Can large language models produce expert‐quality philosophical texts? To investigate this, we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry‐picking. Experts on Dennett's work succeeded at distinguishing the Dennett‐generated and machine‐generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the experts, while ordinary (...) research participants were near chance distinguishing GPT‐3's responses from those of an “actual human philosopher”. (shrink)

Book Three of Avatar: The Last Airbender portrays Uncle Iroh as wise and peace‐loving, in the mold of a Daoist sage. This chapter argues that Iroh's Book One foolishness is a pose, and Iroh's character does not fundamentally change. In Book One, he is wisely following strategies suggested by the ancient Chinese Daoist philosopher Zhuangzi for dealing with incompetent leaders. His seeming foolishness in Book One is in fact a sagacious strategy for minimizing the harm that Prince Zuko would otherwise (...) inflict on himself and others a gentle touch that more effectively helps Prince Zuko find wisdom than would be possible with a more confrontational approach. To understand Iroh's wisdom, it is useful to look to the ancient Daoist philosopher Zhuangzi, specifically his advice for dealing with incompetent rulers by following peacefully along with them, unthreateningly modeling disregard for fame and accomplishment while not being too useful for their ends. (shrink)

Can large language models be trained to produce philosophical texts that are difficult to distinguish from texts produced by human philosophers? To address this question, we fine-tuned OpenAI's GPT-3 with the works of philosopher Daniel C. Dennett as additional training data. To explore the Dennett model, we asked the real Dennett ten philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry-picking. We recruited 425 participants to distinguish Dennett's answer from (...) the four machine-generated answers. Experts on Dennett's work (N = 25) succeeded 51% of the time, above the chance rate of 20% but short of our hypothesized rate of 80% correct. For two of the ten questions, the language model produced at least one answer that experts selected more frequently than Dennett's own answer. Philosophy blog readers (N = 302) performed similarly to the experts, while ordinary research participants (N = 98) were near chance distinguishing GPT-3's responses from those of an "actual human philosopher". (shrink)