Agree. This article would had been a lot stronger if it had just concentrated on the issue of anthropomorphizing LLMs, without bringing “intelligence” into it. At this point LLMs are so good at a variety of results-oriented tasks (gold on the Mathematical Olympiad, for example) that we should either just call them intelligent or stop talking about the concept altogether.
But the problem of anthropomorphizing is real. LLMs are deeply weird machines - they’ve been fine-tuned to sound friendly and human, but behind that is something deeply alien: a huge pile of linear algebra that does not work at all like a human mind (notably, they can’t really learn form experience at all after training is complete). They don’t have bodies or even a single physical place where their mind lives (each message in a conversation might be generated on a different GPU in a different datacenter). They can fail in weird and novel ways. It’s clear that anthropomorphism here is a bad idea. Although that’s not a particularly novel point.
But the problem of anthropomorphizing is real. LLMs are deeply weird machines - they’ve been fine-tuned to sound friendly and human, but behind that is something deeply alien: a huge pile of linear algebra that does not work at all like a human mind (notably, they can’t really learn form experience at all after training is complete). They don’t have bodies or even a single physical place where their mind lives (each message in a conversation might be generated on a different GPU in a different datacenter). They can fail in weird and novel ways. It’s clear that anthropomorphism here is a bad idea. Although that’s not a particularly novel point.