Artificial intelligence

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.

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. 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.

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.

Postdoctoral associate Wen Shuhao and postdoctoral fellows Ma Jian and Lai Lipeng co-founded Xtalpi, a biotech startup that uses “artificial intelligence to find chemical compounds that could be developed into new drugs,” reports Zinnia Lee for Forbes. “By combining AI, quantum physics, cloud computing and robotic automation, Xtalpi said it helps increase the efficiency and success rate of identifying novel drug compounds,” writes Lee. “The company has recently expanded into discovering new chemical compounds for agricultural technology, cosmetics and other applications.”

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.”

Prof. Daniela Rus, director of CSAIL, speaks with The Boston Globe’s Hiawatha Bray the future of AI. “Everyone is recognizing that AI can have an impact on their business, and they’re just wondering exactly how,” says Rus. She adds that she foresees, “a future where generative AI is not just a technological marvel, but a force for hope and a force for good.”