When ChatGPT started "breathing" during voice interactions this year, this design choice seemed innocuous. However, it highlights a concern around increasingly human-like AI features being implemented without proper evaluation of their necessity or impact. The tragic case of Character.AI -- where anthropomorphic design choices allegedly contributed to a teenager's death -- demonstrates the urgent need for systematic analysis of these features. This article proposes a two-step framework for evaluating anthropomorphic AI design: first assessing both social and technical necessity, then implementing appropriate guidelines and safeguards (some of which are suggested herein). While some applications (like accessibility tools) may well justify human-like features, others (like companionship AI) tread on ethically difficult territory. As AI becomes more sophisticated, we need clear criteria for determining when anthropomorphic design serves legitimate social purposes. An LLM doesn't really need to breathe, and perhaps we ought to think much harder about how we would like human-to-human relationships to look like in the future before delving too far into enabling human-to-AI facsimiles.

Did you know we're all mind readers? Humans develop what’s called “theory of mind” (ToM) around the age of four, enabling us to hold views and beliefs about our own mental states and the mental states of others. It’s one of those things that, well, makes us human. However, a recent paper by Strachan et al. explores this capability in the context of AI -- a finding which has some interesting implications. The authors compared the performance of a series of LLMs (GPT-3, GPT-4, and LLaMA2) against around 1,900 humans across a battery of tests designed to explore ToM. They determined that, in every task bar one (designed to test faux pas), LLMs can perform at or above human level. My own cursory testing suggests that GPT-4o may even be able to demonstrate ToM across the board. While we can’t conclude that these models actually have ToM (in a cognitive sense) they certainly seem to behave as if they do. So what does this mean in practice? These LLMs open the door to exciting potential applications across areas such as mental health and decision support making. Ultimately, this paper’s conclusions give us a moment to stop and think about the state of our technology: to be impressed by what we’ve accomplished, and acknowledge the risks that should be considered as we move forward.