Artificial intelligence

TechCrunch reporter Kyle Wiggers spotlights Codeium, a generative AI coding company founded by MIT alums Varun Mohan SM '17 and Douglas Chen '17. Codeium’s platform is run by generative AI models trained on public code, providing suggestions in the context of an app’s entire codebase. “Many of the AI-driven solutions provide generic code snippets that require significant manual work to integrate and secure within existing codebases,” Mohan  explains. “That’s where our AI coding assistance comes in.” 

MIT alumni Mike Ng and Nikhil Buduma founded Ambiance, which has developed an “AI-powered platform geared towards improving documentation processes in medicine,” reports Fortune’s Allie Garfinkle. “In a world filled with AI solutions in search of a problem, Ambience is focusing on a pain point that just about any doctor will attest to (after all, who likes filling out paperwork?),” writes Garfinkle. 

Writing for The Boston Globe, President Emeritus L. Rafael Reif makes the case that “without strong research universities and the scientific and technological advances they discover and invent, the United States could not possibly keep up with China.” He emphasizes that “punishing universities financially for their failings — real and imagined — would be counterproductive. If anything, the China challenge demands that universities do more than they are already doing — and that they have the resources to do so.”

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. 

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.” 

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.  

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."

Writing for Fortune, Prof. Daron Acemoglu explores the estimated scale of AI’s impact on the labor market and productivity. “The problem with the AI bubble isn’t that it is bursting and bringing the market down,” writes Acemoglu. “It’s that the hype will likely go on for a while and do much more damage in the process than experts are anticipating." 

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.” 

Prof. Jonathan Gruber joins GBH’s All Things Considered to discuss stock market jitters, AI hype and interest rates, urging calm and a long-term view. “No one who’s in the market should be overreacting to one day’s movement,” Gruber says. “These short-run reactions are really overreactions to individual bits of news.”

Prof. Simon Johnson and Prof. David Autor speak with New York Times reporter Emma Goldberg about the anticipated impact of AI on the job market. “We should be concerned about eliminating them,” says Prof. Simon Johnson, of the risks posed by automating jobs. “This is the hollowing out of the middle class.”