Machine learning

MassLive reporter Scott Kirsner visited CSAIL’s Living Lab, a mocked-up apartment space designed to test and train humanoid robots. “We’re creating an environment that captures real-life properties of the spaces where we want to put machines in. So that’s a real kitchen with a real fridge and a real dishwasher and a real oven. We can make cookies in the kitchen,” explains Prof. Daniela Rus, director of CSAIL. “In the past, our robots have been in research environments, where the environment is heavily simplified over the kinds of things we expect to see in the physical world.”

MIT researchers have developed a new AI tool, dubbed “DrugReflector,” aimed at speeding up the drug discovery process, reports William A. Haseltine for Forbes. The researchers used DrugReflector to test tested almost 9,600 drugs in different human cell types.“This system was 17 times more accurate than older computational methods and improved as it used honest lab feedback,” writes Haseltine. 

Forbes reporter Gemma Allen spotlights Prof. Daniela Rus, director of CSAIL, and her work revolutionizing the field of robotics by bringing “empathy into engineering and proving that responsibility is as radical and as commercially attractive as unguarded innovation.” Rus says of her vision for the future of robotics and AI: “With robots, we can amplify strength and precision. With AI, we can amplify cognition, creativity, empathy, and foresight. These tools should help us become better versions of ourselves."

Prof. Daron Acemoglu speaks with Newsweek reporter Hugh Cameron about the impact of AI on layoffs at major retailers. “I don't think we are at the cusp of mass unemployment,” says Acemoglu. “AI models have many limitations, and while there will be companies such as Amazon that will attempt to organize work to get more out of AI and reduce their headcount, at the macroeconomic level things will go more slowly.”

Wired reporter Steven Levy spotlights Research Scientist Sarah Schwettmann PhD '21 and her work investigating the unknown behaviors of AI agents. Schwettmann has co-founded Transluce, a nonprofit interpretability startup “to further study such phenomena,” writes Levy.

At a recent conference, Prof. Sergey Ovchinnikov and his colleagues presented a paper demonstrating how they have used advanced versions of ChatGPT to “generate amino acid sequences that code for biologically active proteins with a structural feature called a four-helix bundle,” reports Jeffrey Brainard for Science. “To Ovchinnikov’s surprise, ChatGPT produced gene sequences without further refinement of his team’s query,” writes Brainard. “Still, the application of ChatGPT to this task needs refinement, Ovchinnikov found. Most of the sequences his team produced did not garner 'high confidence' on a score predicting whether they would form the desired protein structure.” 

Prof. Srini Devadas speaks with Fortune reporter Beatrice Nolan about data and privacy concerns surrounding AI assistants. “The challenge is that if you want the AI assistant to be useful, you need to give it access to your data and your privileges, and if attackers can trick the AI assistant, it is as if you were tricked,” says Devadas. 

Prof. Alex Shalek and his colleagues developed a deep-learning model called DrugReflector aimed at speeding up the process of drug discovery, reports Heidi Ledford for Nature. “They used DrugReflector to find chemicals that can affect the generation of platelets and red blood cells — a characteristic that could be useful in treating some blood conditions,” explains Ledford. The researchers found that “DrugReflector was up to 17 times more effective at finding relevant compounds than standard, brute-force drug screening that depends on randomly selecting compounds from a chemical library.”

Prof. Pat Pataranutaporn speaks with The Guardian reporter Madeleine Aggeler about the impact of AI on human relationships. “If you converse more and more with the AI instead of going to talk to your parents or your friends, the social fabric degrades,” says Pataranutaporn. “You will not develop the skills to go and talk to real humans.” 

Prof. Daron Acemoglu speaks New York Times reporter Karen Weise about workplace automation at Amazon. “Nobody else has the same incentive as Amazon to find the way to automate,” Acemoglu. “Once they work out how to do this profitably, it will spread to others, too.” 

A new study by researchers at MIT suggests that “the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models,” reports Will Knight for Wired. “By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade.” 

Researchers at MIT have developed a new method that can predict how plasma will behave in a tokamak reactor given a set of initial conditions, reports Gayoung Lee for Gizmodo. The findings “may have lowered one of the major barriers to achieving large-scale nuclear fusion,” explains Lee. 

Writing for Forbes, Senior Lecturer Guadalupe Hayes-Mota '08, SM '16, MBA '16 emphasizes the importance of implementing ethical frameworks when developing AI systems designed for use in healthcare. “The future of AI in healthcare not only needs to be intelligent,” writes Hayes-Mota. “It needs to be trusted. And in healthcare, trust is the ultimate competitive edge.” 

Researchers at MIT have studied how chatbots perceived the political environment leading up to the 2024 election and its impact on automatically generated election-related responses, reports Patrick Kulp for Tech Brew. The researchers “fed a dozen leading LLMs 12,000 election-related questions on a nearly daily basis, collecting more than 16 million total responses through the contest in November,” explains Kulp.  

Institute Prof. Daron Acemoglu participated in a “global dialogue on artificial intelligence governance” at the United Nations, reports Steve Lohr for The New York Times. “The AI quest is currently focused on automating a lot of things, sidelining and displacing workers,” says Acemoglu.