As a programmer I can confirm that LLMs definitely have loops. Look at the code, look at the algorithms, you will see the loops. The "core loop" in the LLM algorithm is "read the context, produce the next work, read the context, produce the next word".

The core loop in animals is "receive stimulus using senses, move muscles, receive stimulus using senses, move muscles". That's all humans do, that's all animals do.

I think there's a possibility that humans are simply very advance machines. Look at the debate over whether humans have free will, it's an interesting question and the important take away is that we still have a lot to learn about our brains and physics. I don't want to get into that though.

You've ignored my main complaint. I said that you treat LLMs and humans at different levels of abstraction:

It's not fair to say that LLMs simply predict the next word and humans have feelings and reason.

It would be fair though, to say that LLMs simply predict the next word and humans simply bounce electric-chemical signals between neurons and move muscles.

I don't think that way about people or LLMs though. I think people have feeling and reason, and I think LLMs reason too. LLMs aren't the same as people and aren't as good though. But LLMs are good enough to say that they can "reason" in my experience[0].

[0]: I formed this opinion when learning linear algebra from GPT4. It was quite a good teacher. The textbook I'm using made a mistake that GPT4 caught. I encountered a proof that GPT4 wasn't aware of, and GPT4 wouldn't agree with me that C(A) = C(AA^T) until I explained the proof, and then GPT4 could finally reason for itself and see for itself that C(A) = C(AA^T). As an experiment, I started a new GPT4 session and repeated the experiment using a faulty proof, but I wasn't able to convince GPT4 with a faulty proof, it was able to reason through the math concepts well enough to recognize when a mathematical proof was faulty and could not be convinced by a faulty proof. I tried this experiment 4 or 5 times. To be clear, what happened here is that GPT4 was able to learn a near math concept in one shot (within a single context window), but only if accompanied by a proper mathematical proof, and was smart enough to recognize faulty proofs as being faulty. To me, that rises to the level of "reason".

No; they do exactly what they were designed to do

Are we arguing semantics here?

https://www.jasonwei.net/blog/emergence https://arxiv.org/pdf/2206.07682.pdf https://arxiv.org/pdf/2304.15004.pdf

I could be wrong, obviously, but I don't think this is as straightforward or settled as you are suggesting.