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Forbes

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.

Tech Briefs

Javier Ramos '12, SM '14, co-founder of InkBit, and his colleagues have developed a, “3D inkjet printer that uses contact-free computer vision feedback to print hybrid objects with a broad range of new functional chemistries,” reports Ed Brown for Tech Briefs. “Our vision for Inkbit is to reshape how the world thinks about production, from design to execution and make our technology readily available,” says Ramos. “The big opportunity with 3D printing is how to disrupt the world of manufacturing — that’s what we're focused on.”

Wired

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.”

Axios

Graduate student Zhichu Ren has developed CRESt (Copilot for Real-World Experimental Scientist), a lab assistant which “suggests experiments, retrieves data, manages equipment and guides research to the next steps in an experiment,” reports Ryan Heath for Axios.

Wired

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.”

TechCrunch

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.

Bloomberg

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.

Politico

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.

Fierce Biotech

In a new paper, MIT researchers detail how they have used AI techniques to discover a class of “of antibiotics capable of killing methicillin-resistant Staphylococcus aureus (MRSA),” reports Helen Floresh for Fierce Biotech. “This paper announces the first AI-driven discovery of a new class of small molecule antibiotics capable of addressing antibiotic resistance, and one of the few to have been discovered overall in the past 60 years,” says postdoctoral fellow Felix Wong.

New Scientist

Researchers at MIT have used artificial intelligence to uncover, “a new class of antibiotics that can treat infections caused by drug-resistant bacteria,” reports Jeremy Hsu for New Scientist. “Our [AI] models tell us not only which compounds have selective antibiotic activity, but also why, in terms of their chemical structure,” says postdoctoral fellow Felix Wong.

Fortune

Fortune reporter Trey Williams spotlights alumnus Alexandr Wang, co-founder of Scale AI, a “software company that tags text, images, and videos to help companies improve the data used to train AI algorithms.”

USA Today

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.