Researchers from North Carolina State University analyzed the state of smart textile research, and found that fiber battery encapsulation and mathematical modeling must improve in order for the technology to be useable. 

Researchers looked at five encapsulation techniques, from traditional polymeric tubes to using liquid metal to keep out oxygen and water. Each had benefits and drawbacks, but none had all of the qualities needed for viability in everyday clothing. They also found that existing mathematical models struggle to accurately predict the effect of fiber length on a battery's output. 

Scientists concluded that expertise from the packaging and electrochemical industries could help solve these problems, leading to fiber batteries capable of being woven into clothing. 

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.