Researchers at MIT have found that prospective job applicants who utilized basic AI modules in their application process were, on average, more likely to get hired and receive higher wages, reports Maria Gracia Santillana Linares for Forbes. “[Applicants] with access to the technology are more likely to get hired without any negative implications [from] employers,” says graduate student Emma Wiles.
Cofounded by postdoctoral associate Wen Shuhao and postdoctoral fellows Ma Jian and Lai Lipeng, biotech startup Xtalpi "combines AI, quantum physics, cloud computing and robotic automation to find novel molecules that could be developed into new medicines,” reports Zinnia Lee for Forbes. “Xtalpi has also recently expanded into discovering new chemical compounds for applications such as agriculture, cosmetics, healthcare, as well as petrochemicals and new materials for electric vehicle batteries,” writes Lee.
Washington Post reporter Carolyn Johnson spotlights how Prof. Laura Schulz and her colleagues have been exploring why ChatGPT-4 performs well on conversation and cognitive tests, but flunks reasoning tests that are easy for young children. Schulz makes the case that to understand intelligence and create it, childhood learning processes should not be discounted. “That’s the kind of intelligence that really might give us a big picture,” Schulz explains. “The kind of intelligence that starts not as a blank slate, but with a lot of rich, structured knowledge — and goes on to not only understand everything we have ever understood, across the species, but everything we will ever understand.”
Prof. Devavrat Shah is interviewed by Forbes’ Gary Drenik on balancing AI innovation with ethical considerations, noting governance helps ensure the benefits of AI are fairly distributed across society. “Our responsibility is to harness [AI’s] potential while safeguarding against its risks,” Shah explains. “This approach to promoting responsible AI development hinges on governance rooted in collaboration, transparency and actionable guidance."
Prof. Hal Abelson speaks with Inside Higher Ed reporter Lauren Coffey about AI policies in academia. “We put tremendous emphasis on creating with AI but that’s the sort of place that MIT is,” says Abelson. “It’s about making things. Other places have a very different view of this.”
A new paper by Prof. Daron Acemoglu and Prof. Simon Johnson uses the impact of automation in the textile industry to predict potential similar effects from AI, writes Bloomberg’s Andy Mukherjee. Noting the parallels between the Indian textile industry and disruption currently underway in tech outsourcing, the economists write “the impact of automation on workers today is more complex than an automatic linkage from higher productivity to better wages.”
Prof. Tod Machover joins GBH’s The Culture Show to discuss artificial intelligence in music, from exciting new tools to debates about licensing. Speaking with host Jared Bowen, he says “the field is changing so fast right now, it’s so important to keep up and also to decide how to influence it, because we’re trying to push this towards a positive end.”
New York Times columnist Thomas Edsall spotlights recent research by Profs. Daron Acemoglu, David Autor and Simon Johnson, in which they explore whether artificial intelligence could be a beneficial tool for workers. “It is quite possible to leverage generative AI as an informational tool that enables various different types of workers to get better at their jobs and perform more complex tasks,” explains Acemoglu. However, he notes “to turn generative AI pro-worker, we need a major course correction.”
Researchers at MIT have designed an “AI-powered chatbot that simulates a user’s older self and dishes out observations and pearls of wisdom,” reports Ian Sample for The Guardian. “The goal is to promote long-term thinking and behavior change,” says graduate student Pat Pataranutaporn. “This could motivate people to make wiser choices in the present that optimize for their long-term wellbeing and life outcomes.”
An essay co-authored by Prof. Simon Johnson in Project Syndicate argues that for all the predictions about AI’s effect on the workforce, the most likely outcome is that many people will face pressure to change jobs as the labor market adjusts. Policymakers must focus on human capital, he writes, and “shared prosperity can flow from new technology, but only if its adoption is accompanied by upgraded human skills and more proactive worker redeployment.”
Boston Globe reporter James McCown highlights the architectural design of the new MIT Schwarzman College of Computing, noting that it is, “the most exciting work of academic architecture in Greater Boston in a generation.”Dean Daniel Huttenlocher adds: “The building was designed to be the physical embodiment of the college’s mission of fortifying studies in computer science and artificial intelligence. The building’s transparent and open design is already drawing a mix of people from throughout the campus and beyond.”
Prof. Daron Acemoglu’s new study projects just mild economic upside in the U.S. stemming from AI advancement, writes Business Insider’s Filip De Mott. According to Acemoglu, AI-led U.S. GDP growth in the next 10 years will rise just 0.93% to 1.16%, due to uncertainty on how much AI can really advance total factor productivity.
Prof. Regina Barzilay joins The Economist’s “Babbage” podcast to discuss how artificial intelligence could enable health care providers to understand and treat diseases in new ways. Host Alok Jha notes that Barzilay is determined to “overcome those challenges that are standing in the way of getting AI models to become useful in health care.” Barzilay explains: “I think we really need to change our mindset and think how we can solve the many problems for which human experts were unable to find a way forward.”
Current AI models require enormous resources and often provide unpredictable results. But graduate student Ziming Liu and colleagues have developed an approach that surpasses current neural networks in many respects, reports Manion Bischoff for Scientific American. “So-called Kolmogorov-Arnold networks (KANs) can master a wide range of tasks much more efficiently and solve scientific problems better than previous approaches,” Bischoff explains.
Financial Times reporter Robin Wigglesworth spotlights Prof. Daron Acemoglu’s new research that predicts relatively modest productivity growth from AI advances. On generative AI specifically, Acemoglu believes that gains will remain elusive unless industry reorients “in order to focus on reliable information that can increase the marginal productivity of different kinds of workers, rather than prioritizing the development of general human-like conversational tools,” he says.