and you don't know for sure if it was really reasoning or memorization and stochastic parroting. From another hand there are many research results demonstrating that neural networks have limitation in learning even something like multiplying numbers with many digits.
And most humans struggle with multiplying numbers with even quite few digits.
most of the humans can learn the algorithm, and do many iterations reliably if you give pen and paper, current LLMs struggle with this.
So, if we define reasoning as learning and executing algorithm of complexity N, than humans can learn more complex algorithms for higher N.
Yet, still, I think you overestimate the average humans ability to reliably execute even algorithms that simple.
I've had LLMs do that correctly up to far larger numbers than I've multiplied by hand. I could do it, but I'm not confident I wouldn't get sloppy and make mistakes without going back and reviewing my work.
I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs, or taking a grammar in an unspecified BNF variant abd generate grammatically valid sentences in it, or conversely validating a sentence against the grammar of a fictional language.
I think a whole lot of the arguments here assume a level of human reasoning well above average.
That's not to say current LLMs do not also have glaring blind spots, but so it seems to me does people dismissing their apparent reasoning out of hand because of assumptions about human abilities we mostly haven't tested.
but given enough time you can multiply any numbers, while LLM has fundamental inability to learn algorithm.
> I've also had LLMs execute algorithms most people wouldn't even understand. E.g. converting between NFAs and DFAs
this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim.
It can also use WolframAlpha. It’s not a pure LLM solution but it’s good enough to multiply numbers.
Have you tried it?
The irony of these objections is that people speak out of a "but we learned to do that at school" way of thinking, often on the basis of above average skills, rather than considering that while most people maybe could (I'm not convinced), most people won't do it step by step unless you really goad them into it and pay attention that they don't skip or gloss over steps.
The same with coding: it can produce code for tasks where something similar was in training dataset, but if you ask to do completely unknown problem - it will fail.
GPT4 at least knows the algorithms - many alternative version - and will follow them. What it won't do, just like most humans, is take the time to go over it and review each step for possible errors without further prompting and inducement.
Yes, humans could. Most humans don't, without outside pressure. With pressure applied and LLM will too.
Again, this is an example of how people overestimate how humans do in the same condition.
I would suggest you look up some studies on numeracy - sure, most people can learn if put through enough pressure, and good humans would do well against currebt LLMs, but average adult human numeracy is shockingly poor.
Consider if you would question people's ability to reason in general if you find a few examples of humans who do poorly at it.
It is fascinating when people compare LLMs this way against some platonic ideal of a human without considering that many of the failures of LLMs to stick to the prompt occurs in humans too. E.g. when you argue a human could do something with enough time, you leave out the effort you'd need to expend to convince said human to do so, and follow your instructions carefully enough.
Yes, there are still plenty of conditions where humans can beat LLMs given sufficient inducement, and even in many cases easily. But that is a very different subject.
> this is a not good quality discussion, such claims should be expressed in the form of some research results: describing what you do exactly, how data has been obtained, verified for correctness, what is complexity of your NFAs etc, otherwise I personally have hard time to judge how indicative your claim
I'll happily do that if you do the same for your unsubstantiated claims first.
Otherwise you can simply actually try to ask ChatGPT about these things yourself, and then try to ask a random person about them.
unless it is fundamentally incapable.
Ask your "AI" something about a non-popular domain (e.g., Clifford algebra) or even ask it to derive something (e.g., the geometric product) given some axioms and definitions.
Mathematics is precise as it gets, yet it failed to derive the geometric product given a set of axioms (e.g., distributive property) and definitions. Ask it to derive a theorem from perspective geometry.
Or, try something else that is not so popular or widespread (something outside of math), I suspect, that it also does a poor job deriving things from principles (axioms, definitions, theorems).
If a hunk of metal looks like a car, sounds like a car, and drives like a car is it a car?
Also, hype is good for the stock market, so keep hyping it up? At the end, Elon Musk success seems to stem from hype and sensationalism. CNN (i.e., "breaking news" everywhere) has financial success due to sensationalism and hype, so hype is good, I guess?