I think the article is a lot more interesting than you make out, because it isn’t really about ‘what we mean by reasoning’.

It’s about do we really know what’s going on in the box - an is the ‘chain of reasoning’ indicative of what’s going on, or merely an anthropomorphised fiction that kids us into believing we understand what’s going on.

> I don't see a clear demarcation of the things that only "reasoning" can accomplish and can't be approximated or imitated by other methods, and so I think the question is simply not meaningful or relevant.

One person starting the conversation might be the first step toward another person eventually making progress on such a definition, so it seems weird to reject an entire question outright early like this.

Generally I've seen a few ways LLM tools can produce sub-optimal or poor results that haven't changed a ton over the last couple of years, while the tooling has gotten FAR better at helping them stick the "at least SOMETHING functional was produced" landing. IMO a lot of it has to do with "reasoning"-as-a-process-that-involves-backtracking. And the that things could eventually be formalized around that, and if that is or isn't the case, the more people would understand what to hand off and what to not. Or how to build better prompt harnesses to compensate for those things.

In order to properly engineer things, we must know how they work.

We cannot just jump from emergent property to more convincing emergent property as if the rocks on the surface of a lake.

"Reasoning" is an important part in said framework: yes, we must understand how it works, how it works properly, how its simulations work, how they work properly...

I think the question and definition game is interesting only inasmuch as it helps us understand ourselves (what actually explains some of the mysterious properties of our perceived consciousness) or helps guide us towards improving performance and reliability of AI models.

I believe Djikstra's quote has long been taken out of context. It was a criticism of other computer scientists anthropomorphizing machines and applying human concepts like thinking and reasoning to them. Djikstra wasn't saying it's functionally the same so it's just a semantic quibble. He was saying those words don't apply to machines. Just like we don't say submarines swim because that's how animals move through water, even though subs also move through water, because it's done by a different mechanical means.

I disagree -- I think if you can nail down better what's happening and why and get a thorough handling on the mechanics, its limitations, its costs, etc you open the door to a) major efficiency wins b) improvements in rigor of said reasoning?

Right now we're playing a stochastic game with the weights, and getting major incremental improvements. But if we have a more formal modeling of how reasoning happens in them (whether we can even call it, that) we can potentially apply optimizations, adaptations of existing symbolic AI techniques, etc. to substantially shrink/optimize the models or make the inference process more efficient and more reliable.

> I'll admit that I find this discussion a bit navel-gazy. It has become a question of semantics not a question of actual functionality

Ah, so you're more in the Investor mindset than the Scientist mindset. All you care about is results, not how it got there. There's a whiff of "hey, it's magic!" to that.

Philosophical thinking about the nature of things is actually pretty enjoyable for some of us and probably a good thing to have in society

The answers to these questions probably do start to inform how we should treat these AI machines as a society too.

For instance, legally, should AI have human rights? Well, we have to try and understand how much of an independent entity AIs are, how "conscious" they are, before we can make a good decision about that.

Which might seem navel-gazey but it's probably important to talk about

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