Computer Science and Artificial Intelligence Laboratory (CSAIL)

Prof. Daniela Rus, director of CSAIL, joins Hakeem Oluseyi, host of GBH’s “Particles of Thought,” to discuss the lab’s work utilizing AI to enhance robots’ ability to move, reason, and act like humans. “The objective is to bring AI’s ability to understand text, images, and other online information to make physical machines intelligent, and if we achieve this then AI will not just reside in our computers—it will drive, it will walk it will fly, it will interact with us in the physical world.” 

Prof. Daniela Rus, director of CSAIL, joins Hakeem Oluseyi, host of GBH’s “Particles of Thought,” to discuss her group’s work developing liquid neural networks, AI that runs locally on a given device rather than a data center to increase privacy and improve energy efficiency. By using liquid AI, "you avoid privacy concerns and you avoid the security concerns associated with accessing the cloud,” says Rus. “Furthermore, the cost of running your models on a device is much, much lower than running a huge model in the cloud.” 

Writing for Forbes about efforts to improve air travel safety, Tanya Eves highlights the Air-Guardian system, an eye-tracking monitor for pilots developed by CSAIL researchers that assists when attention wavers. “In tests, it reduced flight risk and improved navigation success rates,” writes Eves. “It's a model for how the virtual co-pilot relationship should work: not replacement, but a seamless, intelligent partnership that understands when to act and when to stay silent.”

Scientific American reporter Deni Ellis Béchard spotlights graduate student Alex Zhang for the publication’s special section featuring 28 rising young scientists. “The types of research that I want to work on are things that I think should be shared for the benefit of people in general,” Zhang says of his work aimed at improving AI user experience with recursive language models. 

Writing for IEEE Spectrum, reporter Matthew S. Smith highlights Fractal, a new operating system hand-coded by CSAIL researchers to provide a clear view of security vulnerabilities. “We paved the way with techniques such as custom kernel patches and kernel extensions,” says graduate student Joseph Ravichandran. “The dream was always to have a completely custom operating system which would make these hacks unnecessary.”

Nature reporter Jyoti Madhusoodanan features Prof. Regina Barzilay and Prof. James Collin’s work developing AI tools aimed at accelerating the process of drug discovery and tackling the growing problem of antibiotic resistance. Barzilay notes that the goal of AI-based drug design is not to have the perfect method, but to find working solutions to the antibiotic-resistance crisis. “To me, the art is really in taking the tools we currently have, which are already doing quite a bit, and translating them into something which is useful in clinic,” she explains. 

Prof. Regina Barzilay speaks with WBUR’s Priyanka Dayal McCluskey about her work developing an AI risk detection tool that can analyze mammogram images and help predict risk of breast cancer before it happens or spreads. Barzilay, who describes the tool as a hi-tech weather forecast for breast health, notes that: “We really need to have tools that can help, rather than just staring at an image and trying to guess.” 

Sybil, a new AI tool developed by researchers from MIT and Mass General Brigham Cancer Institute, “analyzes a single CT scan and generates a risk score predicting the likelihood of developing lung cancer over a period of up to six years,” reports Ivan Rodriguez for WCVB-TV. “In 2023, researchers reported that Sybil achieved an accuracy rate of 86% to 94% in distinguishing high-risk patients from low-risk patients within a year.”

Fortune reporter Nick Lichtenberg highlights research by MIT economists that finds “automation doesn’t affect all parts of a job equally. The critical variable is whether the tasks being automated are the expert parts of a role or the administrative scaffolding around them.” 

A new working paper by researchers from MIT FutureTech finds that “AI’s march through the labor market looks far less like a sudden catastrophe and far more like a slow, rising flood — serious and accelerating, but not the overnight apocalypse that has dominated headlines and executive anxiety for the past two years,” writes Nick Lichtenberg for Fortune. “Rather than arriving in crashing waves that transform a certain set of tasks at a time,” the researchers write, “progress typically resembles a rising tide, with widespread gains across many tasks simultaneously.”

Researchers at MIT have “developed a new technique that enables large language models to learn new skills and knowledge without forgetting their past capabilities,” reports Ben Dickson for Venture Beat. “Their technique, called self-distillation fine-tuning (SDFT), allows models to learn directly from demonstrations and their own experiments by leveraging the inherent in-context learning abilities of modern LLMs,” explains Dickson. “Experiments show that SDFT consistently outperforms traditional supervised fine-tuning (SFT) while addressing the limitations of reinforcement learning algorithms.” 

Prof. Andrew Lo speaks with Wall Street Journal reporter Peter Coy about why he feels current AI systems aren’t suited to serving as financial advisors and his goal to create “an AI financial adviser that is a true fiduciary—namely, an entity that always puts the client’s interests first and tailors its advice to their particular needs, including emotional needs.” Lo notes that: “The AI people are using now can be dangerous, especially if the user isn’t fully aware of the biases, inaccuracies and other limits” of large language models. 

Prof. David Karger speaks with GBH’s Morning Edition host Mark Herz about the rapid development of new AI tools, the need for generative AI regulation, and the importance of transparency when it comes to AI-generated content. "I think we need to involve more entities, more people, more sources in the fact-checking process,” says Karger. “We need to figure out how to ensure that the fact checking can propagate into the platforms, even though the platforms are not doing the fact checking themselves.” 

Graduate student Stephen Casper speaks with Wired reporter Matt Burgess about the rise of “deepfake video abuse and its role in nonconsensual intimate imagery generation.” “This ecosystem is built on the back of open-source models,” says Casper. “Oftentimes it’s just an open-source model that has been used to develop an app that then a user uses.” 

Forbes reporter Craig Smith spotlights Prof. Regina Barzilay for her work using her personal health experience to develop transformative medical technology. In response to her breast cancer diagnosis, Barzilay “developed a deep learning model that analyzes mammography images to predict breast cancer risk up to five years in advance,” writes Smith.