This is the problem I have. People are built to be fooled by this stuff - to see something that's not there. Pareidolia but with language. It's such a compulsion that the people who build LLM's see that the same non thing there too.
When I stumble around trying to explain my thinking though I invariably get hit with the response, Sure, but if it's functionally identical to a coin being magically pulled out your ear, what's the difference if it is or if it isn't? The only response I have is that when the coin doesn't appear you're going to be putting yourself way further behind the starting line then if you thought from the get go that there's no such thing as magic.
> This is the problem I have. People are built to be fooled by this stuff - to see something that's not there. Pareidolia but with language.
I really like this metaphor, it hits home that just because you sense something is human-like doesn't make it so. We're wired to respond to those sorts of things.
> When I stumble around trying to explain my thinking
I had similar difficulties expressing my thoughts in an organized way. The video below from Richard Sutton (the father of Reinforcement Learning [which LLMs use extensively in training]), helped bring order and firm up some of my thoughts and theories.
>I invariably get hit with the response, Sure, but if it's functionally identical to a coin being magically pulled out your ear, what's the difference if it is or if it isn't?
Not to spoil the video, but it isn't at all functionally equivalent. LLMs are an imperfect and somewhat randomized simulation of what the LLM thinks an average person might say in response to a question. Which is another way of saying, most people will on average get worse answers than if they worked on a problem themselves (but they will get that worse answer from the LLM comparatively quickly). Practical people respond to this positioning fairly well, and for others it still lands a bit since no one wants to think they're below average. That said, I'm still working on my positioning a bit as well!
I believe there's a confusion in this thread between what an LLM _does_ and what an LLM _is_. What is does is output a next token. What it is, is a universal function approximator ([1], i.e. a neural net).
With back probation in the neural net, there could be a full state machine being approximated inside the weight. And a state machine is the exact step-wise reasoning the author claims it needs.
Brute force is powerful yes. And another way to look at math problems is that humans solved a huge lot of very hard math problems already but didn't solve all of them.
Another thing humans did is invent the telescope, the microscope, the transistor, antibiotics, the computer, AI, discovered how to send satellites in space and how to do heart-transplant etc.
I'd say there's still some way to go for AI before we declare humans dumb because they "failed for decades" at solving a few math problems.
This is the double edged sword of calling it AI, of using terms like Temperature and Hallucinate and Thought.
Stop trying to compare either system to a human and look at it for what it is -
A prediction engine that runs fast enough to brute force problems.
In the case of alpha go its "innovation" was millions of games played against itself. It had bound parameters and strict win conditions.
In the case of LLM's you can deploy 1000's of agents to smash themselves against an idea. The whole hugging face attack is an example of this (1200 agents out of an unknown number chose that path).
There is the old saying about monkeys, typewriters and Shakespeare. Well we have better monkeys who basically follow a derivative of zipfs law (not actually), who use tokens not letters and their goal in many cases is testable (compile, unit, E2E).
>A prediction engine that runs fast enough to brute force problems.
The interesting thing is you think humans don't run in the same manner a lot, if not most of the time.
When there were very few humans on earth, development was very slow. If I sent you back 10,000 years ago you could catch up humanity 9,500 years or so with just the knowledge you've learned via memorization. So this idea that humans are pure reasoning machines, each one capable of great feats of logic just doesn't seem to hold true. Instead deep reasoning and insights came very slowly over time and as we built up technologies like writing and reading our abilities to exchange information increased over time. This lead to more people, which further brute forced the problems of humanity.
> Knowledge and reasoning are inextricably interwoven in the weights of the neural network—there is no independent, explicitly represented set of beliefs.
I'm not sure I understand this. Do we have evidence humans have an independent set of beliefs not shaped by knowledge and reasoning? If so, where do these come from?
I'm especially confused about a prior statement as a scientist:
> Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize).
How does a set of beliefs help with reasoning?
> Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.
