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.”
Prof. Zeynep Ton speaks with Marketplace host Meghan McCarty Carino about the impact of automation, such as self-service kiosks or chatbot customer service agents, on retail shopping. When thinking about self checkout stations and chatbots, Ton recommends companies evaluate whether the technologies can “improve value for the customer? And would this improve productivity for employees and make their jobs better so that they can serve the customers much better too.”
Boston Globe reporters Aaron Pressman and Jon Chesto spotlight Liquid AI, a new startup founded by MIT researchers that is developing an AI system that relies on neural-network models that are “much simpler and require significantly less computer power to train and operate” than generative AI systems. “You need a fraction of the cost of developing generative AI, and the carbon footprint is much lower,” explains Liquid AI CEO Ramin Hasani, a research affiliate at CSAIL. “You get the same capabilities with a much smaller representation.”
Prof. Daron Acemoglu speaks with CNBC about the potential impact of AI in the workplace. “I think the incentive in the industry… especially with the idea that you have to dominate the market by becoming the largest players, I think those are not helping because those are making us rush down the easiest road, the lowest resistance path, which is often automation,” says Acemoglu. “I don’t think that is going to get us the kind of aspirations that are articulated where we can make blue collar workers, electricians, nurses, teachers much more capable because we have given them tools to be better workers and to make much higher quality services.”
Prof. Daniela Rus, director of CSAIL, and research affiliates Ramin Hasani, Mathias Lechner, and Alexander Amini have co-founded Liquid AI, a startup building a general-purpose AI system powered by a liquid neural network, reports Kyle Wiggers for TechCrunch. “Accountability and safety of large AI models is of paramount importance,” says Hasani. “Liquid AI offers more capital efficient, reliable, explainable and capable machine learning models for both domain-specific and generative AI applications."
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.