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Animats ◴[] No.41890003[source]
This is an important result.

The actual paper [1] says that functional MRI (which is measuring which parts of the brain are active by sensing blood flow) indicates that different brain hardware is used for non-language and language functions. This has been suspected for years, but now there's an experimental result.

What this tells us for AI is that we need something else besides LLMs. It's not clear what that something else is. But, as the paper mentions, the low-end mammals and the corvids lack language but have some substantial problem-solving capability. That's seen down at squirrel and crow size, where the brains are tiny. So if someone figures out to do this, it will probably take less hardware than an LLM.

This is the next big piece we need for AI. No idea how to do this, but it's the right question to work on.

[1] https://www.nature.com/articles/s41586-024-07522-w.epdf?shar...

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CSMastermind ◴[] No.41892068[source]
When you look at how humans play chess they employ several different cognitive strategies. Memorization, calculation, strategic thinking, heuristics, and learned experience.

When the first chess engines came out they only employed one of these: calculation. It wasn't until relatively recently that we had computer programs that could perform all of them. But it turns out that if you scale that up with enough compute you can achieve superhuman results with calculation alone.

It's not clear to me that LLMs sufficiently scaled won't achieve superhuman performance on general cognitive tasks even if there are things humans do which they can't.

The other thing I'd point out is that all language is essentially synthetic training data. Humans invented language as a way to transfer their internal thought processes to other humans. It makes sense that the process of thinking and the process of translating those thoughts into and out of language would be distinct.

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nox101 ◴[] No.41892362[source]
It sounds like you think this research is wrong? (it claims llms can not reason)

https://arstechnica.com/ai/2024/10/llms-cant-perform-genuine...

or do you maybe think no logical reasoning is needed to do everything a human can do? Tho humans seem to be able to do logical reasoning

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bbor ◴[] No.41892803[source]
I’ll pop in with a friendly “that research is definitely wrong”. If they want to prove that LLMs can’t reason, shouldn’t they stringently define that word somewhere in their paper? As it stands, they’re proving something small (some of today’s LLMs have XYZ weaknesses) and claiming something big (humans have an ineffable calculator-soul).

LLMs absolutely 100% can reason, if we take the dictionary definition; it’s trivial to show their ability to answer non-memorized questions, and the only way to do that is some sort of reasoning. I personally don’t think they’re the most efficient tool for deliberative derivation of concepts, but I also think any sort of categorical prohibition is anti-scientific. What is the brain other than a neural network?

Even if we accept the most fringe, anthropocentric theories like Penrose & Hammerhoff’s quantum tubules, that’s just a neural network with fancy weights. How could we possibly hope to forbid digital recreations of our brains from “truly” or “really” mimicking them?

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1. shkkmo ◴[] No.41893265[source]
> LLMs absolutely 100% can reason, if we take the dictionary definition; it’s trivial to show their ability to answer non-memorized questions, and the only way to do that is some sort of reasoning.

Um... What? That is a huge leap to make.

'Reasoning' is a specific type of thought process and humans regularly make complicated decisions without doing it. We uses hunches and intuition and gut feelings. We make all kinds of snap assessments that we don't have time to reason through. As such, answering novel questions doesn't necessarily show a system is capable of reasoning.

I see absolutely nothing resumbling an argument for humans having an "ineffable calculator soul", I think that might be you projecting. There is no 'categorical prohibition', only an analysis of the current flaws of specific models.

Personally, my skepticism about imminent AGI has to do believing we may be underestimating the complexity of the software running on our brain. We've reached the point where we can create digital "brains", or atleast portions of them. We may be missing some other pieces of a digital brain, or we may just not have the right software to run on it yet. I suspect it is both but that we'll have fully functional digital brains well before we figure out the software to run on them.

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2. bbor ◴[] No.41895516[source]
All well said, and I agree on many of your final points! But you beautifully highlighted my issue at the top:

  'Reasoning' is a specific type of thought process 
If so, what exactly is it? I don’t need a universally justified definition, I’m just looking for an objective, scientific one. A definition that would help us say for sure that a particular cognition is or isn’t a product of reason.

I personally have lots of thoughts on the topic and look to Kant and Hegel for their definitions of reason as the final faculty of human cognition (after sensibility, understanding, and judgement), and I even think there’s good reason (heh) to think that LLMs are not a great tool for that on their own. But my point is that none of the LLM critics have a definition anywhere close to that level of specificity.

Usually, “reason” is used to mean “good cognition”, so “LLMs can’t reason” is just a variety of cope/setting up new goalposts. We all know LLMs aren’t flawless or infinite in their capabilities, but I just don’t find this kind of critique specific enough to have any sort of scientific validity. IMHO

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3. mannykannot ◴[] No.41896163[source]
I feel you are putting too much emphasis on the importance and primacy of having a definition of words like 'reasoning'.

