I love the idea of educating students on the math behind AI to demystify them. But I think it's a little weird to assert "AI is not magic and AI systems do not think. It’s just maths." Equivalent statements could be made about how human brains are not magic, just biology - yet I think we still think.
I agree saying "they don't think" and leaving it at that isn't particularly useful or insightful, it's like saying "submarines don't swim" and refusing to elaborate further. It can be useful if you extend it to "they don't think like you do". Concepts like finite context windows, or the fact that the model is "frozen" and stateless, or the idea that you can transfer conversations between models are trivial if you know a bit about how LLMs work, but extremely baffling otherwise.
> or the fact that the model is "frozen" and stateless,
much like a human adult. Models get updated at a slower frequency than humans. AI systems have access to fetch new information and store it for context.
> or the idea that you can transfer conversations between models are trivial
because computers are better-organized than humanity.
My context window is about a day. I can remember what I had for lunch today, and sometimes what I had for lunch yesterday. Beyond that, my lunches are gone from my context window and are only in my training data. I have vague ideas about what dishes I ate, but don't remember what days specifically. If I had to tell you what separate dishes I ate in the same meal, I don't have specific memories of that. I remember I ate fried plantains, and I ate beans & rice. I assume they were on the same day because they are from the same cuisine, and am confident enough that I would bet money on it, but I don't know for certain.
One of my earliest memories is of painting a ceramic mug when I was about 3 years old. The only reason I remember it is because every now and then I think about what my earliest memory is, and then I refresh my memory of it. I used to remember a few other things from when I was slightly older, but no longer do, because I haven't had reasons to think of them.
I don't think humans have specific black and white differences between types of knowledge that way LLMs do, but there is definitely a lot of behavior that is similar to context window vs training data (and a gradient in between). We remember recent things a lot better than less recent things. The quantity of stuff we can remember in our "working memory" is approximately finite. If you try to hold a complex thought in your mind, you can probably do that indefinitely, but if you then try to hold a second equally complex thought as well, you'll often lose the details of the first thought and need to reread or rederive those details.
A lot of people genuinely can't remember what they did an hour ago, but to be very clear you're implying that an LLM can't "remember" something from an hour, or three hours ago, when it's the opposite.
I can restart a conversation with an LLM 15 days later and the state is exactly as it was.
Can't do that with a human.
The idea that humans have a longer, more stable context window than LLM's, CAN or is even LIKELY to be true given certain activities but please let's be honest about this.
If you talk to someone for an hour about a technical conversation I would guesstimate that 90% of humans would immediately start to lose track of details in about 10 minutes. So they write things down, or they mentally repeat things to themselves they know or have recognized they keep forgetting.
I know this because it's happened continually in tech companies decade after decade.
LLM's have already passed the Turing test. They continue to pass it. They fool and outsmart people day after day.
I'm no fan of the hype AI is receiving, especially around overstating its impact in technical domains, but pretending that LLM's can't or don't consistently perform better than most human adults on a variety of different activities is complete non-sense.
it doesn't sound like you really understand what these statements mean. if LLMs are like any humans it's those with late stage dementia, not healthy adults
It's just provencial nonsense, there's no sound reasoning to it. Reductionism being taken and used as a form of refutation is a pretty common cargo culting behavior I've found.
Overwhelmingly, I just don't think the majority of human beings have the mental toolset to work with ambiguous philosophical contexts. They'll still try though, and what you get out of that is a 4th order baudrillardian simulation of reason.
"Just" is used here as a reductive device. You reduce others to a few sentences.
Sentences constructed of words and representations of ideas defined long before you existed. I question whether you can work with ambiguous contexts as you have had the privilege of them being laid out in language for you already by the time you were born.
From my reference frame you appear to merely be circumlocuting from memory, and become the argument you make about others.
