Oh, for sure. I focused on ML in college. My first job was actually coding self-driving vehicles for open-pit copper mining operations! (I taught gigantic earth tillers to execute 3-point turns.)

I'm not in that space anymore, but I do get how LLMs work. Philosophically, I'm inclined to believe that the statistical model encoded in an LLM does model a sort of intelligence. Certainly not consciousness - LLMs don't have any mechanism I'd accept as agency or any sort of internal "mind" state. But I also think that the common description of "supercharged autocorrect" is overreductive. Useful as rhetorical counter to the hype cycle, but just as misleading in its own way.

I've been playing with chatbots of varying complexity since the 1990s. LLMs are frankly a quantum leap forward. Even GPT-2 was pretty much useless compared to modern models.

All that said... All these models are trained on the best - but mostly worst - data the world has to offer... And if you average a handful of textbooks with an internet-full of self-confident blowhards (like me) - it's not too surprising that today's LLMs are all... kinda mid compared to an actual human.

But if you compare the performance of an LLM to the state of the art in natural language comprehension and response... It's not even close. Going from a suite of single-focus programs, each using keyword recognition and word stem-based parsing to guess what the user wants (Try asking Alexa to "Play 'Records' by Weezer" sometime - it can't because of the keyword collision), to a single program that can respond intelligibly to pretty much any statement, with a limited - but nonzero - chance of getting things right...

This tech is raw and not really production ready, but I'm using a few LLMs in different contexts as assistants... And they work great.

Even though LLMs are not a good replacement for actual human skill - they're fucking awesome. 😅