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Large Language Models Are Neurosymbolic Reasoners

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111–120 of 172 posts

Re: Large Language Models Are Neurosymbolic Reasoners

#111

Earlier quoted context omitted.

My view is opposite. "inability to think outside of the switching frequencies of a silicon chip" Why can't you "think outside", and conceptualize that this 'internal subjective experience' we are having, is not unique, and could be taking place inside a silicon based NN? It helps to meditate and observe your own thoughts, and how they arise. You can begin to realize that you don't 'think'. You don't think about what…

Organisms are mechanistic, there's nothing "not mechanistic" about the mind. Its insane to suppose that somatosensory representation building, which requires organic neruoplasticity to be connected to the organically adaptive neuromotor system, etc. etc. etc. can just be instantiated in a bit of sand. This is deeply mystical, pseudoscience. You're credulously throwing away any kind of empirical analysis of the world…

Really. I'm not sure what you are getting at. I think from your last few sentences. We actually agree, and maybe just miss-understanding.

I'm agreeing, to the mechanistic nature. Nothing Mystical. Just physical world. No souls. I was trying to get you to see the mechanistic nature of mind, then you tell me to stop being Mystical?

You think that generating a mind from sand is 'mystical'. "just be instantiated in a bit of sand?"

But don't seem to realize Carbon, is also just a basic element. So why is any reasoning based from Carbon, Non-mystical? But Silicon is?

Then some other sentences seem to be . But in these discussions it is hard to tell when someone is being sarcastic or making a point.

Like this sentence:: "We are biological organisms; we do not have souls, even if in your religion, the soul is "a pattern". Your consciousness will not be uploaded; commander data will never visit; and your local VC shyster is on a stock manipulation grift to bamboozle you out of money."

I totally agree, no soul, it is a pattern. That does not mean we can't create a 'mind' from silicon, that is based on a pattern, and be equivalent in functionality.

I also agree, we will never be uploaded. I don't think we'll ever be able to measure the human neurons to a degree to allow this. But that doesn't mean we'll never build a NN with as many connections as a human brain has, and that it wont also be able to have a 'pattern' of self.

And, there is a lot of VC Shysters out there, doesn't mean a lot of real progress is not being made.

Re: Large Language Models Are Neurosymbolic Reasoners

#112

I'm trying to tackle this problem more head-on, by outfitting LLMs with lambda calculus, stacks, queues, etc. directly in their internals, operating over their latent space. [1] I'll read your paper, but, LLMs famously fail horribly at "multi jump" reasoning, which to me means they can't reason at all. They can merely output a reflection of the human reasoning that was baked into the training data, and they can also…

Chain of thought is basically reasoning as humans do it, the only difference is that unlike humans the model can't see that its output is wrong, abandon a line of reasoning and re-prompt itself (yet).

Various attempts at feeding their output back in to check itself have shown marked improvements in accuracy.

Re: Large Language Models Are Neurosymbolic Reasoners

#113
post #13

Earlier quoted context omitted.

It sounds like you're asking whether the output of a neural network is a deterministic function of its input. For many LLMs, you can make that answer yes with the right combination of parameters (temperature = 0) and underlying compute (variance in floating point calculations can still introduce randomness in model outputs even when the model should theoretically return the same answer every time). There are some way…

People can not be reduced to lookup tables even in theory. No one even knows how a single cell does what it does let alone an entire organism like a person. I'm not making an abstract claim about neural networks because all numerical algorithms like neural networks can be reduced to a lookup table given a large enough hard drive. This is not practical because the space required would exceed the number of atoms in the…

> People can not be reduced to lookup tables even in theory.

Yes they can, this is a direct corollary of the Bekenstein Bound.

Re: Large Language Models Are Neurosymbolic Reasoners

#114

Earlier quoted context omitted.

