A new study by researchers from MIT and elsewhere tested a generative AI chatbot’s ability to debunk conspiracy theories , reports Mack Degeurin for Popular Science. “In the end, conversations with the chatbot reduced the participant’s overall confidence in their professed conspiracy theory by an average of 20%,” writes Degeurin.
A new study by researchers from MIT and elsewhere has found that an AI chatbot is capable of combating conspiracy theories, reports Karen Kaplan for The Los Angeles Times. The researchers found that conversations with the chatbot made people “less generally conspiratorial,” says Prof. David Rand. “It also increased their intentions to do things like ignore or block social media accounts sharing conspiracies, or, you know, argue with people who are espousing those conspiracy theories.”
A new chatbot developed by MIT researchers aimed at persuading individuals to stop believing unfounded conspiracy theories has made “significant and long-lasting progress at changing people’s convictions,” reports Teddy Rosenbluth for The New York Times. The chatbot, dubbed DebunkBot, challenges the “widely held belief that facts and logic cannot combat conspiracy theories.” Professor David Rand explains: “It is the facts and evidence themselves that are really doing the work here.”
A new study by Prof. David Rand and his colleagues has found that chatbots, powered by generative AI, can help people abandon conspiracy theories, reports Rebecca Ruiz for Mashable. “Rand and his co-authors imagine a future in which a chatbot might be connected to social media accounts as a way to counter conspiracy theories circulating on a platform,” explains Ruiz. “Or people might find a chatbot when they search online for information about viral rumors or hoaxes thanks to keyword ads tied to certain conspiracy search terms.”
Researchers from MIT and elsewhere have created an AI Risk Repository, a free retrospective analysis detailing over 750 risks associated with AI, reports Tor Constantino for Forbes. “If current understanding is fragmented, policymakers, researchers, and industry leaders may believe they have a relatively complete shared understanding of AI risks when they actually don’t,” says Peter Slattery, a research affiliate at the MIT FutureTech project. “This sort of misconception could lead to critical oversights, inefficient use of resources, and incomplete risk mitigation strategies, which leave us more vulnerable.”
Prof. Stuart Madnick speaks with CNBC reporter Cheryl Winokur Munk about the growing risk of "malvertising" – a new cyberattack technique that places online ads for malicious purposes. “You see something appearing on a Google search, you kind of assume it is something valid,” says Madnick. “You should assume that this could happen to you no matter how careful you are.”
Forbes contributor Peter High spotlights research by Senior Research Scientist Peter Weill, covering real-time decision-making, the importance of digitally savvy leadership and the potential of generative AI. High notes Weill’s advice to keep up. “The gap between digitally advanced companies and those lagging is widening, and the consequences of not keeping pace are becoming more severe. ‘You can’t get left behind on being real time,’ he warned.”
Researchers from MIT and Northwestern University have developed some guidelines for how to spot deepfakes, noting “there is no fool-proof method that always works,” reports Jeremy Hsu for New Scientist.
Senior lecturer Paul McDonagh-Smith speaks with Forbes reporter Joe Mckendrick about the history behind the AI hype cycle. “While AI technologies and techniques are at the forefront of today’s technological innovation, it remains a field defined — as it has from the 1950s — by both significant achievements and considerable hype," says McDonagh-Smith.
After meeting at MIT, alumni Honghao Deng and Jiani Zeng founded Butr, which makes anonymous people-detecting sensors to measure movement inside buildings, reports Zoya Hasan for Forbes. The sensors could help address staffing challenges in senior living communities, and alert staff of falls or other medical issues.
Prof. William Deringer speaks with David Westin on Bloomberg’s Wall Street Week about the power of early spreadsheet programs in the 1980s financial services world. When asked to compare today’s AI in the context of workplace automation fears, he says “one thing we know from the history of technology - and certainly the history of calculation tools that I like to study – is that the automation of some of these calculations…doesn’t necessarily lead to less work.”
Researchers at MIT have developed “a publicly available database, culled from reports, journals, and other documents to shed light on the risks AI experts are disclosing through paper, reports, and other documents,” reports Jon McKendrick for Forbes. “These benchmarked risks will help develop a greater understanding the risks versus rewards of this new force entering the business landscape,” writes McKendrick.
Edwin Olson '00, MEng '01, PhD '08 founded May Mobility, an autonomous vehicle company that uses human autonomous vehicle operators on its rides, reports Gus Alexiou for Forbes. “May Mobility is focused above all else on gradually building up the confidence of its riders and community stakeholders in the technology over the long-term,” explains Alexiou. “This may be especially true for certain more vulnerable sections of society such as the disability community where the need for more personalized and affordable forms of transportation is arguably greatest but so too is the requirement for robust safety and accessibility protocols.”
A new database of AI risks has been developed by MIT researchers in an effort to help guide organizations as they begin using AI technologies, reports Will Knight for Wired. “Many organizations are still pretty early in that process of adopting AI,” meaning they need guidance on the possible perils, says Research Scientist Neil Thompson, director of the FutureTech project.
TechCrunch reporter Kyle Wiggers writes that MIT researchers have developed a new tool, called SigLLM, that uses large language models to flag problems in complex systems. In the future, SigLLM could be used to “help technicians flag potential problems in equipment like heavy machinery before they occur.”