A new working paper by MIT researchers predicts “only 23% of wages linked to vision-related tasks could be feasibly cost-effectively replaced by AI,” reports Gil Press for Forbes. The researchers “argue that their findings apply also to generative AI or the automation of language-related tasks,” writes Press.
Wall Street Journal reporter Lindsey Choo spotlights Principal Research Scientist Matthias Winkenbach and his work developing an AI model to help delivery drivers find the best routes. The model would “take into consideration complex real-world constraints,” such as allowing drivers to, “choose a route that may not be the shortest but allows them to park more conveniently or unload packages in safer spaces,” writes Choo.
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.
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.”
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.”
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.
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.”
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.”
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.
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 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.”
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.”