To be honest, I don't really understand what you mean by Hilbert spaces being computable, and what that has to do with the linearity of QM, determinism of classical mechanics, universe being geometrical and not computational etc. I'm familiar with all of those concepts, but not sure how they tie together here. If you have resources you could share I would appreciate it (I had little success with google).

computable = expressible as Int -> Int Hilbert space = set of functions in Real -> Real geometrical & non-computable = Reals determinism = g(x, t_future) fully set by g(x, t_now) and g if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t)) construct a classical system of arbitrarily complexity (eg., 10^BIG i…

> if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t))

Nobody takes "computable approximation to g: R -> R" to mean "a computable function c: R* -> R" where R is the computable reals. There are many mathematical issues with this caused by self-referential programs (realised by Turing himself in "On Computable Numbers"). Typically you would model it as "c: R* x Q -> R*" where Q is a rational describing your desired precision, right?

> Since 10^BIG are required, "delta(g, c) I'm not sure what you mean by this - the computable approximation "c" is deterministic essentially by definition. If you mean "in order to remain within some bound of g" I can kinda see what you're saying but in that case you can interleave computations with smaller and smaller precisions (the "Q" I mentioned) in order to work around that issue, right? It won't be efficient, but it will certainly be computable.

> Refences: Look for physical church-turing, church-turing thesis, non-det and det in chaos theory, non-det in classical mechanics, physical interpretations of the reals -- this will be in postgrad work, it wont be in popsci books.

Thanks! I don't know much chaos theory, I'll have a look around for a good textbook.

Edit: I just want to say - you have a pretty wild way of writing that makes it hard for me to tell if you're a crank or not. Either way, reading your posts here has given me a ton of food for thought =) what's your background?

Re: Large Language Models Are Neurosymbolic Reasoners

#115
post #66

Earlier quoted context omitted.

Can you rephrase that? It currently reads like shifting goalposts, and I'd like to guess that was not your intention…

Any theory that asserts exhaustive coverage of people would need to take all relevant aspects of reality into consideration, so I suggested some of the trickiest things that are relevant. Unfortunately for me, they are so tricky that they "don't count" (try, genuinely , to model the reality bending capability of people in a theory, I would love to see that!).

> model the reality bending capability of people

Much to the disappointment of my teenage self who would really have liked the shape-shifting spell to work, I don't see any evidence we can bend reality.

--

> consciousness?

I think this is a red herring. We can talk about P-Zombies, but we lack the means to determine if some random human (let alone AI) is one.

> the unknown?

> https://en.m.wikipedia.org/wiki/Necessity_and_sufficiency

> the misunderstood?

What about them? I still don't know why these are an interesting problem in this scenario.

> https://plato.stanford.edu/Entries/perception-problem/

Isn't one of the big criticisms of AI at the moment the fact that they do this slight more than humans, and we can point and laugh at them?

(While conveniently forgetting that half of us were Yani and the other half Laurel, that half of us were blue and black while the other half were white and gold, etc.)

> The Science of the Gaps will do I suppose?

A reference to the ever diminishing role for God in the late 19th century onwards, but I'm not sure how you're using it here?

> Culture could do it though I think.

Banks? Sure, but fictional.

Re: Large Language Models Are Neurosymbolic Reasoners

#116

Earlier quoted context omitted.

computable = expressible as Int -> Int Hilbert space = set of functions in Real -> Real geometrical & non-computable = Reals determinism = g(x, t_future) fully set by g(x, t_now) and g if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t)) construct a classical system of arbitrarily complexity (eg., 10^BIG i…

> if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t)) Nobody takes "computable approximation to g: R -> R" to mean "a computable function c: R* -> R " where R is the computable reals. There are many mathematical issues with this caused by self-referential programs (realised by Turing himself in "On Comput…

1 yr medicine, 6 yr physics, 4 yr debating union, 20 yr c programming, 20 yr love of political and stand up comedy, 15 yr software eng, 10 yr data scientist, 15 yr python, 22 yr informal & formal philosophy, 8 yr data sci & software consult/coach to finance/defence/... and maybe soon, 4 yr PhD AI & HCI

Of those, you may decide which is the most relevant to my writing style. The amount of theatrics and irony in a live delivery might change the interpretation.

