works in our field as a matter of professional responsibility for ourselves and for our patients.”  

CodeSignal, co-founded by alumnus and CEO Tigran Sloyan, has developed Agentic Skills Intelligence, a new product that “uses AI agents to evaluate the skills people use in their work and links the findings to personalized practice,” writes Forbes’ Ray Ravaglia. CodeSignal has built an AI agent “for each domain in its job taxonomy, such as engineering and sales, along with agents for capabilities that cut across jobs, among them communication and AI literacy,” notes Ravaglia. “The agents draw on systems including Gong, Salesforce, and GitHub, and they score what they find against rubrics the company says are grounded in industrial-organizational psychology.” 

Prof. Sanjay Sarma speaks to Bloomberg’s Saritha Rai about the difficulty of developing dexterous robots for use in the home versus developing factory machines for use in standardized plants. “Heavy work, hazardous tasks have long been the focus of automation,” says Sarma. “Robots seem to be following an evolutionary path–the finer the motor or cognitive skills required, the harder to automate.”  

Writing for Fortune, MIT Innovation Fellow David Siegel SM ’86, PhD ’91, Jeffrey Sonnenfeld and Stephen Henriques examine the timeline of the current investment boom in AI infrastructure. “The market may already have overbuilt relative to the payback window that data center financing assumes. Much of what corporate America spends on AI today funds exploratory pilots,” Siegel, Sonnenfeld and Henriques note. “A pullback to reassess is not only the likely next step, but a healthy one.”  

Prof. Ryan Williams speaks to TechCrunch’s Tim Fernholz about AI platforms refuting the Exponential Time Hypothesis, part of a broader class of problems known as P versus NP. “I do think that it kind of calls into question a lot of the intuitions that we thought we had about … how tractable [hard NP problems] are. I also think that this is probably not the final answer either,” says Williams. AI is “forcing people to be a little more ambitious about the kind of work that they do, and to think more deeply about, okay, if all the things I used to work on are now low-hanging fruit for AI, how can I raise the bar for the kind of scientific questions I want to ask?” 

Prof. Gary Gensler speaks with “Bloomberg Surveillance” hosts Tom Keene and Paul Sweeney about how the current AI investment boom may impact the U.S. economy. “You can be positive about the technology and still say our capital markets tend to overfund in exuberance,” says Gensler. “I think that we will have a deceleration of this AI boom, and then some form of a reversal in the next 18 to 30 months. And the question really, to me, is a little bit about timing: when and then how deep it [the deceleration] is. I think the depth of it is going to be dependent on how much debt is in the system, where it is [and] who holds it.”