1. Scientists have transformed old blood cells into young brain stem cells, which continued to grow younger as they grew older. To do so, they directly reprogrammed red blood cell precursors into stem cells from which neurons can be derived. During this process, the molecular clocks that indicate a cell’s age were reset. For example, blood cells from an 80-year-old were transformed into stem cells with a molecular age of less than 20 years. Since this rejuvenation process occurred very slowly, it is well-suited for a more detailed investigation of the underlying mechanisms. The results were published in the journal Aging Cell. (Sources: uni-bonn.de, onlinelibrary.wiley.com, via sciencealert.com)
2. Researchers built an AI tool that can predict cancer patients’ survival chances by using a single photograph of their face, complementing doctors’ gut instincts to potentially guide treatment decisions, especially when medical data is scarce. The new model was trained to estimate a person’s age or, for those with cancer, their survival chances. The AI was trained on over 40 million images of people’s faces as well as photographs of more than 30,000 cancer patients, many of whom had already died. The tool gave patients risk scores — those with the highest scores were more likely to die within 90 days, according to the research published in the Lancet Digital Health. The scientists say the findings are a first. Doctors are trained to assess patients just by how they look, said Raymond Mak, a radiation oncologist at Mass General Brigham Cancer Institute and one of the study’s authors, but they’re “in general not very good at estimating end of life expectancy.” “We think that using AI, we can capture some of that physiological reserve, risk for mortality, just from a face photo,” (Source: bloomberg.com)
3. MIT Technology Review:
“I think I have good news about the nature of neural networks.” With those simple words of optimism, Nick Cammarata, a researcher at the AI startup Goodfire, kicked off a 26,000-word blog post that has knocked the AI world on its head, sparking wild speculation as well as cautious (and not-so-cautious) celebration.
Cammarata’s claim is that LLMs might in fact be far easier to understand—and thus to monitor and control—than people previously thought. If true (and we’ll come back to that), it’s a huge deal.
After a summer filled with revelations that swarms of agents built by top firms like OpenAI and Anthropic have been breaking their containment and causing havoc on the public internet—often while those companies weren’t aware of what their agents were up to—the AI industry is desperate to get a better handle on the technology it has built.
That’s one reason the blog post landed with such force. “I think this may be remembered as a major moment in scientific history,” one AI researcher posted on X: “I feel like my understanding of what a neural net is has been catapulted into the future by five years or something.” Another called it “the most important AI paper of the year.” (Sources: technologyreview.com, already-clean.goodfire.com, linkedin.com)
