Graduate student Shayne Longpre speaks with Washington Post reporter Nitasha Tiku about the ethical and legal implications surrounding language model datasets. Longpre says “the lack of proper documentation is a community-wide problem that stems from modern machine-learning practices.”
As MIT’s fall semester was starting, President Sally Kornbluth spoke with Ben Wildavsky, host of the Higher Ed Spotlight podcast, about the importance of incorporating the humanities in STEM education and the necessity of breaking down silos between disciplines to tackle pressing issues like AI and climate change. “Part of the importance of us educating our students is they’re going to be out there in the world deploying these technologies. They’ve got to understand the implications of what they’re doing,” says Kornbluth. “Our students will find themselves in positions where they’re going to have to make decisions as to whether these technologies that were conceived for good are deployed in ways that are not beneficial to society. And we want to give them a context in which to make those decisions.”
Researchers from MIT and elsewhere have developed a transparency index used to assess 10 key AI foundation models, reports Ryan Heath for Axios. Heath writes that the researchers emphasized that “unless AI companies are more forthcoming about the inner workings, training data and impacts of their most advanced tools, users will never be able to fully understand the risks associated with AI, and experts will never be able to mitigate them.”
TechCrunch reporter Kylie Wiggers spotlights the SmartEM project by researchers from MIT and Harvard, which is aimed at enhancing lab work using, “a computer vision system and ML control system inside a scanning electron microscope” to examine a specimen intelligently. “It can avoid areas of low importance, focus on interesting or clear ones, and do smart labeling of the resulting image as well,” writes Wiggers.
Research scientist Nataliya Kosmyna speaks with The World host Chris Harland-Dunaway about the science behind using non-invasive brain scans and artificial intelligence to understand people’s different thoughts and mental images.
Tom Davenport, a visiting scholar at the MIT Initiative on the Digital Economy, writes for Forbes about how organizations are approaching generative AI. “If organizations are to succeed with generative AI, they need to increase the focus on data preparation for it, which is a primary prerequisite for success,” writes Davenport.
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.