That is a technical detail, not a fundamental change. By fundamental mechanism I mean what the machine is designed to do. Of course techniques and implementations evolve, refine and improve in 60 years, but the idea behind the technology did not evolve much (NLP).

Agree to disagree.

There is a lot that can be discussed in a philosophical debate. However, any 8 years old would be able to count how many letters are in a word. LLMs can’t reliably do that by virtue of how they work. This suggests me that it’s not just a model/training difference. Also evolution over million of years improved the “hardware” and the genetic material. Neither of this is compares to computing power or amount of data which is used to train LLMs.

I believe a lot of this conversation stems from the marketing (calling “intelligence”) and the anthropomorphization of AI.

Anyway, time will tell. Personally I think it’s possible to reach a general AI eventually, I simply don’t think the LLMs approach is the one leading there.

As much as I agree with you, humans can learn a bunch of stuff without first learning the content of the whole internet and without the computing power of a datacenter or consuming the energy of Belgium. Humans learn to count at an early age too, for example.

I would say that the burden of proof is therefore reversed. Unless you demonstrate that this technology doesn’t have the natural and inherent limits that statistical text generators (or pixel) have, we can assume that our mind works differently.

Also you say immature technology but this technology is not fundamentally (I.e. in terms of principle) different from what Weizenabum’s ELIZA in the '60s. We might have refined model and thrown a ton of data and computing power at it, but we are still talking of programs that use similar principles.

So yeah, we don’t understand human intelligence but we can appreciate certain features that absolutely lack on GPTs, like a concept of truth that for humans is natural.

That is my experience, it’s generally quite decent for small and simple stuff (as I said, distillation of documentation). I use it for rust, where I am sure the training material was much smaller than other languages. It’s not a matter a prompting though, it’s not my prompt that makes it hallucinate functions that don’t exist in libraries or make it write code that doesn’t compile, it’s a feature of the technology itself.

GPTs are statistical text generators after all, they don’t “understand” the problem.

I hardly see it changed to be honest. I work in the field too and I can imagine LLMs being good at producing decent boilerplate straight out of documentation, but nothing more complex than that.

I often use LLMs to work on my personal projects and - for example - often Claude or ChatGPT 4o spit out programs that don’t compile, use inexistent functions, are bloated etc. Possibly for languages with more training (like Python) they do better, but I can’t see it as a “radical change” and more like a well configured snippet plugin and auto complete feature.

LLMs can’t count, can’t analyze novel problems (by definition) and provide innovative solutions…why would they radically change programming?

Even if they plateaued in place where they are right now it would lead to major shakeups in humanity’s current workflow

Like which one? Because it’s now 2 years we have chatGPT and already quite a lot of (good?) models. Which shakeup do you think is happening or going to happen?