Prof. Pulkit Agrawal speaks with NPR Short Wave host Regina Barber and science correspondent Geoff Brumfiel about his work developing a new technique that allows robots to train in simulations of scanned home environments. “The power of simulation is that we can collect very large amounts of data,” explains Agrawal. “For example, in three hours' worth of simulation, we can collect 100 days' worth of data.”
Prof. David Sontag, Monica Agrawal PhD '23, Luke Murray SM '22, and Divya Gopinath '19, MEng '20 co-founded Layer Health - an AI healthcare startup that is applying large language models (LLMs) to help clinicians with medical chart reviews and data abstraction, reports Seth Joseph for Forbes. “The same chart review problem we’re solving with our clinical registry module is faced by clinicians at the point of care,” says Sontag. “For example, one of our next modules will focus on real-time clinical decision support to help automate clinical care pathways, leading to more reliable, high-quality care."
A new study by researchers at MIT explores how AI chatbots can impact people’s feelings and mood, reports Matthew Gault for Gizmodo. “One of the big takeaways is that people who used the chatbots casually and didn’t engage with them emotionally didn’t report feeling lonelier at the end of the study,” explains Gault. “Yet, if a user said they were lonely before they started the study, they felt worse after it was over.”
Researchers at MIT and elsewhere have found that “heavy users of ChatGPT tend to be lonelier, more emotionally dependent on the AI tool and have fewer offline social relationships,” reports Rachel Hall for The Guardian. “The researchers wrote that the users who engaged in the most emotionally expressive personal conversations with the chatbots tended to experience higher loneliness – though it isn’t clear if this is caused by the chatbot or because lonely people are seeking emotional bonds,” explains Hall.
Graduate student Cathy Fang speaks with CBS News reporter Lindsey Reiser about her research studying the effects of AI chatbots on people’s emotional well-being. Fang explains that she and her colleagues found that how the chatbot interacts with the user is important, “but also how the user interacts with the chatbot is equally important. Both influence the user’s emotional and social well-being.” She adds: “Overall, we found that extended use is correlated with more negative outcomes.”
Researchers at MIT and elsewhere have found “that frequency chatbot users experience more loneliness and emotional dependence,” reports Beatrice Nolan for Fortune. “The studies set out to investigate the extent to which interactions with ChatGPT impacted users’ emotional health, with a focus on the use of the chatbot’s advanced voice mode,” explains Nolan.
[Originally in Spanish] MIT researchers have developed a new technique to educate robots by increasing human input, reports Uriel Bederman for TN Tecno. “We can’t expect non-technical people to collect data and fine-tune a neural network model," explains graduate student Felix Yanwei Wang. "Consumers will expect the robot to work right out of the box, and if it doesn’t, they’ll want an intuitive way to customize it. That’s the challenge we’re addressing in this work."
Prof. Eric So speaks with Financial Times reporter Seb Murray about the use of AI in business programs. “I’ve seen a mixture of surprise, enthusiasm, concern and trepidation,” explains So. “It’s quite difficult to design assignments that can be done without AI. I suspect much of our curriculum will be redesigned from the ground up.”
A new proposal by graduate student Shayne Longpre and other AI researchers suggests “a new scheme supported by AI companies that gives outsiders permission to probe their models and a way to disclose flaws publicly,” reports Will Knight for Wired. “The authors suggest three main measures to improve the third-party disclosure process: adopting standardized AI flaw reports to streamline the reporting process; for big AI firms to provide infrastructure to third-party researchers disclosing flaws; and for developing a system that allows flaws to be shared between different providers,” writes Knight.
Writing for Fast Company, graduate student Sheng-Hung Lee and Devin Liddell of Teague highlight four types of AI technologies that could aid senior citizens in their homes. “To better understand how seniors want AIs and robots to help in their homes, we asked them,” they write. “We recruited seniors from the MIT AgeLab’s research cohort—each around 70 years old and in the early stages of retirement—and then engaged in wide-ranging conversations about their aspirations and fears about these technologies.”
Research Scientist Eva Ponce, director of online education for the MIT Center for Transportation & Logistics, speaks with Financial Times reporter Rafe Uddin about how companies are shifting toward automation and the impact on employees. “Companies are investing more in upskilling associates… ensuring they’re ready for a new style of work,” says Pone. “More complex tasks will still need to be done by people… These technologies are disruptive. The warehouse of the future is a combination of robotics, sensors and computer vision.”
Prof. Sara Beery speaks with TechCrunch reporter Kyle Wiggers about the use of AI tools in the advancement of science. “Most science isn’t possible to do entirely virtually — there is frequently a significant component of the scientific process that is physical, like collecting new data and conducting experiments in the lab,” explains Beery.
Principal Research Scientist Andrew McAfee co-founded Workhelix, a “tech-enabled service startup that works with enterprises to better understand and monitor AI automation at their companies,” reports Rebecca Szkutak for Tech Crunch. “Workhelix breaks down a company’s employee positions into specific job functions and tasks and scores each task for its suitability for AI adoption,” explains Szkutak. “This helps companies build roadmaps for how and where to adopt AI and gives enterprises a way to monitor if the AI they adopted is working.”
Mohamed Elrefaie speaks with Automotive World reporter Will Girling about his work developing an open-source dataset of 8,000 car designs, including their aerodynamic characteristics, which could be used to develop novel car designs in a more efficient manner. “If an automaker wants to reduce drag and improve performance, it can guide the GenAI model to produce those specific designs,” Elrefaie explains. “The standard development cycle for a design using legacy tools can take anywhere from three to five years, as it requires collaboration between many specialized departments. With AI, you could validate up to 600 designs in just one or two minutes.”
Writing for Forbes, Paula Schneider, CEO of Susan G. Komen, highlights Prof. Regina Barzilay’s research using AI to detect breast cancer. “Using her own mammograms in her research at MIT, Dr. Barzilay demonstrated how AI could have detected her breast cancer much earlier, potentially improving her prognosis,” writes Schneider. “Studies show that incorporating AI into mammogram analysis boosts cancer detection rates by 20%, without increasing false positives. This is a significant leap forward, as early detection is key to a better chance at positive outcomes and survival.”