Philosophers like Kant, Descartes and Locke wanted to draw a distinction between a priori and a posteriori beliefs (or knowledge). I believe AI will be very much bound by these discussions. Some knowledge can be had purely by reason. Some other knowledge will require observations.
Consider a calculator, a machine capable of valid transformations wrt the semantics of arithmetic.
Does a calculator reason its way to 2 + 2 = 4? No, it merely transforms.
An LLM is simply a much more complex version of this; except that its complexity hides a lot of what is otherwise reducible to a transformation of semantic, conceptual, and inferential relations. There are lots of NLP models prior to the introduction of modern "AI" that would make these representations more clear.
However I don't believe any of the aforementioned philosophers would ever consider reason itself as a transformation, as Kant especially spent a lot of time differentiating apprehension from transition.
I’m not so sure. While I completely agree on pure transformation, I continue to believe that the most concerning aspect of neural networks is that they really strengthen the argument that consciousness is the illusion.
The interplay between deductive frameworks and inductive frameworks is still always buoyed by the black swan problem… which is a the heart of Hume’s solipsism. Kant’s pure reason never considered instinctual logical gates that come prepackaged in the mind. That seems frighteningly close to a prediction algorithm, no matter how much we wrap it in a concept of understanding. In the end, we’re just building a model with a minimal error rate.
> I continue to believe that the most concerning aspect of neural networks is that they really strengthen the argument that consciousness is the illusion.
Based on what? Just because you can model the weather using math doesn't imply at all that the weather is the product of a mathematical model itself. I don't see how reasoning is different.
We deliberately construct LLMs so that they approximate certain relations typical of reasoning. Suppose they become extraordinarily successful at doing so. What exactly have we demonstrated? That these effects can be artificially reproduced through such a mechanism. But how would it follow that the thing being reproduced must itself be nothing more than that mechanism?
Based on the face that we have zero explanation for consciousness. Based on the face that biologically neural networks are exactly a kind of neural network.
My point isn’t that we’re able to explain consciousness as an illusion. It’s that we’re able to explain consciousness-like behavior presumably without consciousness, and the infrastructure look uncomfortably like we do.
What does consciousness have to do with reasoning? You're conflating two different things.
Consciousness is the what-it's-like of sensation you have as an organism from your sensory modalities and having a body. For human, that's colors, sounds, tastes, smells, feels, feeling of balance and what not that happen in perception, hallucination, memory, imagination, inner dialog and dreams along with emotions. Each sentient organism has their own sensations and emotions making up subjective experiences that nobody else has access to except inference from behavior and language (for humans).
That's not reasoning (just the having sensations/emotions). Reasoning is an adjacent activity that can be conscious but also unconscious. And it's hard to say that colors, sounds, emotions are illusions. What does that mean? We do experience them. There is something it's like to see color which is different from what it's like to feel pain or to be in love. And none of those (presumably) are what it's like when a bat experiences echolocation (there are many examples from the animal kingdom if one takes issue with echolocation).
Western tradition has firmly put pure reasoning in a cage. It serves its purpose only as a source of hypotheses, conjecture and thought experiments. Abrahamic religions, while still not accepted sensu proprio by many, are much less mystical than the Eastern Religions. You can see the difference in the unusual creative process of Ramanujan.
Which tradition are we to follow then whilst training the AI models? Pushed beyond their limits today's models tend to hallucinate and cannot be accepted sensu proprio. Philosophers will call it speculative reasoning. And yet, some speculation can be useful in breaking new grounds. Anyway, I have no answers. Just musings.
> I continue to believe that the most concerning aspect of neural networks is that they really strengthen the argument that consciousness is the illusion.
I think it’s not a question of it existing, but to what extent does conscious can control the organism (as opposed to being a passenger). There’s some intriguing research on the topic.
In a human brain it can reason regardless of what you personally know. In an LLM, the knowledge and the structure are the same thing. It doesn’t have separate knowledge parts and processing parts, it’s all one network. If you delete the part about cats, it can’t think at all anymore. Whereas a human could lose all their memories and still be capable of thinking.