As humanity has struggled to understand the world, it has frequently given names to concepts that seem to matter, well before it is capable of explaining with any sort of precision what these things are, and what makes them matter - take the word 'energy', for example.

It seems clear to me that one must have these vague concepts before one can begin to to understand them, and also that it would be bizarre not to give them a name at that point - and so, at that point, we have a word without a locked-down definition. To insist that we should have the definition locked down before we begin to investigate the phenomenon or concept is precisely the wrong way to go about understanding it: we refine and rewrite the definitions as a consequence of what our investigations have discovered. Again, 'energy' provides a useful case study for how this happens.

A third point about the word 'energy' is that it has become well-defined within physics, and yet retains much of its original vagueness in everyday usage, where, in addition, it is often used metaphorically. This is not a problem, except when someone makes the lexicographical fallacy of thinking that one can freely substitute the physics definition into everyday speech (or vice-versa) without changing the meaning.

With many concepts about the mental, including 'reasoning', we are still in the learning-and-writing-the-definition stage. For example, let's take the definition you bring up: reasoning as good cognition. This just moves us on to the questions of what 'cognition' means, and what distinguishes good cognition from bad cognition (for example, is a valid logical argument predicated on what turns out to be a false assumption an example of reasoning-as-good-cognition?) We are not going to settle the matter by leafing through a dictionary, any more than Pedro Carolino could write a phrase book just from a Portugese-English dictionary (and you are probably aware that looking up definitions-of-definitions recursively in a dictionary often ends up in a loop.)

A lot of people want to jump the gun on this, and say definitively either that LLMs have achieved reasoning (or general intelligence or a theory of mind or even consciousness, for that matter) or that they have not (or cannot.) What we should be doing, IMHO, is to put aside these questions until we have learned enough to say more precisely what these terms denote, by studying humans, other animals, and what I consider to be the surprising effectiveness of LLMs - and that is what the interviewee in the article we are nominally discussing here is doing.

You entered this thread by saying (about the paper underlying an article in Ars Tech [1]) I’ll pop in with a friendly “that research is definitely wrong”. If they want to prove that LLMs can’t reason..., but I do not think there is anything like that claim in the paper itself (one should not simply trust what some person on HN says about a paper. That, of course, goes as much for what I say about it as what the original poster said.) To me, this looks like the sort of careful, specific and objective work that will lead to us a better understanding of our concepts of the mental.

[1] https://arxiv.org/pdf/2410.05229

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4. shkkmo ◴[] No.41897126[source]
> don’t need a universally justified definition, I’m just looking for an objective, scientific one. A definition that would help us say for sure that a particular cognition is or isn’t a product of reason.

Unfortunately, you won't get one. We simply don't know enough about cognition to create rigourous definitions of the type you are looking for.

Instead, this paper, and the community in general are trying to perform practical capability assessments. The claim that the GSM8k measures "mathematical reasoning" or "logical reasoning" didn't come from the skeptics.

Alan Turring didn't try to define intelligence, he created a practical test that he thought would be a good benchmark. These days we believe we have better ones.

> I just don’t find this kind of critique specific enough to have any sort of scientific validity. IMHO

"Good cognition" seems like dismisal of a definition, but this is exactly the definition that the people working on this care about. They are not philosphers, they are engineers who are trying to make a system "better" so "good cognition" is exactly what they want.

The paper digs into finding out more about what types of changes impacts peformance on established metrics. The "noop" result is pretty interesting since "relevancy detection" isn't something we commonly think of as key to "good cognition", but a consequence of it.

5. NemoNobody ◴[] No.41898385{3}[source]
This is one of my favorite comments I've ever read on HN.

The first three paragraphs you wrote very succinctly and obviously summarize the fundamental flaw of our modern science - that it can't make leaps, at all.

There is no leap of faith in science but there is science that requires such leaps.

We are stuck bc those most capable of comprehending concepts they don't understand and are unexplainable - they won't allow themselves to even develop a vague understanding of such concepts. The scientific method is their trusty hammer and their faith in it renders all that isn't a nail unscientific.

Admitting that they don't kno enough would be akin to societal suicide of their current position - the deciders of what is or isn't true, so I don't expect them to withhold their conclusions til they are more able to.

They are the "priest class" now ;)

I agree with your humble opinion - there is much more we could learn if that was our intent and considering the potential of this, I think we absolutely ought to make certain that we do everything in our power to attain the best possible outcomes of these current and future developments.

Transparent and honest collaboration for the betterment of humanity is the only right path to an AGI god - to oversimplify a lil bit.

Very astute, well formulated position, presented in accessible language and with humility even!

Well done.