AI and brains can do some, AI and brains definitely provably cannot do others, some others are untestable at present, and nobody really knows enough about what human brains do to be able to tell if or when some existing or future AI can do whatever is needed for the stuff we find special about ourselves.
A lot of people use different definitions, and respond to anyone pointing this out by denying the issue and claiming their own definition is the only sensible one and "obviously" everyone else (who isn't a weird pedant) uses it.
The definition of "thinking" in any of the parent comments or TFA is actually not defined. Like literally no statements are made about what is being tested.
So, if we had that we could actually discuss it. Otherwise it's just opinions about what a person believes thinking is, combined with what LLMs are doing + what the person believes they themselves do + what they believe others do. It's entirely subjective with very low SNR b/c of those confounding factors.
There are people who insist that the halting problem "proves" that machines will never be able to think. That this means they don't understand the difference between writing down (or generating a proof of) the halting problem and the implications of the halting problem, does not stop them from using it.
I don't know that I agree that computation is a variety of thinking. It's certainly influenced by thinking, but I think of thinking as more the thing you do before, after, and in-between the computation, not the actual computation itself.
Statements like "it is bound by the laws of physics" are not "verifiable" by your definition, and yet we safely assume it is true of everything. Everything except the human brain, that is, for which wild speculation that it may be supernatural is seemingly considered rational discussion so long as it satisfies people's needs to believe that they are somehow special in the universe.
I think what many are saying is that of all the things we know best, it's going to be the machines we build and their underlying principles.
We don't fully understand how brains work, but we know brains don't function like a computer. Why would a computer be assumed to function like a brain in any way, even in part, without evidence and just hopes based on marketing? And I don't just mean consumer marketing, but marketing within academia as well. For example, names like "neural networks" have always been considered metaphorical at best.
What has it got to do with anything whether brains function like computers? This is only relevant if you define thinking as something only the brain can do, and then nothing that doesn't work like a brain can think. This would be like defining flight as "what birds do" and then saying airplanes can't fly because they don't work like birds.
And then what do you even mean by "a computer?" This falls into the same trap because it sounds like your statement that brains don't function like a computer is really saying "brains don't function like the computers I am familiar with." But this would be like saying quantum computers aren't computers because they don't work like classical computers.
To use your own example, it's relevant because the definition of "flight" that we apply to planes is not as versatile as the one we apply to birds.
To put this in terms of "results", because that's what your way of thinking insists upon, a plane does not take off and land the way a bird does. This limits a plane's practicality to such an extent that a plane is useless for transportation without all the infrastructure you're probably ignoring with your argument. You might also be ignoring all the side effects planes bring with them.
Would you not agree that if we only ever wanted "flight" for a specific use case that apparently only birds can do after evaluating what a plane cannot do, then planes are not capable of "flight"?
This is the very same problem with "thought" in terms of AI. We're finding it's inadequate for what we want the machine to do. Not only is it inadequate for our current use cases, and not only is it inadequate now, but it will continue to be inadequate until we further pin down what "thought" is and determine what lies beyond the Church-Turing thesis.
Relevant quote: "B. Jack Copeland states that it is an open empirical question whether there are actual deterministic physical processes that, in the long run, elude simulation by a Turing machine; furthermore, he states that it is an open empirical question whether any such processes are involved in the working of the human brain"
Yes, that's a problem of me not being a native english speaker.
"All x aren't y" may mean "not all x are y" in my tongue.
Not a single x is y is more what we would say in the previous case.
But in our case we would say there are x that aren't y.
If thinking is definable, it is wrong that all statements about it are unverifiable (i.e. there are statements about it that are verifiable.)
At the end of the day most people would agree that if something is able to solve a problem without a lookup table / memorisation that it used reasoning to reach the answer. You are really just splitting hairs here.
The difference between thinking and reasoning is that I can "think" that Elvis is still alive, Jewish space lasers are responsible for California wildfires, and Trump was re-elected president in 2020, but I cannot "reason" myself into those positions.