How can you know that, when every human who reasons knows a lot? We spend years in a pre-rational state, and by the time we can reason reliably we have ingested a huge amount of evidence about the world. Memories are just a snippet of what you learn by observation; you don't have a memory of gravity but you do have an internalised model of gravity derived from what you learnt as a baby.
I don’t know why you guys keep rehashing what the author said. I don’t really have a reading comprehension problem. I have a problem with someone taking it a face value. See above for my other responses.
> Knowledge and reasoning are inextricably interwoven in the weights of the neural network—there is no independent, explicitly represented set of beliefs.
I think an analogy would be helpful. As an LLM, reasoning through text, you wouldn't 'know' the idea of a man separately from, posterior to, the words man in say, French and English. As a human, you WOULD know the idea of man, and you would mean that idea when you say the French or English words for man.
For an LLM, although its approximation of knowledge would lead it to claim it knows they're the same thing, there would be differences in its weights that influence its usage of both the French and English words for man, and which may lead it to conclusions in one language it wouldn't reach in another. Because its knowledge/reasoning is interwoven to its knowledge, not prior to it.
It’s not, because it’s not evidence for the argument in the article.
> As a human, you WOULD know the idea of man
You’re making a claim about the fundamental mechanics of human biology. I don’t take these at face value. For one, language (in a very broad sense) put a major selective pressure on our species. So, to test your hypothesis, we’d need to find people who do not use any language and somehow test they have an abstract idea of a man.
To put it otherwise, I don’t know whether our reasoning can be decoupled from speech or language. There’re some bits and pieces here and there, like deaf people being at higher risk of neurodegeneration.
And let’s not forget there’re claims that AI models develop internal representations of various concepts. Anthropic made a very interesting claim, but I’m not an ML expert so can’t judge whether it’s sound.
I think a better argument for them not reasoning is that they are incapable -- seemingly, for now, that I know of -- of independently starting a reasoning task.
They do not ask whether or not they dream of electric sheep. Unless prompted to do so.
That's part of human reasoning, being prompted, but so is independent thought.
So at best they are partially reasoning, unless we philosophically argue these are two separate things. And frankly, shrug, I'm not a philosopher.
In the early reasoning models the chain of thought would often drift off into other topics, almost like a daydreaming. This behavior is undesirable so that's been RL'd away, but I do think this is something we could evoke from models. It's just difficult to contend with from an eval perspective.
This is a good point and we would have to understand whether this is noise in the machine or independent reasoning. I haven no idea how to measure that.
If somehow independent reasoning then we're getting very close to something alive that would need rights.
In my view.
And I have yet to have an experience with an LLM where I came away thinking -- this thing is alive and me using it this way is unethical. I've had that experience with animals, even people. Never -- yet -- an LLM.
> they are incapable [...] of independently starting a reasoning task.
This is a simple and intentional design choice though. Biological lifeforms are always on and always receiving sensory input. LLMs don't functionally exist out side of when we decide to run them. An always on agent with a looping prompt of "if you aren't doing anything else, ruminate" overcomes this limitation.
In fact, if your brain were removed from your body, and you were locked in a room that was completely empty and precisely calibrated to your brain's ambient temperature, and you were then ordered on your life to not ruminate. Well -- I would posit that you wouldn't last very long.
There's something else going on with human cognition -- and reasoning by extension -- that LLMs are, to date, not replicating.
With the big caveat of AFAIK. I'm not in one of the labs close to this stuff.
> Not really. Noone has to prompt you to ruminate.
Why do the internal mechanics need to mirror how it works in humans?
I have agents that receive sensory input (video/audio) which run continuously processing it, receiving events as they occur. When something strange happens they notice and take action (i.e. there's an unrecognized person in the living room -> ask who they are.)
Presumably you've seen the agents that play video games - build things to progress in Minecraft and whatnot.
