A study by MIT researchers shows that “workers have cost employers a 25% tax rate, while the rate of software and equipment has stood around 5%,” write Diego Areas Munhoz and Samantha Handler for Bloomberg. “This lopsidedness in tax code gives employers more reason to invest in automating goods like machines and computer software instead of workers.”
Science reporter Robert F. Service spotlights how Prof. Kevin Esvelt is sounding the alarm that “AI could help somebody with no science background and evil intentions design and order a virus capable of unleashing a pandemic.”
“Power and Progress,” a new book by Institute Prof. Daron Acemoglu and Prof. Simon Johnson, has been named one of the best new books on economics by the Financial Times. “The authors’ nuanced take on technological development provides insights on how we can ensure the coming AI revolution leads to widespread benefits for the many, not just the tech bros,” writes Tej Parikh.
Writing for The New York Times, Institute Prof. Daron Acemoglu and Prof Simon Johnson make the case that “rather than machine intelligence, what we need is ‘machine usefulness,’ which emphasizes the ability of computers to augment human capabilities. This would be a much more fruitful direction for increasing productivity. By empowering workers and reinforcing human decision making in the production process, it also would strengthen social forces that can stand up to big tech companies.”
New York Times reporter Natasha Singer spotlights the Day of AI, an MIT RAISE program aimed at teaching K-12 students about AI. “Because AI is such a powerful new technology, in order for it to work well in society, it really needs some rules,” said MIT President Sally Kornbluth. Prof. Cynthia Breazeal, MIT’s dean of digital learning, added: “We want students to be informed, responsible users and informed, responsible designers of these technologies.”
Graduate student Kartik Chandra writes for Inside Higher Education about how many of this year’s college graduates are feeling anxiety about new AI technologies. “We scientists are still debating the details of how AI is and is not humanlike in its use of language,” writes Chandra. “But let’s not forget the big picture: unlike AI, you speak because you have something to say.”
Prof. Danielle Li and graduate student Lindsey Raymond speak with NPR hosts Wailin Wong and Adrian Ma about how generative artificial intelligence could impact the workplace based on their research examining how an AI chatbot affected workers at customer contact centers. “A lot of what customer service is, is about managing people's feelings 'cause people come, they're tired or whatever,” says Li. “And so in some sense there's kind of this sort of human soft skills component that these technologies are able to capture in a way that prior technologies couldn't.”
Institute Prof. Daron Acemoglu and Prof. Aleksander Mądry join GBH’s Greater Boston to explore how AI can be regulated and safely integrated into our lives. “With much of our society driven by informational spaces — in particular social media and online media in general — AI and, in particular, generative AI accelerates a lot of problems like misinformation, spam, spear phishing and blackmail,” Mądry explains. Acemoglu adds that he feels AI reforms should be approached “more broadly so that AI researchers actually work in using these technologies in human-friendly ways, trying to make humans more empowered and more productive.”
Prof. Daron Acemoglu speaks with VOX Talks host Tim Phillips about his new book written with Prof. Simon Johnson, “Power and Progress.” The book explores “how we can redirect the path of innovation,” Phillips explains.
MIT researchers have developed a new method to make chatbots more factual, reports Gerrit De Vynck for The Washington Post. “The researchers proposed using different chatbots to produce multiple answers to the same question and then letting them debate each other until one answer won out,” explains Vynck. “The researchers found using this ‘society of minds’ method made them more factual.”
MIT researchers have developed a new machine-learning technique that can identify which pixels in an image represent the same material, which could help with robotic scene understanding, reports Kyle Wiggers for TechCrunch. “Since an object can be multiple materials as well as colors and other visual aspects, this is a pretty subtle distinction but also an intuitive one,” writes Wiggers.
Researchers from MIT and McMaster University have used artificial intelligence to identify a new antibiotic that can fight against a drug-resistant bacteria commonly found in hospitals and medical offices, reports Ken Alltucker for USA Today. The researchers believe the AI “process used to winnow thousands of potential drugs to identify one that may work is an approach that can work in drug discovery,” writes Alltucker.
Researchers from MIT and elsewhere have used artificial intelligence to develop a new antibiotic to address Acinetobacter baumannii, a bacteria known for infecting wounds, lungs and kidneys, reports Harland-Dunaway for The World.
Using a machine-learning algorithm, researchers from MIT and McMaster University have discovered a new type of antibiotic that works against a type of drug-resistant bacteria, reports Brenda Goodman for CNN. Goodman notes that the compound “worked in a way that stymied only the problem pathogen. It didn’t seem to kill the many other species of beneficial bacteria that live in the gut or on the skin, making it a rare narrowly targeted agent.”
Researchers from MIT and McMaster University used a machine-learning algorithm to identify a new antibiotic that can treat a bacteria that causes deadly infections, reports Maya Yang for The Guardian. The researchers used an “AI algorithm to screen thousands of antibacterial molecules in an attempt to predict new structural classes. As a result of the AI screening, researchers were able to identify a new antibacterial compound which they named abaucin,” writes Yang.