  Thanks to rapid progress in artificial intelligence, we have entered an era
when technology and philosophy intersect in interesting ways. Sitting squarely
at the centre of this intersection are large language models (LLMs). The more
adept LLMs become at mimicking human language, the more vulnerable we become to
anthropomorphism, to seeing the systems in which they are embedded as more
human-like than they really are. This trend is amplified by the natural
tendency to use philosophically loaded terms, such as "knows", "believes", and
"thinks", when describing these systems. To mitigate this trend, this paper
advocates the practice of repeatedly stepping back to remind ourselves of how
LLMs, and the systems of which they form a part, actually work. The hope is
that increased scientific precision will encourage more philosophical nuance in
the discourse around artificial intelligence, both within the field and in the
public sphere.