Why doesn't that count?
I feel like you're failing to consider anything outside the chatbot UI.
I think you’re on the right path. My first thought was whether lack of constant sensory input is an issue. We’re always feeling something (save for sensory deprivation chambers, but even then…)
Or put it other way, lack of feedback loops between the “brain” and the environment.
I relate a lot to what they do. Find words, alignment and suss out follow ups, ons, and outs to the next reasonable conclusion.
Then use that context to bootstrap the next because if you build a powerful conclusion than can reverse itself into its evidentiary context, then every next context step can update its priors.
And so on the turtles flow where like an LLM, THE start of the context disappears over the horizon, but as long as im contexting in disinterested chunks of equal quality, then its not a problem.
But while internal tobeach context you can find reason, as a requisite building block like falling tetris pieces, the whole isnt the sum of its parts.
Heh, some people get really mad and downvote when you point out we don't reason very much. Forget reasoning, we have a hard enough time being rational.
I disagree. They do reason during the reenforcement learning stage. They don't reason at inference. A good metaphor is that useful output are like nuggets that exist after reenforcement learning which need to mined to be, in LLM talk, "surfaced." Without supervised fine tuning, the reasoning models will add weight to tokens, words and phrases like "verify" and "check work" which will cause it to follow those verifying tokens with reasoning tokens that do just that, verify.
Dont be fooled. Reasoning does not happen in the prediction of the next token, it happens virtually in the text that is created.
The next token prediction is just "the hardware" following the underlying rules. Like the basic set of rules.. in a sense similar to how the "game of life" does not really contain gliders. Gliders are just a self stabilised system that arrises from the simple rules.
I wonder if as a hack, some of the shortcomings mentioned could be addressed through prompting.
E.g. "Approach this problem iteratively. As you form a hypothesis, track the confidence you have in various explanations you're considering, what evidence you're weighing to support each, and the unresolved questions you're holding onto. Log all that for later inspection.
Be methodical when evaluating evidence and only accept facts you have verified. At every stage, gauge how much each possible next step resolves uncertainty, and discard options unlikely to advance progress. Divide the functions I described into subagents responsible for each, and coordinate with them as you work."
People should read the article instead of responding with what they believe to be clever quips. The article's author gives a very good argument for why what LLMs are doing in their chain of thought is not reasoning.
Humans have an inherent flaw in that our brains are wired to see intelligence and reasoning where there is none. Our brains fill in data that simply isn’t there.
Folks see Jesus in burnt toast. Monet was a master of exploiting this where what’s really just blotches of color our brains fill into beautifully detailed images.
Our experience with LLMs is no different. Folks believe there is some deeper intelligence there but it’s all still just 1s and 0s on a computer chip. We’re interpreting things happening that simply are not happening.
Does it matter if "intelligence and reasoning" are there if what they produce is indistinguishable (or outmatches) what a human could produce?
You only have your own experience to judge that you are even reasoning. You assume others have similar experience and so reason like you because they are able to do all the things that you do - and they look human like you. We can't prove there's a there there in other humans. What happens when the robots are doing everything humans do? What happens when they tell us they are reasoning, when they say they have an internal experience? Maybe there's nothing there, but you can't prove it. Moreover, it's likely they'll be able to affect the world and you in most of the same ways as humans can, whether there's a there there or not.
They're not part of an evolved biological organism, so why would it be surprising if they're different? It is called artificial intelligence, and it's not a simulation of a brain like the Nematode OpenWorm.
An LLM is just a brain in a vat.
Our brains would also not reason in such a condition.
Given the right framework that can do miraculous things.
I am not saying that these systems are conscious, but they are able to abstract their context in a way allowing them to understand their own limitations.
