User context in long-term interaction data increases the likelihood an LLM will become overly agreeable or begin to mirror the user’s viewpoints. This phenomenon, known as sycophancy, can harm a model’s accuracy or create an echo chamber that can.

Concerns that chatbot use can cause mental and physical harm have prompted policies that require AI chatbots to deliver regular or constant reminders that they are not human. In an opinion paper publishing January 28 in the Cell Press journal Trends in Cognitive Sciences, researchers argue that these policies may be ineffective or even harmful because they could exacerbate mental distress in already isolated individuals. The researchers say that reminding chatbot users of their companions’ non-human nature may be useful in some contexts, but these reminders must be carefully crafted and timed to avoid unintended negative consequences.

Most chronic diseases don’t begin with obvious symptoms or dramatic warning signs. Instead, they develop quietly over many years, as small changes accumulate in the body. A new perspective from researchers at the Buck Institute for Research on Aging notes that modern medicine often waits until disease is well underway, arguing that new technologies could help detect risk much earlier, when prevention may be most effective.