It ties into another aspect of these perennial threads, where it is somehow OK for humans to engage in deluded or hallucinatory thought, but when an AI model does it, it proves they don't "think."
>Equivalent statements could be made about how human brains are not magic, just biology - yet I think we still think.
They're not equivalent at all because the AI is by no means biological. "It's just maths" could maybe be applied to humans but this is backed entirely by supposition and would ultimately just be an assumption of its own conclusion - that human brains work on the same underlying principles as AI because it is assumed that they're based on the same underlying principles as AI.
Unless you're supposing something mystical or supernatural about how brains work, then yes, it is "just" math, there is nothing else it could be. All of the evidence we have shows it's an electrochemical network of neurons processing information. There's no evidence that suggests anything different, or even the need for anything different. There's no missing piece or deep mystery to it.
It's on those who want alternative explanations to demonstrate even the slightest need for them exists - there is no scientific evidence that exists which suggests the operation of brains as computers, as information processors, as substrate independent equivalents to Turing machines, are insufficient to any of the cognitive phenomena known across the entire domain of human knowledge.
We are brains in bone vats, connected to a wonderful and sophisticated sensorimotor platform, and our brains create the reality we experience by processing sensor data and constructing a simulation which we perceive as subjective experience.
The explanation we have is sufficient to the phenomenon. There's no need or benefit for searching for unnecessarily complicated alternative interpretations.
If you aren't satisfied with the explanation, it doesn't really matter - to quote one of Neil DeGrasse Tyson's best turns of phrase: "the universe is under no obligation to make sense to you"
If you can find evidence, any evidence whatsoever, and that evidence withstands scientific scrutiny, and it demands more than the explanation we currently have, then by all means, chase it down and find out more about how cognition works and expand our understanding of the universe. It simply doesn't look like we need anything more, in principle, to fully explain the nature of biological intelligence, and consciousness, and how brains work.
Mind as interdimensional radios, mystical souls and spirits, quantum tubules, none of that stuff has any basis in a ruthlessly rational and scientific review of the science of cognition.
That doesn't preclude souls and supernatural appearing phenomena or all manner of "other" things happening. There's simply no need to tie it in with cognition - neurotransmitters, biological networks, electrical activity, that's all you need.
>it doesn't really matter - to quote one of Neil DeGrasse Tyson's best turns of phrase: "the universe is under no obligation to make sense to you"
Right back at you, brochacho. I'm not the one making a positive claim here. You're the one who insists that it must work in a specific way because you can't conceive of any alternatives. I have never seen ANY evidence or study linking any existent AI or computer system to human cognition.
>There's no need or benefit for searching for unnecessarily complicated alternative interpretations.
Thanks, if it's alright with you I might borrow this argument next time somebody tries to tell me the world isn't flat.
>It simply doesn't look
That's one of those phrases you use when you're REALLY confident that you know what you're talking about.
> like we need anything more, in principle, to fully explain the nature of biological intelligence, and consciousness, and how brains work.
Please fully explain the nature of intelligence, consciousness, and how brains work.
>Mind as interdimensional radios, mystical souls and spirits, quantum tubules, none of that stuff has any basis in a ruthlessly rational and scientific review of the science of cognition.
well i definitely never said anything even remotely similar to that. If i didn't know any better i might call this argument a "hallucination".
Panpsychism is actually quite reasonable in part because it changes the questions you ask. Instead of “does it think” you need to ask “in what ways can it think, and in what ways is it constrained? What types of ‘experience/qualia’ can this system have, and what can’t it have?”
When you think in these terms, it becomes clear that LLMs can’t have certain types of experiences (eg see in color) but could have others.
A “weak” panpsychism approach would just stop at ruling out experience or qualia based on physical limitations. Yet I prefer the “strong” pansychist theory that whatever is not forbidden is required, which begins to get really interesting (would imply that for example an LLM actually experiences the interaction you have with it, in some way).
