Technology and society

Graduate student Kaylee Cunningham speaks with CBS Boston about her work using social media to help educate and inform the public about nuclear energy. Cunningham, who is known as Ms. Nuclear Energy on TikTok, recalls how as a child she was involved in musical theater, a talent she has now combined with her research interests as an engineer. She adds that she also hopes her platform inspires more women to pursue STEM careers. “You don't have to look like the stereotypical engineer,” Cunningham emphasizes.

Graduate student Sarah Gurev and her colleagues have developed a new AI system named EVEscape that can, “predict alterations likely to occur to viruses as they evolve,” reports Erin Prater for Fortune. Gurev says that with the amount of data the system has amassed, it “can make surprisingly accurate predications.”

Arvid Lunnemark '22, Michael Truell '22, Sualeh Asif '22, and Aman Sanger '22 co-founded Anysphere, a startup building an “‘AI-native’” software development environment, called Cursor,” reports Kyle Wiggers for TechCrunch. “In the next several years, our mission is to make programming an order of magnitude faster, more fun and creative,” says Truell. “Our platform enables all developers to build software faster.”

Curtis Northcutt SM '17, PhD '21, Jonas Mueller PhD '18, and Anish Athalye SB '17, SM '17, PhD '23 have co-founded Cleanlab, a startup aimed at fixing data problems in AI models, reports Alex Konrad for Forbes. “The reality is that every single solution that’s data-driven — and the world has never been more data-driven — is going to be affected by the quality of the data,” says Northcutt.

Axios reporter Alison Snyder writes about how a new study by MIT researchers finds that preconceived notions about AI chatbots can impact people’s experiences with them. Prof. Pattie Maes explains, the technology's developers “always think that the problem is optimizing AI to be better, faster, less hallucinations, fewer biases, better aligned, but we have to see this whole problem as a human-plus-AI problem. The ultimate outcomes don't just depend on the AI and the quality of the AI. It depends on how the human responds to the AI.”

MIT researchers have found that user bias can drive interactions with AI chatbots, reports Nick Hilden for Scientific American.  “When people think that the AI is caring, they become more positive toward it,” graduate student Pat Pataranutaporn explains. “This creates a positive reinforcement feedback loop where, at the end, the AI becomes much more positive, compared to the control condition. And when people believe that the AI was manipulative, they become more negative toward the AI—and it makes the AI become more negative toward the person as well.”

Prof. Thomas Kochan and Prof. Thomas Malone speak with Boston Globe reporter Hiawatha Bray about the recent deal between the Writers Guild of America and the Alliance of Motion Picture and Television Producers, which will “protect movie screenwriters from losing their jobs to computers that could use artificial intelligence to generate screenplays.” Kochan notes that when it comes to AI, “where workers don’t have a voice through a union, most companies are not engaging their workers on these issues, and the workers have no rights, no redress.”

Researchers from MIT and elsewhere have identified some of the benefits and disadvantages of generative AI when used for specific tasks, reports Paige McGlauflin and Joseph Abrams for Fortune. “The findings show a 40% performance boost for consultants using the chatbot for the creative product project, compared to the control group that did not use ChatGPT, but a 23% decline in performance when used for business problem-solving,” explain McGlauflin and Abrams.

Researchers from Atlantic Quantum, an MIT startup building quantum computers, have published new research showing “the architecture of the circuits underlying its quantum computer produces far fewer errors than the industry standard,” reports Rashi Shrivastava for Forbes.

A study by researchers from MIT and Harvard examined the potential impact of the use of AI technologies on the field of radiology, reports Laura Landro for The Wall Street Journal. “Both AI models and radiologists have their own unique strengths and areas for improvement,” says Prof. Nikhil Agarwal.

Maria Telleria ’08, SM’10, PhD ’13 speaks with Forbes contributor Stuart Anderson about her experience immigrating to the U.S. as a teenager, earning her PhD at MIT, and co-founding a company. “I don’t think I would have had these opportunities if I could not have come to the United States,” said Telleria. “I think it helped me grow by being exposed to two cultures. When you have had to think in two different ways, I think it makes you better understand other people and why they’re different. Coming to America has been an amazing opportunity.”

Prof. Regina Barzilay speaks with The Economist about how AI can help advance medicine in areas such as uncovering new drugs. With AI, “the type of questions that we will be asking will be very different from what we’re asking today,” says Barzilay.

A new study by MIT researchers demonstrates how “machine-learning systems designed to spot someone breaking a policy rule—a dress code, for example—will be harsher or more lenient depending on minuscule-seeming differences in how humans annotated data that were used to train the system,” reports Ananya for Scientific American. “This is an important warning for a field where datasets are often used without close examination of labeling practices, and [it] underscores the need for caution in automated decision systems—particularly in contexts where compliance with societal rules is essential,” says Prof. Marzyeh Ghassemi.

MIT AgeLab Director Joseph Coughlin speaks with New York Times opinion columnist Farhad Manjoo about why the tech industry tends to ignore older people. “The market is aging,” says Coughlin. “The market is numerous. The market has got more money than the people they have been building products for.”

Prof. Regina Barzilay writes for CNN about how AI can impact health care. “Smart AI algorithms, which are trained on large-scale medical data and equipped with powerful computing, can go beyond what is humanly possible, eliminate care delays and reduce the cost of health care,” writes Barzilay.