Prof. David Autor and Prof. Daron Acemoglu speak with New York Times columnist Peter Coy about the impact of AI on the workforce. Acemoglu and Autor are “optimistic about a continuing role for people in the labor market,” writes Coy. “An upper bound of the fraction of jobs that would be affected by A.I. and computer vision technologies within the next 10 years is less than 10 percent,” says Acemoglu.
MIT researchers have found that “when an AI tool for radiologists produced a wrong answer, doctors were more likely to come to the wrong conclusion in their diagnoses,” report Daniel Payne, Carmen Paun, Ruth Reader and Erin Schumaker for Politico. “The study explored the findings of 140 radiologists using AI to make diagnoses based on chest X-rays,” they write. “How AI affected care wasn’t dependent on the doctors’ levels of experience, specialty or performance. And lower-performing radiologists didn’t benefit more from AI assistance than their peers.”
Alumnus Benjamin Rapoport co-founded Precision Neuroscience, a brain-computer interface company, that is developing technology that will allow “paralyzed patients the ability to operate a computer with their thoughts,” reports Jo Craven McGinty for The Wall Street Journal. “In order to be a citizen of the world in 2024, to communicate with loved ones, to make a living, the ability to work with a digital system is indispensable,” says Rapoport. “To operate a word processor is totally transformative.”
Washington Post columnist Eduardo Porter spotlights various MIT research efforts aimed at ensuring that AI is used to empower human workers rather than replace them. Prof. David Autor notes: “AI, if used well, can assist with restoring the middle-skill, middle-class heart of the U.S. labor market that has been hollowed out by automation and globalization.”
Prof. Pulkit Agrawal and graduate student Gabriel Margolis speak with The Economist’s Babbage podcast about the simulation research and technology used in developing intelligent machines. “Simulation is a digital twin of reality,” says Agrawal. “But simulation still doesn’t have data, it is a digital twin of the environment. So, what we do is something called reinforcement learning which is learning by trial and error which means that we can try out many different combinations.”
Prof. Thomas Malone speaks with Poets & Quants for Executives reporter Alison Damast about the executive education course he teaches with Prof. Daniela Rus that aims to provide senior-level managers with a better sense of how AI works. “We are certainly not trying to teach people to understand the details of how to write AI programs, though some of those in the course may know that already,” Malone says. “What we are trying to do is give them a sense of when it is easy and when it is hard to use AI technology at various times for different kinds of business applications.”
Mashable reporter Adele Walton spotlights Joy Buolamwini PhD '22 and her work in uncovering racial bias in digital technology. “Buolamwini created what she called the Aspire Mirror, which used face-tracking software to register the movements of the user and overlay them onto an aspirational figure,” explains Walton. “When she realised the facial recognition wouldn’t detect her until she was holding a white mask over her face, she was confronted face on with what she termed the ‘coded gaze.’ She soon founded the Algorithmic Justice League, which exists to prevent AI harms and increase accountability.”
Writing for Fast Company, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 shares methods to address the influence of AI in healthcare. “Despite these advances [of AI in healthcare], the full spectrum of AI’s potential remains largely untapped,” explains Hayes-Mota. “Systemic hurdles such as data privacy concerns, the absence of standardized data protocols, regulatory complexities, and ethical dilemmas are compounded by an inherent resistance to change within the healthcare profession. These barriers underscore the urgent need for transformative action from all stakeholders to fully harness AI’s capabilities.”
A new study conducted by researchers at MIT and elsewhere has found large language models (LLMs) can be used to predict the future as well as humans can, reports Chris Stokel-Walker for Fast Company. “Accurate forecasting of future events is very important to many aspects of human economic activity, especially within white collar occupations, such as those of law, business and policy,” says postdoctoral fellow Peter S. Park.
Prof. Daniela Rus, director of CSAIL, speaks with The Economist’s Babbage podcast about the history and future of artificial neural networks and their role in large language models. “The early artificial neuron was a very simple mathematical model,” says Rus. “The computation was discrete and very simple, essentially a step function. You’re either above or below a value.”
Prof. Philip Isola and Prof. Daniela Rus, director of CSAIL, speak with Associated Press reporter Matt O’Brien about AI generated images and videos. Rus says the computing resources required for AI video generation are “significantly higher than for still image generation” because “it involves processing and generating multiple frames for each second of video.”
Prof. Daniela Rus, director of CSAIL, speaks with Boston Globe reporter Evan Sellinger about her new book, “The Heart and the Chip: Our Bright Future With Robots,” in which she makes the case that in the future robots and humans will be able to team up to create a better world. “I want to highlight that machines don’t have to compete with humans, because we each have different strengths. Humans have wisdom. Machines have speed, can process large numbers, and can do many dull, dirty, and dangerous tasks,” Rus explains. “I see robots as helpers for our jobs. They’ll take on the routine, repetitive tasks, ensuring human workers focus on more complex and meaningful work.”
Prof. Stuart Madnick speaks with CNBC reporter Kevin Williams about how the rise of generative AI technologies could lead to cyberattacks on physical infrastructure. “If you cause a power plant to stop from a typical cyberattack, it will be back up and online pretty quickly,” Madnick explains, “but if hackers cause it to explode or burn down, you are not back online a day or two later; it will be weeks and months because a lot of the parts in these specialized systems are custom made.”
Prof. Skylar Tibbits speaks with Tech Briefs reporter Andrew Corselli about his team’s work developing a new “additive manufacturing technique that can print rapidly with liquid metal, producing large-scale parts like table legs and chair frames in a matter of minutes.” Of his advice for engineers aiming to bring their ideas to fruition, Tibbits emphasizes: “Work hard, fail a lot, keep trying, don’t give up, and have amazing people around you. We're a research lab, so our whole goal is to go from impossible to possible. So, we're allowed to fail; we're not limited by profitability or customer demand or economy.”
Prof. Charles Stewart III and Ben Adida PhD ’06 speak with Fast Company reporter Spenser Mestel about how to restore the public’s faith in voting technology. Adida discusses his work launching VotingWorks, a non-profit focused on building voting machines. VotingWorks is “unique among the legacy voting technology vendors," writes Mestel. “The group has disclosed everything, from its donors to the prices of its machines.”