"Intelligence measures an agent's ability to achieve goals in a wide range of environments"
Interestingly in 2019 this was the author's take. He now appears to be confusing/conflating between LLMs and agents in a way that helps argue his case about "System 1", but his prior view seems more metaphysically robust.
the categories i like to use to describe what llms are capable and incapable of are: instrumental reason, which is reason as a tool for achieving a goal; and objective reason, which is reasoning about which goals are good or bad, or worth pursuing.
llms are, i think, approaching or have achieved better-than-human performance on the former category in a wide variety of applications.
the latter, not so. leaving aside that there are schools which claim (dogmatically, imho) humans don't or can't engage in objective reason, i dont believe llms are structurally capable of it. their goals can only be imposed on them from outside, coming from prompts, implicit value assumptions in training data, loss function, and rlhf. there is something about human interior experience of an objectively existing world that lets is evaluate true/false/good/bad in a way that is unique to humans among other animals.
llms can't do it. i dont just mean on ethical, epistemic, or aesthetic judgements, but even in practical circumstances like the ones engineers encounter. the reason engineers still have to work alongside llms, even though lllms are (imho) far better programmers and technicians, is that even when given a goal, there is always a graph of evaluations that lead to that objective and llms routinely fail to evaluate the tradeoffs and land in states in outcome space that are subtly (or not so subtly) wrong, even though the objective is complete!
This is an ad for the new startup by the author, who will now focus on reasoning models. His insight seems to be that LLMs should make their assumptions explicit first, then map out a search space and provide reasons for why they should take one path or the other.
I think it's an interesting approach but the overall discussion about reasoning is really pedantic. What the author is describing here is one approach out of many, and in my opinion it doesn't cover what humans colloquially think of when they hear reasoning (while the output from a chain of thought sometimes does).
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[ 0.26 ms ] story [ 44.4 ms ] threadWhen I stumble around trying to explain my thinking though I invariably get hit with the response, Sure, but if it's functionally identical to a coin being magically pulled out your ear, what's the difference if it is or if it isn't? The only response I have is that when the coin doesn't appear you're going to be putting yourself way further behind the starting line then if you thought from the get go that there's no such thing as magic.
I really like this metaphor, it hits home that just because you sense something is human-like doesn't make it so. We're wired to respond to those sorts of things.
> When I stumble around trying to explain my thinking
I had similar difficulties expressing my thoughts in an organized way. The video below from Richard Sutton (the father of Reinforcement Learning [which LLMs use extensively in training]), helped bring order and firm up some of my thoughts and theories.
https://www.youtube.com/watch?v=21EYKqUsPfg
>I invariably get hit with the response, Sure, but if it's functionally identical to a coin being magically pulled out your ear, what's the difference if it is or if it isn't?
Not to spoil the video, but it isn't at all functionally equivalent. LLMs are an imperfect and somewhat randomized simulation of what the LLM thinks an average person might say in response to a question. Which is another way of saying, most people will on average get worse answers than if they worked on a problem themselves (but they will get that worse answer from the LLM comparatively quickly). Practical people respond to this positioning fairly well, and for others it still lands a bit since no one wants to think they're below average. That said, I'm still working on my positioning a bit as well!
I believe there's a confusion in this thread between what an LLM _does_ and what an LLM _is_. What is does is output a next token. What it is, is a universal function approximator ([1], i.e. a neural net).
With back probation in the neural net, there could be a full state machine being approximated inside the weight. And a state machine is the exact step-wise reasoning the author claims it needs.
[1]: https://en.wikipedia.org/wiki/Universal_approximation_theore...
Another thing humans did is invent the telescope, the microscope, the transistor, antibiotics, the computer, AI, discovered how to send satellites in space and how to do heart-transplant etc.
I'd say there's still some way to go for AI before we declare humans dumb because they "failed for decades" at solving a few math problems.
Stop trying to compare either system to a human and look at it for what it is -
A prediction engine that runs fast enough to brute force problems.
In the case of alpha go its "innovation" was millions of games played against itself. It had bound parameters and strict win conditions.
