For instance -I'm not trying to be mean and I'm certainly not blaming you in particular, because I've seen this very often- but the reasoning that because LLMs can generate language, and humans can generate language not only LLMs are somehow like humans but also humans are like LLMs is not sound.
For example, walls have ears, cats have ears, therefore walls are like cats and cats are like walls. That doesn't work because walls' ears are not like cats' ears and even if they were, that still wouldn't make walls cats and cats walls, it would just make them both entities with ears.
Nah. Nobody personifies LLMs like this. What you're laying out here is a fundamental mistake that you'd have to be extremely stupid to make. I think barely anyone is making this mistake to even qualify mentioning it.
Seriously who here things that LLMs are anything like humans? That is not the claim. The claim is that LLMs understand you. Intelligence and understanding are clearly orthogonal to "human-like"
There is no evidence, basically none whatsoever that general "perfect logical reasoning" is a thing that actually exists in the real world. None.
No animal we've observed does it. Humans certainly don't do it. The only realm this idea actually works is Fiction. and this was not like for a lack of trying. Some of the greatest minds worked on this for decades and some people still don't seem to get it. Logic doesn't scale. They break at real world relationships.
Logic systems are that guy in the stands yelling that he could've made the shot, while he's not even on the field.
Besides which, you may not hear about them in the news but pretty much all the classical, symbolic- and logic-based approaches of Good, Old-Fashioned AI are still going strong and are doing very well thank you in tasks in which statistical machine learning approaches underperform.
To give a few examples: automated planning and scheduling (used e.g. by NASA in its autonomous guidance systems for its spaceships and rovers), program verification and model checking (the latter has transformed the semiconductor industry and led to several recent Turing awards), SAT-solving and constraint satisfaction (where recent algorithmic advances have made it possible to solve many instances of NP-complete decision problems in polynomial time), adversarial search (AlphaGo and friends aren't going anywhere without Monte Carlo Tree Search), program synthesis (you can generate code with LLMs, but good luck if you want it to work correctly), automated theorem proving, heuristic search, rule learning, etc etc.
To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades. You may not read about them in the news and they're not even considered "AI" by many, but that's because they work and work very well, and the "AI Effect" takes hold [1].
Even poor old expert systems are the de facto standard for expressing business logic in the software industry. I guess. Informally, of course.
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Not what I mean. Logic is part of the real world. Logic is not the real world. The idea that you can use this small subset of the world to model the whole thing is what is incredibly suspect. No one has demonstrated this and there is no real reason to believe it can.
>To clarify, those are all logic-based approaches that remain the state of the art in classical AI tasks where statistical machine learning has made no progress in the last many decades
Logic is good at what logic does. Please don't take this to mean me calling logic useless. It's not that statistical machine learning has not made progress. But you won't beat logic on problems with clear definitions and unambiguous axioms. That is very cool but that is clearly not all of reality.
I agree and I don't think there's any kind of logic that can do that, but there is also no other formal system that can, so far. I'm not sure if you are suggesting there is?
>> But you won't beat logic on problems with clear definitions and unambiguous axioms. That is very cool but that is clearly not all of reality.
Certainly not. Logic is a set of powerful formalisms that we can use to solve certain kinds of problem - it's a form of maths, like geometry or calculus. I don't think anyone expects that geometry or calculus is going to solve every problem in existence and the same goes for logic.
No i wasn't. I guess i wasn't very clear in my first reply.
I was mainly getting at this,
>and, consequently, its' a very bad idea to try and make machines that "think like humans", because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers.
No one is scaling up and pouring millions of compute into LLMs for general intelligence because they thought it was an excellent idea before the fact(virtually no one did, even some of the most verbal proponents).
They're doing it because it's seems to be working in a way logic failed to. and logic had the headstart, both in research and public consciousness. Nearly all of fictional ai is an envisioning of the hard symbolic logic general intelligence system that dominated early ai research. Logic was not the underdog here.
The point i was really driving at is that you say "because that way we'll only make machines with none of the advantages of machines and all the disadvantages of computers." almost like it's a choice, like Logic and GPT are both on the field and people are going for the worse player. Logic is not even in consideration because ot couldn't make the cut.
There are two worlds, if you want. For me it's a mistake to try and keep them separated. All the great pioneers of AI were not only this or only that people. e.g. Shannon's MSc thesis gave us boolean logic-based circuits (logic gates) and he also introduced information theory. The people who have made real contributions to AI and to computer science were never one-trick ponies.
An analogy I like to make is that we have both airplanes and helicopters. A flying machine is something so useful to have that we 're going to use any kind we can make. Obviously a helicopter will not compete with a jet for speed, but a jet isn't anywhere as manoeuverable or flexible as a helicopter. So we use both.
>> Logic was not the underdog here.
It wasn't, but there was a bit of a Triassic extinction event, with the last AI winter of the '90s that took the expert systems and basically severed the continuity of logic-based AI research. The story is more complex than that, but logic-based AI was dealt a powerful blow, and progress slowed down. Although again like I say in my other comment, it didn't get completely extinguished. Perhaps, like we recognise birds today as the remaining dinosaurs, we'll recognise the old-new wave of logic-based AI that is hidden by the AI effect.
Gödel’s Incompleteness Theorems also demonstrates that in any sufficiently powerful mathematical system, there are true statements that cannot be proven within the system. This implies that no matter how refined a logical system you devise, it will invariably be incomplete or inconsistent when grappling with real-world phenomena.