But parent didn't try to apply "it's just maths" to humans. He said one could just as easily say, as some do: "Humans are just biology, hence they're not magic". Our understanding of mathematics, including the maths of transformer models is limited, just as our understanding of biology. Some behaviors of these models have taken researches by surprise, and future surprises are not at all excluded. We don't know exactly how far they will evolve.
As for applying the word thinking to AI systems, it's already in common usage and this won't change. We don't have any other candidate words, and this one is the closest existing word for referencing a computational process which, one must admit, is in many ways (but definitely not in all ways) analogous to human thought.
Human brains and experiences seem to be constrained by the laws of quantum physics, which can be simulated to arbitrary fidelity on a computer. Nit sure where Godel’s incompleteness theory would even come in here…
how are we going to deduce/measure/know the initialization and rules for consciousness? do you see any systems as not encodable/simulatable by quantum?
I think you are asking whether consciousness might be a fundamentally different “thing” from physics and thus hard or impossible to simulate.
I think there is abundant evidence that the answer is ‘no’. The main reason is that consciousness doesn’t give you new physics, it follows the same rules and restrictions. It seems to be “part of” the standard natural universe, not something distinct.
Can you look at any arbitrary program and tell if it halts without running it indefinitely? If so, you should explain how and collect your Nobel. Telling everybody whether the Collatz conjecture is correct is a good warm up. If not, you can’t solve the halting program either. What does that have to do with consciousness though?
Having read “I Am a Strange Loop” I do not believe Hofstadter indicates that the existence of Gödel’s theorem precludes consciousness being realizable on a Turing machine. Rather if I recall correctly he points out that as a possible argument and then attempts to refute it.
On the other hand Penrose is a prominent believer that human’s ability to understand Gödel’s theorem indicates consciousness can’t be realized on a Turing machine but there’s far from universal agreement on that point.
per halting problem: any system capable of self reference has unprovable (un)truths, the system can not be complete and consistent. consciousness falls under this umbrella
I'll try and ask OG q more clearly: why would the brain, consciousness, be formalizable?
I think there's a yearn view nature as adhering to an underlying model, and a contrary view that consciousness is transcendental, and I lean towards the latter
> that human brains work on the same underlying principles as AI
That wasn't the assumption though, it was only that human brains work by some "non-magical" electro-chemical process which could be described as a mechanism, whether that mechanism followed the same principles of AI or not.
Straw man. The person who you're responding to talked about "equivalent statements" (emphasis added), whereas you appear to be talking about equivalent objects (AIs vs. brains), and pointing out the obvious flaw in this argument, that AIs aren't biology. The obvious flaw in the wrong argument, that is.
Indeed, people confidently assert as established fact things like "brains are bound by the laws of physics" and therefore "there can't be anything special" about them, so "consciousness is an illusion" and "the mind is a computer", all with absolute conviction but with very little understanding of what physics and maths really do and do not say about the universe. It's a quasi-religious faith in a thing not fully comprehended. I hope in the long run some humility in the face of reality will eventually be (re)learned.
If your position is that brains are not actually bound by the laws of physics -- that they operate on some other plane of existence unbound by any scientifically tested principle -- then it is not only your ideological opposites who have quasi-religious faith in a thing not fully comprehended.
My "position" isn't remotely that. The problem with "brains are bound by the laws of physics" isn't that there's something special about brains. It's that physics doesn't consist of "laws" that things are "bound" by. It consists of theories that attempt to describe.
These theories are enormously successful, but they are also known to be variously incomplete, inconsistent, non-deterministic, philosophically problematic, open to multiple interpretations and only partially understood in their implications, with links between descriptions of things at different scales a particularly challenging and little understood topic. The more you learn about physics (and while I'm no physicist, I have a degree in the subject and have learned a great deal more since) the more you understand the limits of what we know.
