Researchers at MIT have discovered how a new computational imaging algorithm can capture user interactions through ambient light sensors commonly found in smartphones, reports Davey Winder for Forbes. “By combining the smartphone display screen, an active component, with the ambient light sense, which is passive, the researchers realized that capturing images in front of that screen was possible without using the device camera,” explains Winder.
Writing for Wired, Institute Prof. Daron Acemoglu predicts that expectations for generative AI will need to recalibrated during the year ahead. Acemoglu notes that he believes in 2024, “generative AI will have been adopted by many companies, but it will prove to be just ‘so-so automation’ of the type that displaces workers but fails to deliver huge productivity improvements.”
Writing for Wired, research scientist Kate Darling highlights the importance of addressing the fundamentally human behaviors that have been incorporated into AI chatbots. “Research in human-computer and human-robot interaction shows that we love to anthropomorphize—attribute humanlike qualities, behaviors, and emotions to—the nonhuman agents we interact with, especially if they mimic cues we recognize,” writes Darling. “And, thanks to recent advances in conversational AI, our machines are suddenly very skilled at one of those cues: language.”
Philip Adama Abel '15 co-founded Cleva, “a banking platform for African individuals and businesses to receive international payments by opening USD accounts,” reports Tage Kene-Okafor TechCrunch. “Long term, we are open to Cleva evolving from just being a product-only service to being a platform issuing APIs to do a bunch of other things that help us distribute services across other African countries or around the world,” says Abel.
MIT researchers have used machine learning to uncover the different kinds of sentences that most likely to activate the brain’s key language processing centers, reports Kyle Wiggers and Devin Coldewey for TechCrunch. The model, “was able to predict for novel sentences whether they would be taxing on human cognition or not,” they explain.
Prof. David Autor speaks with Bloomberg about the future of generative AI and the technology’s potential impact on productivity and the labor market. “When we interact with AI, we need to learn how to treat it not as authoritative, but as a guide to support decision making, and that’s really critical,” says Autor.
MIT researchers have found that job applicants who used AI to improve spelling and grammar on their resumes were eight percent more likely to be hired, reports Bryan Robinson for Forbes. “If you take two identical workers with the same skills and background, the one with the better-written resume is more likely to get hired,” explains graduate student Emma van Inwegen. “The takeaway is that employers actually care about the writing in the resume; it’s not just a correlation.”
Researchers at MIT and elsewhere developed an artificial intelligence predictive model that can be used to detect which strains of Covid-19 could become dominant and lead to a new wave of illness, reports Ruth Reader, Carmen Paun, Daniel Payne and Eric Schumaker for Politico. The model, “found three strong predictors of a dominant variant: the number of infections a strain causes in its first week relative to the number of times it appears in sequencing, the number of mutations in the spike protein, and the number of weeks since the current dominant variant began circulating,” they note.
Former Postdoc Rana el Kaliouby speaks with Boston Globe reporter Janelle Nanos about her work focused on infusing emotional intelligence into AI. “I’m an optimist, but I’m a realist as well, and I can see where this can go wrong,” says Kaliouby. “I think one of the biggest concerns around AI is bias and how we’re building bias into these models and then deploying them at scale everywhere around the world.”
Prof. Manolis Kellis speaks with Brian Bergstein of The Boston Globe’s “Say More” podcast about the potential influence of artificial intelligence on biological advances and medicine. “I would say there is no way to answer biological questions without computational science nowadays,” says Kellis. “The genome and all of the technologies that it enabled transformed biology into an information-based discipline.”
Dynamic Labs, co-founded by Itai Turbahn '11 and Yoni Goldberg '09, MEng '10, offers “tech for crypto and non-crypto companies alike to create seamless login experiences backed by digital wallets,” reports Marco Quiroz-Gutierrez for Fortune. “The firm’s wallet-based business is twofold,” explains Quiroz-Gutierrez. “It offers a customizable all-in-one service that can either push the crypto wallet technology to the background for less savvy users or put it front and center for Web3 natives.”
Prof. Manolis Kellis speaks with USA Today reporter Josh Peter about the potential impact of AI in developing undetectable performance-enhancing drugs (PEDs). "The most feasible approach would be using generative AI to alter existing PEDs that trigger drug tests in a way that makes those drugs undetectable by current testing technology,” Kellis notes.
Axios reporter Ina Fried spotlights a new report by Prof. Emeritus Stuart Madnick that finds “2.6 billion personal records have been exposed in data breaches over the past two years and that number continues to grow.” Additionally, Madnick found that” “Data breaches in the US through the first nine months of the year are already 20% higher than for all of 2022.”
Researchers from MIT and elsewhere have developed a new AI technique for teaching robots to pack items into a limited space while adhering to a range of constraints, reports Nick Hilden for Scientific American. “We want to have a learning-based method to solve constraints quickly because learning-based [AI] will solve faster, compared to traditional methods,” says graduate student Zhutian “Skye” Yang.
Writing for Politico, MIT Prof. Armando Solar-Lezama and University of Texas at Austin Prof. Swarat Chaudhuri examine the recent executive order on AI. “Especially as new ways to train models with limited resources emerge, and as the price of computing goes down,” they write, “such regulations could start hurting the outsiders — the researchers, small companies, and other independent organizations whose work will be necessary to keep a fast-moving technology in check.”