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.”
MIT CSAIL researchers have developed a new air safety system, called Air-Guardian, that is designed to serve as a “proactive co-pilot, enhancing safety during critical moments of flight,” reports Jace Dela Cruz for Tech Times.
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.
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.
Prof. Eric Klopfer, co-director of the RAISE initiative (Responsible AI for Social Empowerment in Education), speaks with GBH reporter Diane Adame about the importance of providing students guidance on navigating artificial intelligence systems. “I think it's really important for kids to be aware that these things exist now, because whether it's in school or out of school, they are part of systems where AI is present,” says Klopfer. “Many humans are biased. And so the [AI] systems express those same biases that they've seen online and the data that they've collected from humans.”
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.
Research scientist Bryan Reimer speaks with The Ojo-Yoshida Report host Junko Yoshida about the future of the autonomous vehicle industry. “We cannot let the finances drive here,” explains Reimer. “We need to manage the finances to let society win over the long haul.”
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.
Forbes reporter Rob Toews spotlights Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani and their work with liquid neural networks. “The ‘liquid’ in the name refers to the fact that the model’s weights are probabilistic rather than constant, allowing them to vary fluidly depending on the inputs the model is exposed to,” writes Toews.
Researchers at MIT and elsewhere have used artificial intelligence to develop a new antibiotic to combat Acinetobacter baumannii, a challenging bacteria known to become resistant to antibiotics, reports Hannah Kuchler for the Financial Times. “It took just an hour and a half — a long lunch — for the AI to serve up a potential new antibiotic, an offering to a world contending with the rise of so-called superbugs: bacteria, viruses, fungi and parasites that have mutated and no longer respond to the drugs we have available,” writes Kuchler.