Anybody who thinks there's no mystery to physics just doesn't know much about it. Anybody who confidently
asserts as fact things like "the brain consists of protons, neutrons and electrons so it's impossible for it to do anything a computer can't do" is deducing things from their own ignorance.
This. People do not understand the implications of the most basic facts of modern science. Gravitation is instantaneous action at a distance via an "occult" force (to quote Newton's contemporaries).
Lot's of assumptions about humanity and how unique we are constantly get paraded in this conversation. Ironically, the people who tout those perspectives are the least likely to understand why we're really not all that "special" from a very factual and academic perspective.
You'd think it would unlock certain concepts for this class of people, but ironically, they seem unable to digest the information and update their context.
A large number of adults I encounter are functionally illiterate, including business people in very high up positions. They are also almost 100% MATHEMATICALLY illiterate, not only unable to solve basic algebra and geometry problems, but completely unable to reason about statistical and probabilistic situations encountered in every day life. This is why gambling is so popular and why people are constantly fooled by politicians. It's bad enough to be without words in the modern world, but being without numbers makes you vulnerable to all manner of manipulations.
Gambling exists more because of people dopamine systems than math...though I get the overall drift. People are fooled by politicians because ?? Also not really math related I think.
I have yet to hear any plausible definition of "thought" that convincingly places LLMs and brains on opposite sides of it without being obviously contrived for that purpose.
A college level approach could look at the line between Math/Science/Physics and Philosophy. One thing from the article that stood out to me was that the introduction to their approach started with a problem about classifying a traffic light. Is it red or green?
But the accompanying XY plot showed samples that overlapped or at least were ambiguous. I immediately lost a lot of my interest in their approach, because traffic lights by design are very clearly red, or green. There aren't mauve or taupe lights that the local populace laughs at and says, "yes, that's mostly red."
I like the idea of studying math by using ML examples. I'm guessing this is a first step and future education will have better examples to learn from.
> traffic lights by design are very clearly red, or green
I suspect you feel this because you are observing the output of a very sophisticated image processing pipeline in your own head. When you are dealing with raw matrixes of rgb values it all becomes a lot more fuzzy. Especially when you encounter different illuminations, exposures and the cropping of the traffic light has noise on it. Not saying it is some intractably hard machine vision problem, because it is not. But there is some variety and fuzzyness there in the raw sensor measurements.
We observe through our senses geometric relationships.
Syntax is exactly that; letters, sentences, paragraphs organized in spatial/geometric relationships.
At best thought is recreation of neural networks in the brain which only exist as spatial relationships.
Our senses operate on spatial relationships; enough light to work by, and food relative to stomach to satisfy our biological impetus to survive (which is spatial relationships of biochemistry).
The idea of "thought" as anything but biology makes little sense to me then as a root source is clearly observable. Humanity, roughly, repeats the same social story. All that thought does not seem to be all that useful as we end up in the same place; the majority as serfs of aristocracy.
Personally would prefer less "thought" role-play and more people taking the load of the labor they exploit to enable them to sit and "think".
We really don't know how consciousness works. The popular theories that it's emergent might be proven correct, or might be proven to be like the idea that phlogiston built up in a vacuum, putting out flames.
That's where these threads always end up. Someone asserts, almost violently, that AI does not and/or cannot "think." When asked how to falsify their assertion, perhaps by explaining what exactly is unique about the human brain that cannot and/or will not be possible to emulate, that's the last anyone ever hears from them. At least until the next "AI can't think" story gets posted.
The same arguments that appeared in 2015 inevitably get trotted out, almost verbatim, ten years later. It would be amusing on other sites, but it's just pathetic here.
Consider that you might have become polarized yourself. I often encounter good arguments against current AI systems emulating all essential aspects of human thinking. For example, the fact that they can't learn from few examples, that they can't perform simple mathematical operations without access to external help (via tool calling) or that they have to expend so much more energy to do their magic (and yes, to me they are a bit magical), which makes some wonder if what these models do is a form of refined brute-force search, rather than ideating.
