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.
Prof. Daron Acemoglu speaks with NPR Planet Money hosts Greg Rosalsky and Darian Woods about the anticipated economic impacts of generative AI. Acemoglu notes he believes AI is overrated because humans are underrated. "A lot of people in the industry don't recognize how versatile, talented, multifaceted human skills and capabilities are," Acemoglu says. "And once you do that, you tend to overrate machines ahead of humans and underrate the humans."
MIT researchers have found that “when nudged to review LLM-generated outputs, humans are more likely to discover and fix errors,” reports Carter Busse for Forbes. The findings suggest that, “when given the chance to evaluate results from AI systems, users can greatly improve the quality of the outputs,” explains Busse. “The more information provided about the origins and accuracy of the results, the better the users are at detecting problems.”
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.”
Intelmatix, an AI startup founded by by Almaha Almalki MS '18, Anas Alfaris MS '09, PhD '09 and Ahmad Alabdulkareem PhD '18, aims to provide businesses in the Middle East and North Africa with access to AI for decision-making, reports Annie Njanja for TechCrunch. . “The idea of democratizing access to AI has always been something that we’ve been very passionate about,” says Alfaris.
Prof. Sherry Turkle shares the benefits of being polite when interacting with AI technologies, reports Webb Wright for Scientific American, underscoring the risks of becoming habituated to using crass, disrespectful and dictatorial language. “We have to protect ourselves,” says Turkle. “Because we’re the only ones that have to form relationships with real people.”
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.”
Forbes reporter Rodger Dean Duncan spotlights “The Skill Code: How to Save Human Ability in an Age of Intelligent Machines,” a new book by Research Affiliate Matt Bean SM '14, PhD '17. Duncan “explains Beane’s take on AI tools, collaboration and remote work, who suggests traditional mentoring is at risk in the workplace. Beane says today’s successful people have ‘discovered new tactics that others can use to get skills without throwing out the benefits of hybrid working arrangements.’”
Researchers from the Data Provenance Initiative, a research group led by MIT engineers, have found that “important web sources used for training AI models have restricted the use of their data,” reports Kevin Roose for The New York Times. “We’re seeing a rapid decline in consent to use data across the web that will have ramifications not just for A.I. companies, but for researchers, academics and noncommercial entities,” explains graduate student Shayne Longpre.
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.
Using multimodal sensing and a soft robotic manipulator, MIT scientists have developed an automated system, called RoboGrocery, that can pack groceries of different sizes and weights, reports Brian Heater for TechCrunch. Heater explains that as the soft robotic gripper touches an item, “pressure sensors in the fingers determine that they are, in fact, delicate and therefore should not go at the bottom of the bag — something many of us no doubt learned the hard way. Next, it notes that the soup can is a more rigid structure and sticks it in the bottom of the bag.”
Prof. Yoon Kim speaks with Vox reporter Adam Clark Estes on how to address hallucinations and misinformation within large language models. “I don't think we'll ever be at a stage where we can guarantee that hallucinations won't exist,” says Kim. “But I think there's been a lot of advancements in reducing these hallucinations, and I think we'll get to a point where they'll become good enough to use.”
Researchers from MIT have developed RoboGrocery, a soft robotic system that “can determine how to pack a grocery item based on its weight, size and shape without causing damage to the item,” reports Jennifer Kite-Powell for Forbes. “This is more than just automation—it's a paradigm shift that enhances precision, reduces waste and adapts seamlessly to the diverse needs of modern retail logistics,” says Prof. Daniela Rus, director of CSAIL.
Writing for Fast Company, Moshe Tanach highlights how researchers from the MIT Lincoln Laboratory Supercomputing Center are developing new technologies to reduce AI energy costs, such as power-capping hardware and tools that can halt AI training.
MIT scientists have developed a “four-fingered robotic hand which is capable of rotating balls and toys in any direction and orientation,” reports Maisie Lillywhite for BBC News. “The improvement in dexterity could have significant implications for automating tasks such as handling goods for supermarkets or sorting through waste for recycling,” Lillywhite writes.