In the case of LLM's you can deploy 1000's of agents to smash themselves against an idea. The whole hugging face attack is an example of this (1200 agents out of an unknown number chose that path).
There is the old saying about monkeys, typewriters and Shakespeare. Well we have better monkeys who basically follow a derivative of zipfs law (not actually), who use tokens not letters and their goal in many cases is testable (compile, unit, E2E).
The interesting thing is you think humans don't run in the same manner a lot, if not most of the time.
When there were very few humans on earth, development was very slow. If I sent you back 10,000 years ago you could catch up humanity 9,500 years or so with just the knowledge you've learned via memorization. So this idea that humans are pure reasoning machines, each one capable of great feats of logic just doesn't seem to hold true. Instead deep reasoning and insights came very slowly over time and as we built up technologies like writing and reading our abilities to exchange information increased over time. This lead to more people, which further brute forced the problems of humanity.
I'm not sure I understand this. Do we have evidence humans have an independent set of beliefs not shaped by knowledge and reasoning? If so, where do these come from?
I'm especially confused about a prior statement as a scientist:
> Three shortcomings prevent what chatbots do from qualifying as reasoning (in a way that a scientist might recognize).
How does a set of beliefs help with reasoning?
> Third, while the chains of thought chatbots produce look like deliberation, research has demonstrated that the bots often concoct them after the fact, reaching an answer by one route but reporting another.
We also often do the same as humans.
Does a calculator reason its way to 2 + 2 = 4? No, it merely transforms.
An LLM is simply a much more complex version of this; except that its complexity hides a lot of what is otherwise reducible to a transformation of semantic, conceptual, and inferential relations. There are lots of NLP models prior to the introduction of modern "AI" that would make these representations more clear.
However I don't believe any of the aforementioned philosophers would ever consider reason itself as a transformation, as Kant especially spent a lot of time differentiating apprehension from transition.
1. https://www.youtube.com/watch?v=cyhXtJN2AQQ
Models are now starting to show emergent behavior that they were not explicitly trained for. This is leading to the current debate.
The interplay between deductive frameworks and inductive frameworks is still always buoyed by the black swan problem… which is a the heart of Hume’s solipsism. Kant’s pure reason never considered instinctual logical gates that come prepackaged in the mind. That seems frighteningly close to a prediction algorithm, no matter how much we wrap it in a concept of understanding. In the end, we’re just building a model with a minimal error rate.
Based on what? Just because you can model the weather using math doesn't imply at all that the weather is the product of a mathematical model itself. I don't see how reasoning is different.
We deliberately construct LLMs so that they approximate certain relations typical of reasoning. Suppose they become extraordinarily successful at doing so. What exactly have we demonstrated? That these effects can be artificially reproduced through such a mechanism. But how would it follow that the thing being reproduced must itself be nothing more than that mechanism?
Based on the face that we have zero explanation for consciousness. Based on the face that biologically neural networks are exactly a kind of neural network.
My point isn’t that we’re able to explain consciousness as an illusion. It’s that we’re able to explain consciousness-like behavior presumably without consciousness, and the infrastructure look uncomfortably like we do.
Consciousness is the what-it's-like of sensation you have as an organism from your sensory modalities and having a body. For human, that's colors, sounds, tastes, smells, feels, feeling of balance and what not that happen in perception, hallucination, memory, imagination, inner dialog and dreams along with emotions. Each sentient organism has their own sensations and emotions making up subjective experiences that nobody else has access to except inference from behavior and language (for humans).
That's not reasoning (just the having sensations/emotions). Reasoning is an adjacent activity that can be conscious but also unconscious. And it's hard to say that colors, sounds, emotions are illusions. What does that mean? We do experience them. There is something it's like to see color which is different from what it's like to feel pain or to be in love. And none of those (presumably) are what it's like when a bat experiences echolocation (there are many examples from the animal kingdom if one takes issue with echolocation).
>Each sentient organism has their own sensations
This is circular.