Personally, I'm ok with reusing the word "thinking", but there are dogmatic stances on both sides. For example, lots of people decreeing that biology in the end can't but reduce to maths, since "what else could it be". The truth is we don't actually know if it is possible, for any conceivable computational system, to emulate all essential aspects of human thought. There are good arguments for this (in)possibility, like those presented by Roger Penrose in "the Emperor's new Mind" and "Shadows of the Mind".
For example, the fact that they can't learn from few examples
For one thing, yes, they can, obviously [1] -- when's the last time you checked? -- and for another, there are plenty of humans who seemingly cannot.
The only real difference is that with an LLM, when the context is lost, so is the learning. That will obviously need to be addressed at some point.
that they can't perform simple mathematical operations without access to external help (via tool calling)
But yet you are fine with humans requiring a calculator to perform similar tasks? Many humans are worse at basic arithmetic than an unaided transformer network. And, tellingly, we make the same kinds of errors.
or that they have to expend so much more energy to do their magic (and yes, to me they are a bit magical), which makes some wonder if what these models do is a form of refined brute-force search, rather than ideating.
Well, of course, all they are doing is searching and curve-fitting. To me, the magical thing is that they have shown us, more or less undeniably (Penrose notwithstanding), that that is all we do. Questions that have been asked for thousands of years have now been answered: there's nothing special about the human brain, except for the ability to form, consolidate, consult, and revise long-term memories.
That's post-training. The complaint I'm referring to is to the huge amounts of data (end energy) required during training - which is also a form of learning, after all. Sure, there are counter-arguments, for example pointing to the huge amount of non-textual data a child ingests, but these counter-arguments are not waterproof themselves (for example, one can point out that we are discussing text-only tasks). The discussion can go on and on, my point was only that cogent arguments are indeed often presented, which you were denying above.
> there are plenty of humans who seemingly cannot
This particular defense of LLMs has always puzzled me. By this measure, simply because there are sufficiently impaired humans, AGI has already been achieved many decades ago.
> But yet you are fine with humans requiring a calculator to perform similar tasks
I'm talking about tasks like multiplying two 4-digit numbers (let's say 8-digit, just to be safe, for reasoning models), which 5th or 6th graders in the US are expected to be able to do with no problem - without using a calculator.
> To me, the magical thing is that they have shown us, more or less undeniably (Penrose notwithstanding), that that is all we do.
Or, to put it more tersely, they have shown you that that is all we do. Penrose, myself, and lots of others don't see it quite like that. (Feeling quite comfortable being classed in the same camp with the greatest living physicist, honestly. ;) To me what LLMs do is approximate one aspect of our minds. But I have a strong hunch that the rabbit hole goes much deeper, your assessment notwithstanding.
No, it is not. Read the paper. They are discussing an emergent property of the context itself: "For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model."
I'm talking about tasks like multiplying two 4-digit numbers (let's say 8-digit, just to be safe, for reasoning models), which 5th or 6th graders in the US are expected to be able to do with no problem - without using a calculator.
So am I. See, for example, Karpathy's discussion of native computation: https://youtu.be/7xTGNNLPyMI?si=Gckcmp2Sby4SlKje&t=6416 (starts at 1:46:56). The first few tokens in the context actually serve as some sort of substrate for general computation. I don't pretend to understand that, and it may still be something of an open research topic, but it's one more unexpected emergent property of transformers.
You'd be crazy to trust that property at this stage -- at the time Karpathy was making the video, he needed to explicitly tell the model to "Use code" if he didn't want it to just make up solutions to more complex problems -- but you'd also be crazy to trust answers from a 5th-grader who just learned long division last week.
Feeling quite comfortable being classed in the same camp with the greatest living physicist, honestly.
Not a great time for you to rest on your intellectual laurels. Same goes for Penrose.
