Calling Large Language models "sophisticated parrots" is just wrong and weird lol. And it's obvious how wrong it is when you use the se tools and evaluate without any weird biases or undefinable parameters.

This for instance is simply not possible without impressive recursive understanding. https://www.engraved.blog/building-a-virtual-machine-inside/

We give neural networks data and a structure to learn that data but outside that, we don't understand how they work. What I'm saying is that we don't know what individual neurons or parameters are learning or doing. And a neural networks objective function can be deceptively simply.

How you feel about how complex "predicting the next token" can possibly be is much less relevant than the question, "What does it take to generate paragraphs of coherent text?". There are a lot of abstractions to learn in language.

The problem is that people who are telling you these models are "just parrots" are engaging in a useless philosophical question.

I've long thought the "philosophical zombie" to be a special kind of fallacy. The output and how you can interact with it is what matters not some vague notion of whether something really "feels". If you're at the point where no conceivable test can actually differentiate the two then you're engaging in a pointless philosophical debate rather than a scientific one.

"I present to you... the philosophical orange...it tastes like an orange, looks like one and really for all intents and purposes, down to the atomic level resembles one. However, unfortunately, it is not a real orange because...reasons." It's just silly when you think about it.

LLMs are insanely impressive for a number of reasons.

They emerge new abilities at scale - https://arxiv.org/abs/2206.07682

They build internal world models - https://thegradient.pub/othello/

They can be grounded to robotics -( i.e act as a robots brain) - https://say-can.github.io/, https://inner-monologue.github.io/

They can teach themselves how to use tools - https://arxiv.org/abs/2302.04761

They've developed a theory of mind - https://arxiv.org/abs/2302.02083

I'm sorry but anyone who looks at all these and says "muh parrots man. nothing more" is an idiot. And this is without getting into the nice performance gains that come with multimodality (like Visual Language models).

>I know that something like that GPT can produce coding if it's asked to though

Programming languages are meant to be super explicit and well structured, right? So for simple procedures, problem definition to python is just a translation problem.

But most of a programmer's work is "figure out what the hell is wrong with that thing", not "write a method that invert this array"