Machine learning

Prof. Kevin Esvelt and his students have found that language-generating AI models could make it easier to create pandemic potential pathogens, reports Kelsey Piper for Vox.

MIT researchers and an undergraduate class found that chatbots could be prompted to suggest pandemic pathogens, including specific information not commonly known among experts, reports Ryan Health for Axios. The MIT researchers recommend "pre-release evaluations of LLMs by third parties, curating training datasets to remove harmful concepts, and verifiably screening all DNA generated by synthesis providers or used by contract research organizations."

Writing for The Conversation, postdoc Ziv Epstein SM ’19, PhD ’23, graduate student Robert Mahari and Jessica Fjeld of Harvard Law School explore how the use of generative AI will impact creative work. “The ways in which existing laws are interpreted or reformed – and whether generative AI is appropriately treated as the tool it is – will have real consequences for the future of creative expression,” the authors note.  

Researchers at MIT have developed a new artificial intelligence system aimed at helping autopilot avoid obstacles while maintaining a desirable flight path, reports Kyle Wiggers for TechCrunch. “Any old algorithm can propose wild changes to direction in order to not crash, but doing so while maintaining stability and not pulping anything inside is harder,” writes Wiggers.

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.” 

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.”

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.”