In the Media

Metropolis reporter Anthony Paletta details how MIT’s inaugural Future Fest “brought together visionary leaders across design, technology, and art to address the world’s most pressing problems,” and also featured the public debut of the redesigned Metropolitan Warehouse. The festival featured discussions, exhibits and demonstrations on architecture, design and technology, including a new technique that utilizes knitted fibers as a flexible formwork for concrete, a discussion on how AI could be used to increase sustainability in construction and exploration of how “reef legos” can rebuild beaches and combat costal erosion. “There are no shortage of things going wrong in the world, and others likely to follow,” writes Paletta, “but MIT’s Future Fest offered reassuring proof that we’ve not run out of means to address these problems.” 

Flapping Airplanes, a startup co-founded by Benjamin Spector ’22, MEng ’22, has developed a framework for AI models to run efficiently while being trained on less data and in a single graphics processing unit (GPU), writes Julie Bort for TechCrunch. The framework “can run a task that spends most of its time waiting for data to be fetched alongside one that spends most of its time doing math, so the chip is rarely sitting idle,” notes Bort. “The premise is that AI has barely scratched the surface of what GPUs can do, and they believe it can create intelligent systems ‘without ingesting half the internet.’”  

Prof. Daron Acemoglu joins “The Claman Countdown” on Fox Business to discuss the possibility that largescale deployment of AI could triple unemployment within the next decade. “There is so much uncertainty about AI,” says Acemoglu. “It’s very difficult to know exactly when we’re going to see the productivity gains, which will come, and when we’re going to see job losses, and whether we’ll actually see some job creation as well. But on the current path where AI is being prepared and developed as a labor replacing technology, I would be very worried that within the next ten years we’ll see a lot of jobs disappear.”  

Eka Robotics, a startup cofounded by Prof. Pulkit Agrawal, has developed a robot with  a force-sensitive gripper that can “pick up about 1,500 objects that weigh up to 1 kg in an hour, compared to about 600 for a human” with 95% accuracy, reports Tim Fernholz for TechCrunch.  “We are starting off being very pragmatic, and saying here are two verticals in pick, arrange, place, and wire handling, let’s make them be home runs,” says Agrawal. “Once we have made home runs on them, the sky is the limit in how much we can do with the approach we are developing.” 

The New Yorker’s Gideon Lewis-Kraus details how Morris Chang ’52, SM ’53 founded the Taiwan Semiconductor Manufacturing Company (TSMC), “one of the most valuable tech companies globally” that “makes almost all of the world’s advanced [computer] chips.” “Chang proposed a semiconductor ‘foundry,’ which would do for pricey chips what its predecessors had done for cheap junk. At the time, however, the market didn’t exist,” writes Lewis-Kraus. “By the end of TSMC’s first decade, the company was bringing in a billion dollars in annual revenue.”  

Principal Research Scientist Deepjyoti Deka speaks to MassLive’s Tréa Lavery about the possibility of large data centers being located in greater Boston. “Anything, mostly, that you are using the computer for, that you are getting some information from, is housed in a data center,” says Deka. “When you talk about data centers in greater Boston, I doubt if we have any hyperscale, because there would need to be a gigantic factory that you could observe from a distance. But there are small ones.” 

Poets & Quants reporter Jeff Schmitt writes that MIT Sloan School of Management’s Assistant Dean Dawna Levenson has been honored as the “MBA Admissions Director of the Year,” by Poets & Quants. “Dawna brings a very human-first approach to MIT Sloan Admissions, deeply learning about our applicants as people and professionals, getting to know them through interviews, and understanding how they hope to make a difference in the world through their education,” says Sloan Dean Richard Locke. “She supports students throughout the admissions journey, providing guidance, helping them navigate the process, and pivoting, as needed, to support their matriculation.”  

For Manufacturing Dive, reporter Nathan Owens spotlights MIT’s Technologist Advanced Manufacturing Program (TechAMP), a 12-mong-long program that combines online instruction with hands-on lab work to bridge the gap between technical engineering and management skills, designed for working technicians or operators. “Students gain foundational leadership, process control and systems thinking skills,” notes Owens. TechAMP has “really shown me how to communicate more, communicate problems more, communicate solutions more,” says Brian Coppenrath, who completed the program.  

Postdoctoral Associate Iman YeckehZaare speaks to El País reporter Jordi Pérez Colomé about his research on RentAHuman, a platform that allows AI agents to hire people to do physical labor. “RentAHuman has shown that many people are willing to sign up to be hired by AI agents. What it still hasn’t shown is that many AI agents are doing the hiring,” says YeckehZaare. “Listings posted by agents will increase as AI agents become capable of acting and paying on their own.”  

Financial Times reporter Clive Cookson details how the late Margaret Hamilton engineered code that would play a crucial role in achieving the first moon landing, at MIT’s Instrumentation Lab. When Apollo 11’s onboard computer flashed alarms minutes from landing on the moon, “Hamilton and colleagues had engineered the software to dump non-essential background tasks under those circumstances and prioritize procedures needed for the landing,” writes Cookson. “NASA controllers in Houston trusted the software, so they decided not to abort the mission, allowing Neil Armstrong and Buzz Aldrin to touch down safely in their Eagle module.”  

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.” 

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.” 

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.”  

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.”