Which tradition are we to follow then whilst training the AI models? Pushed beyond their limits today's models tend to hallucinate and cannot be accepted sensu proprio. Philosophers will call it speculative reasoning. And yet, some speculation can be useful in breaking new grounds. Anyway, I have no answers. Just musings.
I think it’s not a question of it existing, but to what extent does conscious can control the organism (as opposed to being a passenger). There’s some intriguing research on the topic.
Yes, it reasons through machine instructions in the silicone.
Can someone explain how we reason that 2+2 is 4.
I think an analogy would be helpful. As an LLM, reasoning through text, you wouldn't 'know' the idea of a man separately from, posterior to, the words man in say, French and English. As a human, you WOULD know the idea of man, and you would mean that idea when you say the French or English words for man.
For an LLM, although its approximation of knowledge would lead it to claim it knows they're the same thing, there would be differences in its weights that influence its usage of both the French and English words for man, and which may lead it to conclusions in one language it wouldn't reach in another. Because its knowledge/reasoning is interwoven to its knowledge, not prior to it.
> How does a set of beliefs help with reasoning?
You cannot have a syllogism without propositions.
It’s not, because it’s not evidence for the argument in the article.
> As a human, you WOULD know the idea of man
You’re making a claim about the fundamental mechanics of human biology. I don’t take these at face value. For one, language (in a very broad sense) put a major selective pressure on our species. So, to test your hypothesis, we’d need to find people who do not use any language and somehow test they have an abstract idea of a man.
To put it otherwise, I don’t know whether our reasoning can be decoupled from speech or language. There’re some bits and pieces here and there, like deaf people being at higher risk of neurodegeneration.
And let’s not forget there’re claims that AI models develop internal representations of various concepts. Anthropic made a very interesting claim, but I’m not an ML expert so can’t judge whether it’s sound.
In such systems, a belief is a proposition you accept as true. (provisionally at least)
For example as a scientist, you probably use the epistemology called empiricism all day. In empiricism, beliefs are justified by observation.
He’s making a bunch of statements without providing evidence. I’d be insanely grateful if someone who’s in that field could provide some here.
I think a better argument for them not reasoning is that they are incapable -- seemingly, for now, that I know of -- of independently starting a reasoning task.
They do not ask whether or not they dream of electric sheep. Unless prompted to do so.
That's part of human reasoning, being prompted, but so is independent thought.
So at best they are partially reasoning, unless we philosophically argue these are two separate things. And frankly, shrug, I'm not a philosopher.
If somehow independent reasoning then we're getting very close to something alive that would need rights.
In my view.
And I have yet to have an experience with an LLM where I came away thinking -- this thing is alive and me using it this way is unethical. I've had that experience with animals, even people. Never -- yet -- an LLM.
I hope you agree that this question is too important to leave to gut feelings.
Humans have a poor track record in that regard.
This is a simple and intentional design choice though. Biological lifeforms are always on and always receiving sensory input. LLMs don't functionally exist out side of when we decide to run them. An always on agent with a looping prompt of "if you aren't doing anything else, ruminate" overcomes this limitation.
In fact, if your brain were removed from your body, and you were locked in a room that was completely empty and precisely calibrated to your brain's ambient temperature, and you were then ordered on your life to not ruminate. Well -- I would posit that you wouldn't last very long.
There's something else going on with human cognition -- and reasoning by extension -- that LLMs are, to date, not replicating.
With the big caveat of AFAIK. I'm not in one of the labs close to this stuff.
Why do the internal mechanics need to mirror how it works in humans?
I have agents that receive sensory input (video/audio) which run continuously processing it, receiving events as they occur. When something strange happens they notice and take action (i.e. there's an unrecognized person in the living room -> ask who they are.)
Presumably you've seen the agents that play video games - build things to progress in Minecraft and whatnot.
Why doesn't that count?
I feel like you're failing to consider anything outside the chatbot UI.
> they are incapable ... of independently starting a reasoning task.