Yes, it is. You seem to have misunderstood what I wrote. The critique I was pointing to is of the amount of examples and energy needed during model training, which is what the "learning" in "machine learning" actually refers to. The paper uses GPT-3 which had already absorbed all that data and electricity. And the "learning" the paper talks about is arguably not real learning, since none of the acquired skills persists beyond the end of the session.
> So am I.
This is easy to settle. Go check any frontier model and see how far they get with multiplying numbers with tool calling disabled.
> Not a great time for you to rest on your intellectual laurels. Same goes for Penrose.
Neither am I resting, nor are there much laurels to rest on, at least compared to someone like Penrose. As for him, give the man a break, he's 94 years old and still sharp as a tack and intellectually productive. You're the one who's resting, imagining you've settled a question which is very much still open. Certainty is certainly intoxicating, so I understand where you're coming from, but claiming anyone who doubts computationalism is not bringing any arguments to the table is patently absurd.
Yes, it is. You seem to have misunderstood what I wrote. The critique I was pointing to is of the amount of examples and energy needed during model training, which is what the "learning" in "machine learning" actually refers to. The paper uses GPT-3 which had already absorbed all that data and electricity. And the "learning" the paper talks about is arguably not real learning, since none of the acquired skills persists beyond the end of the session.
Nobody is arguing about power consumption in this thread (but see below), and in any case the majority of power consumption is split between one-time training and the burden of running millions of prompts at once. Processing individual prompts costs almost nothing.
And it's already been stipulated that lack of long-term memory is a key difference between AI and human cognition. Give them some time, sheesh. This stuff's brand new.
This is easy to settle. Go check any frontier model and see how far they get with multiplying numbers with tool calling disabled.
Yes, it is very easy to settle. I ran this session locally in Qwen3-Next-80B-A3B-Instruct-Q6_K: https://pastebin.com/G7Ewt5Tu
This is a 6-bit quantized version of a free model that is very far from frontier level. It traces its lineage through DeepSeek, which was likely RL-trained by GPT 4.something. So 2 out of 4 isn't bad at all, really. My GPU's power consumption went up by about 40 watts while running these queries, a bit more than a human brain.
If I ask the hardest of those questions on Gemini 3, it gets the right answer but definitely struggles: https://pastebin.com/MuVy9cNw
As for him, give the man a break, he's 94 years old and still sharp as a tack and intellectually productive.
(Shrug) As long as he chooses to contribute his views to public discourse, he's fair game for criticism. You don't have to invoke quantum woo to multiply numbers without specialized tools, as the tests above show. Consequently, I believe that a heavy burden of proof lies with anyone who invokes quantum woo to explain any other mental operations. It's a textbook violation of Occam's Razor.
Usually it is the work of the one claiming something to prove it.
So if you believe that AI does "think" you are expected to show me that it really does.
Claiming it "thinks - prove otherwise" is just bad form and also opens the discussion up for moving the goalposts just as you did with your brain emulation statement. Or you could just not accept any argument made or circumvent it by stating the one trying to disprove your assertion got the definition wrong.
There are countless ways to start a bad faith argument using this methodology, hence: Define property -> prove property.
Conversely, if the one asserting something doesn't want to define it there is no useful conversation to be had. (as in: AI doesn't think - I won't tell you what I mean by think)
PS: Asking someone to falsify their own assertion doesn't seem a good strategy here.
PPS: Even if everything about the human brain can be emulated, that does not constitute progress for your argument, since now you'd have to assert that AI emulates the human brain perfectly before it is complete. There is no direct connection between "This AI does not think" to "The human brain can be fully emulated". Also the difference between "does not" and "can not" is big enough here that mangling them together is inappropriate.
So if you believe that AI does "think" you are expected to show me that it really does.
A lot of people seemingly haven't updated their priors after some of the more interesting results published lately, such as the performance of Google's and OpenAI's models at the 2025 Math Olympiad. Would you say that includes yourself?