Replacing R with Q is just replacing it with (Int, Int) -- so be it. My claim concerns whether CM assumes determinism (it does) and therefore requires infinite precision, any gap whatsoever that goes missing means P(t_next|t_now) You might say this indicates reality doesnt follow CM, and so that CM is wrong and (some now less hegemonic views) of QM are correct -- reality isnt deterministic.

Fine, but QM makes the situation worse. Since it's linearity now under threat: we would not be able to compose QM systems linearly if the wavefns didnt have infinite dim.

One important assumption here is that we ought take the explicit and implicit assumptions of physics as given as our starting point, ie., Prior(Physics) = High, and Posterior(NotPhysics|Physics) = Low.

So the dialectical burden is on the "computationalists" to show that there is a workable theory of physics, at every level which preserves either (1) the assumptions of physics; or (2) motivates why those assumptions are wrong non-circularly.

Given the premise on priors above, the argument, "physics is wrong because reality is computational" is both circular and unpersuasive (this doesnt mean its wrong, just that no reason has been presented).

Re: Large Language Models Are Neurosymbolic Reasoners

#117

I'm trying to tackle this problem more head-on, by outfitting LLMs with lambda calculus, stacks, queues, etc. directly in their internals, operating over their latent space. [1] I'll read your paper, but, LLMs famously fail horribly at "multi jump" reasoning, which to me means they can't reason at all. They can merely output a reflection of the human reasoning that was baked into the training data, and they can also…

> they can't reason at all.

I'm pretty sure this is it. They don't understand even negation to begin with.

In this thread someone asked an AI to output images of stereotypical American soccer fans and it drew all the Seattle fans with umbrellas:

https://old.reddit.com/r/MLS/comments/1b10t68/meme_asked_ai_...

Seattle is pretty famously known for everyone not wearing umbrellas.

Re: Large Language Models Are Neurosymbolic Reasoners

#118

Earlier quoted context omitted.

Organisms are mechanistic, there's nothing "not mechanistic" about the mind. Its insane to suppose that somatosensory representation building, which requires organic neruoplasticity to be connected to the organically adaptive neuromotor system, etc. etc. etc. can just be instantiated in a bit of sand. This is deeply mystical, pseudoscience. You're credulously throwing away any kind of empirical analysis of the world…

Really. I'm not sure what you are getting at. I think from your last few sentences. We actually agree, and maybe just miss-understanding. I'm agreeing, to the mechanistic nature. Nothing Mystical. Just physical world. No souls. I was trying to get you to see the mechanistic nature of mind, then you tell me to stop being Mystical? You think that generating a mind from sand is 'mystical'. "just be instantiated in a bit…

You think that reality is a pattern, and that properties obtain from configurations. Eg., that, of course, we can transmute gold into lead. Alchemy.

The problem with this is that the patterns have semantics, a pattern of wood does not have the same properties as a pattern of lead. The pattern isnt the important bit.

If you want to turn hydrogen into lead you first have to fire up a star and wait a very very long time, and as protons and electrons bundle up in every more complex configurations interactions between them come to dominate their properties... so that lead is nothing at all like hydrogen.

What is the only known element that enables "weak polymerisation", ie., adaptive self-replication at the molecular level: carbon.

What are the properties of all intelligent systems known to science? They're organic.

Why? This is no coincidence. In order to think, you have to grow -- self-replicate at every level from the cellular to organs, tissues... the material placitity of the machine that holds your thoughts has to itself adapt its structure to that of your body (the device which explores the world).

Now, should we expect hydrogen to do that? No. Silicon? No. Why would we? Ah, only because we loath the idea of being apes, and of oozing.. our biological disgust instincts tell us to run far away from it. How more beautiful if, like in genesis, we can fashion man out of clay and breath in life.

but alas, we arent clay; nor will clay ever think or desire or want.. clay cannot take impressions of the world without becoming an impression. It cannot adapt.

Re: Large Language Models Are Neurosymbolic Reasoners

#119

Earlier quoted context omitted.

