Artificial intelligence

Prof. Simon Johnson speaks with Reuters reporter Mark John about the impact of AI on the economy. “AI has got a lot of potential – but potential to go either way,” says Johnson. “We are at a fork in the road.”

Researchers at MIT and Dana-Farber Cancer Institute have published a paper showcasing the development of OncoNPC, an artificial intelligence model that can predict where a patient’s cancer came from in their body, reports Tony Ho Tran for The Daily Beast. This information “can help determine more effective treatment decisions for patients and caregivers,” writes Tran.

Prof. Carlo Ratti writes for Financial Times about how new AI algorithms can impact the property market. “To train a real estate bot, our lab at MIT used pictures of 20,000 houses around Boston, as well as data that measured how their prices changed over time,” write Ratti. “When other variables were added — such as structural information and neighbourhood amenities — our algorithm was able to make very accurate predictions of how prices would change over time.”

Prof. Manish Raghavan speaks with The Washington Post reporter Danielle Abril about the risk of AI bias in employers’ recruitment behavior. “For example, AI could appear to be biased in matching mostly Harvard graduates to some jobs when those graduates may just have a higher likelihood to match certain requirements,” explains Abril. “Humans already struggle with implicit biases, often favoring people like themselves, and that could get replicated through AI.”

At CSAIL’s Imagination in Action event, Prof. Stefanie Jegelka’s presentation provided insight into “the failures and successes of neural networks and explored some crucial context that can help engineers and other human observers to focus in on how learning is happening,” reports research affiliate John Werner for Forbes.

Prof. Jacob Andreas explored the concept of language guided program synthesis at CSAIL’s Imagination in Action event, reports research affiliate John Werner for Forbes. “Language is a tool,” said Andreas during his talk. “Not just for training models, but actually interpreting them and sometimes improving them directly, again, in domains, not just involving languages (or) inputs, but also these kinds of visual domains as well.”

Prof. Daniela Rus, director of CSAIL, writes for Forbes about Prof. Dina Katabi’s work using insights from wireless systems to help glean information about patient health. “Incorporating continuous time data collection in healthcare using ambient WiFi detectable by machine learning promises an era where early and accurate diagnosis becomes the norm rather than the exception,” writes Rus.

Researchers from MIT and Massachusetts General Hospital have developed “Sybil,” an AI tool that can detect the risk of a patient developing lung cancer within six years, reports Mary Kekatos for ABC News. “Sybil was trained on low-dose chest computer tomography scans, which is recommended for those between ages 50 and 80 who either have a significant history of smoking or currently smoke,” explains Kekatos.

Prof. Daniela Rus, director of CSAIL, emphasizes the central role universities play in fostering innovation and the importance of ensuring universities have the computing resources necessary to help tackle major global challenges. Rus writes, “academia needs a large-scale research cloud that allows researchers to efficiently share resources” to address hot-button issues like generative AI. “It would provide an integrated platform for large-scale data management, encourage collaborative studies across research organizations, and offer access to cutting-edge technologies, while ensuring cost efficiency,” Rus explains.

During her talk at CSAIL’s Imagination in Action event, Prof. Daniela Rus, director of CSAIL, explored the promise of using liquid neural networks “to solve some of AI’s notorious complexity problems,” writes research affiliate John Werner for Forbes. “Liquid networks are a new model for machine learning,” said Rus. “They're compact, interpretable and causal. And they have shown great promise in generalization under heavy distribution shifts.”

In an article for Forbes, research affiliate John Werner spotlights Prof. Dina Katabi and her work showcasing how AI can boost the capabilities of clinical data. “We are going to collect data, clinical data from patients continuously in their homes, track the symptoms, the evolution of those symptoms, and process this data with machine learning so that we can get insights before problems occur,” says Katabi.

A study by researchers from MIT and Stanford University explored how customer service agents use generative AI chatbots in their response to tech support questions, reports Meghan McCarty for Marketplace. “The most experienced workers see no to even small negative effects of the AI being turned on for them, while the less-experienced and least-productive workers see on average 30 to 35% improvements in productivity,” explains graduate student Lindsey Raymond.

Forbes reporter Stuart Anderson spotlights a number of international students who became founders of top U.S. AI companies, including MIT alumni Sébastien Boyer MS '16 and Aditya Khosla PhD '16. Boyer co-founded “FarmWise, which employs AI for precision weeding on farms,” and Khosla co-founded PathAI, a biotech startup that uses AI to “optimize the analysis of patient tissue samples and for other clinical and diagnostic purposes,” writes Anderson.

Prof. Regina Barzilay speaks with Nicole Estephan of WCVB-TV’s Chronicle about her work developing new AI systems that could be used to help diagnose breast and lung cancer before the cancers are detectable to the human eye.

In conversation with Matthew Huston at Science, Prof. John Horton discusses the possibility of using chatbots in research instead of humans. As he explains, a change like that would be similar to the transition from in-person to online surveys, ““People were like, ‘How can you run experiments online? Who are these people?’ And now it’s like, ‘Oh, yeah, of course you do that.’”