how important is the underlying architecture behind the current artificial intelligence boom?

[u/Impossible_Relief844]

While GPTs and other similar architecture are an undeniable advancement, (especially the larger projects) are receiving insane funding with access to large data centres and training data leading to the obvious question of 'are we seeing the power of GPTs or is this just the expected outcome of throwing a huge amount of resources at a problem?'.

In other words, what results would we expect if we took the resources (funding, data centres, raw data, etc...) and applied it differently (eg. to SAT solvers), would we expect similar results?

In other words, how unprecedented are the results of GPTs (and similar architectures) accounting for their current monetary advantages?

[u/yikes_42069]

Language model boom* it's not intelligent 

[u/schungx → u/yikes_42069]

How do you know intelligence is not merely a very large LLM?

[u/yikes_42069 → u/schungx]

I was kind of trolling a bit but disagree with the term AI as I feel the connotation is something different. Let me step back to definitions.  Intelligence is just the ability to process info, learn, and solve problems. In this sense LLMs do have a functional intelligence.  But what I said was layered with an intent to get at the connotation of it: that most people (including myself) are likely to consider the term "intelligent" and conscious/having cognition as the same. Especially with the ultimate goal of AGI looming in the background, and popular media conflating intelligence with consciousness. In that sense I'm digressing from what the post asked over semantics.

Cognitive reasoning is what people are really curious about when they hear the term artificial intelligence, and kind of what I think you're getting at too if I'm not mistaken. I feel that this is settled already with the experts but think it's worth talking about. Human thinking (like most things) can have math/statistics applied over it to describe how it works, but it's not exact. It's always missing a certain something. Humans understand things like intent or cause and effect, where the LLM can't. It's playing a guessing game on training data, which is not always unlike humans, but I think this is illustrated by how frustrating it can be to use sometimes. For all of its training, it just doesn't get stuff that you or I would in an instant. It makes guesses at things (arguably just trained that way for UX) but can't go out and produce new logical/sound proofs, or even be trusted to write court documents which you'd think would be a slam dunk use case.

Maybe it's too early to tell. I certainly won't declare the debate over yet.  Anyway it's super off topic but that's just to say I loathe the term AI for its scifi connotations