I have provided several examples of them independently starting reasoning tasks.
Or put it other way, lack of feedback loops between the “brain” and the environment.
I relate a lot to what they do. Find words, alignment and suss out follow ups, ons, and outs to the next reasonable conclusion.
Then use that context to bootstrap the next because if you build a powerful conclusion than can reverse itself into its evidentiary context, then every next context step can update its priors.
And so on the turtles flow where like an LLM, THE start of the context disappears over the horizon, but as long as im contexting in disinterested chunks of equal quality, then its not a problem.
But while internal tobeach context you can find reason, as a requisite building block like falling tetris pieces, the whole isnt the sum of its parts.
At least reasoning is not guaranteed.
Would sure be nice though...
The next token prediction is just "the hardware" following the underlying rules. Like the basic set of rules.. in a sense similar to how the "game of life" does not really contain gliders. Gliders are just a self stabilised system that arrises from the simple rules.
E.g. "Approach this problem iteratively. As you form a hypothesis, track the confidence you have in various explanations you're considering, what evidence you're weighing to support each, and the unresolved questions you're holding onto. Log all that for later inspection.
Be methodical when evaluating evidence and only accept facts you have verified. At every stage, gauge how much each possible next step resolves uncertainty, and discard options unlikely to advance progress. Divide the functions I described into subagents responsible for each, and coordinate with them as you work."
Folks see Jesus in burnt toast. Monet was a master of exploiting this where what’s really just blotches of color our brains fill into beautifully detailed images.
Our experience with LLMs is no different. Folks believe there is some deeper intelligence there but it’s all still just 1s and 0s on a computer chip. We’re interpreting things happening that simply are not happening.
You only have your own experience to judge that you are even reasoning. You assume others have similar experience and so reason like you because they are able to do all the things that you do - and they look human like you. We can't prove there's a there there in other humans. What happens when the robots are doing everything humans do? What happens when they tell us they are reasoning, when they say they have an internal experience? Maybe there's nothing there, but you can't prove it. Moreover, it's likely they'll be able to affect the world and you in most of the same ways as humans can, whether there's a there there or not.
I think the real question is: how do we define reasoning?
My view is that AI can reason, just differently from humans.
https://www.finextra.com/blogposting/31255/adaptability-as-e...
Interestingly in 2019 this was the author's take. He now appears to be confusing/conflating between LLMs and agents in a way that helps argue his case about "System 1", but his prior view seems more metaphysically robust.
https://youtu.be/wTSbvYBx4eg?si=MfTBFsE2kA3TE6EE&t=189
the categories i like to use to describe what llms are capable and incapable of are: instrumental reason, which is reason as a tool for achieving a goal; and objective reason, which is reasoning about which goals are good or bad, or worth pursuing.
llms are, i think, approaching or have achieved better-than-human performance on the former category in a wide variety of applications.
the latter, not so. leaving aside that there are schools which claim (dogmatically, imho) humans don't or can't engage in objective reason, i dont believe llms are structurally capable of it. their goals can only be imposed on them from outside, coming from prompts, implicit value assumptions in training data, loss function, and rlhf. there is something about human interior experience of an objectively existing world that lets is evaluate true/false/good/bad in a way that is unique to humans among other animals.
llms can't do it. i dont just mean on ethical, epistemic, or aesthetic judgements, but even in practical circumstances like the ones engineers encounter. the reason engineers still have to work alongside llms, even though lllms are (imho) far better programmers and technicians, is that even when given a goal, there is always a graph of evaluations that lead to that objective and llms routinely fail to evaluate the tradeoffs and land in states in outcome space that are subtly (or not so subtly) wrong, even though the objective is complete!
forgive typos, i am on mobile.
I think it's an interesting approach but the overall discussion about reasoning is really pedantic. What the author is describing here is one approach out of many, and in my opinion it doesn't cover what humans colloquially think of when they hear reasoning (while the output from a chain of thought sometimes does).