alt Hacker News> They're made out of weights" describes why the weight-based construction of neural networks should impact the way that you think about them and their outputs.
How do you connect that description to "LLMs could not possibly be good models of some cognitive capacity"?
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"LLMs could not possibly be good models of some cognitive capacity because they are just a bunch of numbers guessing the next word. They have no linguistic module, so they cannot exhibit cognition". That's the argument. It's pretty clearly stated.
Look, this isn't necessarily directed at you, but I've been a researcher into the theory of deep learning for many years now. I've seen all the phases, heard all the criticism, had to constantly argue against this. Gary Marcus was one of the loudest voices of this argument, but every would-be philosopher came out of the woodwork to explain why LLMs are no more than stochastic parrots because of their design. Geoffrey Hinton famously had to debunk these arguments multiple times.
And now that LLMs start to clearly exhibit intelligent behavior and can be somewhat reliable, now "nobody ever thought that LLMs could not possibly be good models of some cognitive capacity because of next-token predictions, or linear algebra, etc."? No, that's not okay.
The false conclusion that's being drawn is "therefore LLMs could not be good models of consciousness" (consciousness being a cognitive capacity). Plus, I suppose a subtle implication that a good model of consciousness is not actually conscious. To which I would invoke the spirit of the Turing test: if you can't tell the difference, is it not more sensible to say that it is.