> if you model a geometric, g : Real -> Real with computable, c : Int -> Int then there are gaps at arbitrarily high precisions, say p (eg., p = delta(g, c) at (x, t)) Nobody takes "computable approximation to g: R -> R" to mean "a computable function c: R* -> R " where R is the computable reals. There are many mathematical issues with this caused by self-referential programs (realised by Turing himself in "On Comput…

1 yr medicine, 6 yr physics, 4 yr debating union, 20 yr c programming, 20 yr love of political and stand up comedy, 15 yr software eng, 10 yr data scientist, 15 yr python, 22 yr informal & formal philosophy, 8 yr data sci & software consult/coach to finance/defence/... and maybe soon, 4 yr PhD AI & HCI Of those, you may decide which is the most relevant to my writing style. The amount of theatrics and irony in a live…

Wild, well, I'm a lowly math PhD so that's where my interests lie =)

I'm _not_ suggesting we replace R with Q. I'm suggesting that you bake in the desired accuracy of your computational approximation as an input. This is how Turing evades self-referential problems in his conception of computational reals, and also perhaps how you evade your criticisms with CM requiring infinite precision.

Similarly - I think it's reasonable in a computational context to assume linearity up to an error bound that is provided as an input. Of course things become non-computable if you ask for exact linearity. Equality itself is non-computable!

Either way I think we agree about physics. I don't believe the universe is describable as a computable function. Merely that we can approximate it to arbitrary degrees of accuracy =P

Re: Large Language Models Are Neurosymbolic Reasoners

#120

Earlier quoted context omitted.

Really. I'm not sure what you are getting at. I think from your last few sentences. We actually agree, and maybe just miss-understanding. I'm agreeing, to the mechanistic nature. Nothing Mystical. Just physical world. No souls. I was trying to get you to see the mechanistic nature of mind, then you tell me to stop being Mystical? You think that generating a mind from sand is 'mystical'. "just be instantiated in a bit…

You think that reality is a pattern, and that properties obtain from configurations. Eg., that, of course, we can transmute gold into lead. Alchemy. The problem with this is that the patterns have semantics, a pattern of wood does not have the same properties as a pattern of lead. The pattern isnt the important bit. If you want to turn hydrogen into lead you first have to fire up a star and wait a very very long time…

"The pattern isn't the important bit."

I was taking pattern as the electrical impulses in the brain. Which I would say is everything when it comes to thought and thinking.

Human Neurons are just Calcium Voltage potentials. Just like weights in a NN.

Yes. The human brain neurons are far more complicated than that. There is a lot of chemical soup of hormones and modulators that factor into the voltage potentials. What you eat can impact gut, that impacts how many neurotransmitters are produced, that impact when a neuron fires, that is experienced as a mood, etc.... SO yes, the human brain cannot be separated from the body.

There is some entity, brain+body. And it so happens that it is made from Carbon . And we call things based on Carbon to be 'organic' and it can re-produce so we call it 'alive'. These are just definitions, that we have assigned to things.

Just like a CPU can't be removed from it's power supply and keep working. There is a 'system' and it has 'parts'.

That doesn't mean, we can't model a brain to a degree close enough that you couldn't tell an AI and Human apart.

I'm just saying, that when we do reach that point, then we'll be forced to realize the AI also has an internal subject experience, and is 'conscious', or we'll have to acknowledge that humans do not.

It will be either/or.

Humans and AI are both conscious, have internal subjective experience. or Neither do. And if neither do, then humans really are just hallucinating their inner experience, but not in control. Humans are also deterministic. Nothing special.

I see from your other posts, that you seem to be putting a lot of faith in Chaos Theory or Quantum Mechanics as some underlying explanation. To give humans the special sauce.

I'd just say, introducing randomness, does not make a system un-determined. That there is a lot of randomness in our 'chaotic' body, does not imbue it with agency. Randomness != Agency.

Edit: I like the clay example. I'd say that is like the NN that goes through training, then released, but the model is static. Doesn't get updated. Eventually these AI's will learn as they go and be continually updating.

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