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Prof. Regina Barzilay has received the WebMD Health Heros award for her work developing a new system that uses AI to detect breast cancer up to 5 years earlier, reports WHDH. “We do have a right to know our risk and then we, together with our healthcare providers, need to manage them,” says Barzilay.
Writing for The Washington Post, Prof. Daniela Rus, director of CSAIL, and Nico Enriquez, a graduate student at Stanford, make the case that the United States should not only be building more efficient AI software and better computer chips, but also creating “interstate-type corridors to transmit sufficient, reliable power to our data centers.” They emphasize: “The United States has the talent, investor base, corporations and research institutions to write the most advanced AI models. But without a powerful data highway system, our great technology advances will be confined to back roads.”
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.”
In an article for Forbes, Robert Clark spotlights how MIT researchers developed a new model to predict irrational behaviors in humans and AI agents in suboptimal conditions. “The goal of the study was to better understand human behavior to improve collaboration with AI,” Clark writes.
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.
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.
MIT researchers have developed an AI risk repository that includes over 70 AI risks, reports Kyle Wiggers for TechCrunch. “This is an attempt to rigorously curate and analyze AI risks into a publicly accessible, comprehensive, extensible and categorized risk database that anyone can copy and use, and that will be kept up to date over time,” explains Peter Slattery, a research affiliate at the MIT FutureTech project.
Prof. Regina Barzilay joins BBC host Caroline Steel and other AI experts to discuss her inspiration for applying AI technologies to help improve medicine and fight cancer. “I think that in cancer and in many other diseases, the big question is always, how do you deal with uncertainty? It's all the matter of predictions," says Barzilay. "Unfortunately, today, we rely on humans who don't have this capacity to make predictions. As a result, many times people get wrong treatments or they are diagnosed much later.”
In an excerpt from her new book, “The Mind’s Mirror: Risk and Reward in the Age of AI," Prof. Daniela Rus, director of CSAIL, addresses the fear surrounding new AI technologies, while also exploring AI’s vast potential. “New technologies undoubtedly disrupt existing jobs, but they also create entirely new industries, and the new roles needed to support them,” writes Rus.
Research Scientist Mathieu Huot speaks with Tech Briefs reporter Andrew Corselli about his work with GenSQL, a generative AI system for databases that “could help users make predictions, detect anomalies, guess missing values, fix errors, or generate synthetic data with just a few keystrokes.”
MIT researchers have developed “a robotic system that can rotate different types of fruit and vegetable using its fingers on one hand, while the other arm is made to peel,” reports Alex Wilkins for New Scientist. “These additional steps of doing rotation are something which is very straightforward to humans, we don’t even think about it,” Prof. Pulkit Agrawal. “But for a robot, this becomes challenging.”
Prof. Li-Huei Tsai, director of the Picower Institute, speaks with NPR host Jon Hamilton about her work identifying a protein called reelin that appears to protect brain cells from Alzheimer's. “Tsai says she and her team are now using artificial intelligence to help find a drug that can replicate what reelin does naturally,” says Hamilton.
Researchers at MIT have developed a new method for “training home robots in simulation,” reports Brain Heater for TechCrunch. “Simulation has become a bedrock element of robot training in recent decades,” explains Heater. “It allows robots to try and fail at tasks thousands — or even millions — of times in the same amount of time it would take to do it once in the real world.”
Prof. Armando Solar-Lezama speaks with The Wall Street Journal reporter Isabelle Bousquette about large language models (LLMs) in academia. Instead of building LLMs from scratch, Solar-Lezama suggests “students and researchers are focused on developing applications and even creating synthetic data that could be used to train LLMs,” writes Bousquette.
MIT scientists have created RoboGrocery, a robot prototype that can pack a bag of standard groceries, reports Mack DeGeurin for Popular Science. Using an RGB-D camera equipped with computer vision technology and grippers with pressure sensors, RoboGrocery’s “ability to assess items, determine their delicacy, and pack efficiently without causing damage sets it apart from conventional robotic packers,” explains Prof. Daniela Rus.