Artificial intelligence

Writing for The Boston Globe, MIT Prof. Carlo Ratti and Harvard Prof. Antoine Picon examine AI and the future of cities, noting that their research has shown “once trained, visual AI is shockingly accurate at predicting property values, crime rates, and even public health outcomes — just by analyzing photos.” They add: “Tireless, penetrating artificial eyes are coming to our streets, promising to show us things we have never seen before. They will be incredible tools to guide us — but only if we keep our own eyes open.”

Prof. Regina Barzilay speaks with The Economist about how AI can help advance medicine in areas such as uncovering new drugs. With AI, “the type of questions that we will be asking will be very different from what we’re asking today,” says Barzilay.

President Sally Kornbluth joined The Boston Globe’s Shirley Leung on her Say More podcast to discuss the future of AI, ethics in science, and climate change. “I view [the climate crisis] as an existential issue to the extent that if we don’t take action there, all of the many, many other things that we’re working on, not that they’ll be irrelevant, but they’ll pale in comparison,” Kornbluth says.

Prof. Max Tegmark has been named to TIME’s list of the 100 most influential people in AI. “Our best course of action is to follow biotech’s example, and ensure that potentially dangerous products need to be approved by AI-experts at an AI [version of the] FDA before they can be launched,” says Tegmark of how government should regulate the development of AI. “More than 60% of Americans support such an approach.”

A new study by MIT researchers demonstrates how “machine-learning systems designed to spot someone breaking a policy rule—a dress code, for example—will be harsher or more lenient depending on minuscule-seeming differences in how humans annotated data that were used to train the system,” reports Ananya for Scientific American. “This is an important warning for a field where datasets are often used without close examination of labeling practices, and [it] underscores the need for caution in automated decision systems—particularly in contexts where compliance with societal rules is essential,” says Prof. Marzyeh Ghassemi.

Prof. Regina Barzilay writes for CNN about how AI can impact health care. “Smart AI algorithms, which are trained on large-scale medical data and equipped with powerful computing, can go beyond what is humanly possible, eliminate care delays and reduce the cost of health care,” writes Barzilay.

Forbes reporter Rob Toews spotlights Prof. Daniela Rus, director of CSAIL, and research affiliate Ramin Hasani and their work with liquid neural networks. “The ‘liquid’ in the name refers to the fact that the model’s weights are probabilistic rather than constant, allowing them to vary fluidly depending on the inputs the model is exposed to,” writes Toews.

Prof. Tod Machover speaks with Boston Globe reporter A.Z. Madonna about the restaging of his opera ‘VALIS’ at MIT, which features an AI-assisted musical instrument developed by Nina Masuelli ’23.  “In all my career, I’ve never seen anything change as fast as AI is changing right now, period,” said Machover. “So to figure out how to steer it towards something productive and useful is a really important question right now.”

Research fellow Michael Schrage speaks with Fortune reporter Sheryl Estrada about generative AI’s role in the digital economy.  “If you truly understand and structure your use cases for generative AI correctly, there’s much less risk associated with the investment,” says Schrage.

Researchers at MIT and elsewhere have developed a medical device that uses AI to evade scar tissue build up, reports Andrew Paul for Popular Science. “The technology’s secret weapon is its conductive, porous membrane capable of detecting when it is becoming blocked by scar tissue,” writes Paul. 

Prof. Simon Johnson speaks with Freakonomics guest host Adam Davidson about his new book, economic history, and why new technologies impact people differently. “What do people creating technology, deploying technology— what exactly are they seeking to achieve? If they’re seeking to replace people, then that’s what they’re going to be doing,” says Johnson. “But if they’re seeking to make people individually more productive, more creative, enable them to design and carry out new tasks — let’s push the vision more in that direction. And that’s a naturally more inclusive version of the market economy. And I think we will get better outcomes for more people.”

Researchers at MIT and elsewhere have used artificial intelligence to develop a new antibiotic to combat Acinetobacter baumannii, a challenging bacteria known to become resistant to antibiotics, reports Hannah Kuchler for the Financial Times. “It took just an hour and a half — a long lunch — for the AI to serve up a potential new antibiotic, an offering to a world contending with the rise of so-called superbugs: bacteria, viruses, fungi and parasites that have mutated and no longer respond to the drugs we have available,” writes Kuchler.

Prof. Yoon Kim speaks with Popular Science reporter Charlotte Hu about how large language models like ChatGPT operate. “You can think of [chatbots] as algorithms with little knobs on them,” says Kim. “These knobs basically learn on data that you see out in the wild,” allowing the software to create “probabilities over the entire English vocab.”

Principal Research Scientist Kalyan Veeramachaneni speaks with Fast Company reporter Sam Becker about his work in developing the Synthetic Data Vault, which is helpful for creating synthetic data sets, reports Sam Becker for Fast Company. “Fake data is randomly generated,” says Veeramachaneni. “While synthetic data is trying to create data from a machine learning model that looks very realistic.”

Researchers from MIT and Harvard have explored astrocytes, a group of brain cells, from a computational perspective and developed a mathematical model that shows how they can be used to build a biological transformer, reports Kyle Wiggers for TechCrunch. “The brain is far superior to even the best artificial neural networks that we have developed, but we don’t really know exactly how the brain works,” says research staff member Dmitry Krotov. “There is scientific value in thinking about connections between biological hardware and large-scale artificial intelligence networks. This is neuroscience for AI and AI for neuroscience.