MIT researchers have developed a new tool called “PhotoGuard” that can help protect images from AI manipulation, reports Ross Cristantiello for Boston.com. The tool “is designed to make real images resistant to advanced models that can generate new images, such as DALL-E and Midjourney,” writes Cristantiello.
A working paper co-authored by Prof. John Horton and graduate students Emma van Inwegen and Zanele Munyikwa has found that “AI has the potential to level the playing field for non-native English speakers applying for jobs by helping them better present themselves to English-speaking employers,” reports Medora Lee for USA Today. “Between June 8 and July 14, 2021, [Inwegen] studied 480,948 job seekers, who applied for jobs that require English to be spoken but who mostly lived in nations where English is not the native language,” explains Lee. “Of those who used AI, 7.8% were more likely to be hired.”
Researchers at MIT have developed “PhotoGuard,” a tool that can be used to protect images from AI manipulation, reports Catherine Thorbecke for CNN. The tool “puts an invisible ‘immunization’ over images that stops AI models from being able to manipulate the picture,” writes Thorbecke.
Boston Globe reporter Aaron Pressman speaks with alumnus Jeremy Wertheimer, co-founder of ITA Software, about the state of AI innovation in the Greater Boston area, reports Aaron Pressman for The Boston Globe. “Back in the day, we called it good old-fashioned AI,” says Wertheimer. “But the future is to forget all that clever coding. You want to have an incredibly simple program with enough data and enough computing power.”
A number of MIT alumni including Elaheh Ahmadi, Alexander Amini, and Jose Amich have been named to the Forbes 30 Under 30 Local Boston list.
Prof. Daron Acemoglu speaks with Boston Globe reporters Alex Kantrowitz and Douglas Gorman about how to address the advance of AI in the workplace. “We know from many areas that have rapidly automated that they don’t deliver the types of returns that they promised,” says Acemoglu. “Humans are underrated.”
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.”