If so, what do the models still have to do in order to establish that they are capable of all major forms of reasoning, and under what conditions will you accept such proof?
It definietly includes myself, I don't have the interest to stay updated here.
For that matter I have no opinion on if AI does think or not, I simply don't care.
Therefore I also really can't answer your question in what more a model has to do to establish that they are thinking (does being able to use all major forms of reasoning constitute the capability of thought to you?).
I can say however, that any such proof would have to be on a case-by-case basis given my current understanding on AI is designed.
Well first of all I never claimed that I was capable of thinking (smirk).
We also haven't agreed on a definition of "thinking" yet, so as you can read in my previous comment, there is no meaningful conversation to be had.
I also don't understand how your oddly aggresive phrasing adds to the conversation,
but if it helps you: my rights and protections do not depend on whether I'm able to prove to you that I am thinking.
(It also derails the conversation for what it's worth - it's a good strategy in the debating club, but these are about winning or loosing and not about fostering and obtaining knowledge)
Whatever you meant to say with "Sometimes, because of the consequences of otherwise, the order gets reversed" eludes me as well.
If I say I'm innocent, you don't say I have to prove it. Some facts are presumed to be true without burden of evidence because otherwise it could cause great harm.
So we don't require, say, minorities or animals to prove they have souls, we just inherently assume they do and make laws around protecting them.
Thank you for the clarification.
If you expect me to justify an action depending on you being innocent, then I actually do need you to prove it.
I wouldn't let you sleep in my room assuming you're innocent - or in your words: because of the consequences of otherwise.
It feels like you're moving the goalposts here: I don't want to justify an action based on something, i just want to know if something has a specific property.
With regards to the topic: Does AI think?
I don't know, but I also don't want to act upon knowing if it does (or doesn't for that matter).
In other words, I don't care.
The answer could go either way, but I'd rather say that I don't know (especially since "thinking" is not defined).
That means that I can assume both and consider the consequences using some heuristic to decide which assumption is better given the action I want to justify doing or not doing.
If you want me to believe an AI thinks, you have to prove it, if you want to justify an action you may assume whatever you deem most likely.
And if you want to know if an AI thinks, then you literally can't assume it does; simple as that.
Someone asserts, almost religiously, that LLMs do and/or can "think." When asked how to falsify their assertion, perhaps by explaining what exactly is "thinking" in the human brain that can and/or will be possible to emulate...
Err, no, that’s not what’s happening. Nobody, at least in this thread (and most others like it I’ve seen), is confidently claiming LLMs can think.
There are people confidently claiming they can’t and then other people expressing skepticism at their confidence and/or trying to get them to nail down what they mean.
Or they just point to the turing test which was the defacto standard test for something so nebulous. And behold: LLM can pass the turing test. So they think. Can you come up with something better (than the turing test)?
But the Turing test (which I concede, LLMs do pass) doesn't test if some system is thinking; it tests if the system can convince an unbiased observer that it is thinking. I cannot come up with a better "is this thing thinking" test, but that doesn't mean that such a test can't exist; I'm sure there are much smarter people then me trying to solve this problem.
When asked how to falsify their assertion, perhaps by explaining what exactly is "thinking" in the human brain that can and/or will be possible to emulate...
... someone else points out that the same models that can't "think" are somehow turning in gold-level performance at international math and programming competitions, making Fields Medalists sit up and take notice, winning art competitions, composing music indistinguishable from human output, and making entire subreddits fail the Turing test.
> That's kind of a big difference, wouldn't you say?
To their utility.
Not sure if it matters on the question "thinking?"; even if for the debaters "thinking" requires consciousness/qualia (and that varies), there's nothing more than guesses as to where that emerges from.
For my original earlier reply, the main subtext would be: "Your complaint is ridiculously biased."
For the later reply about chess, perhaps: "You're asserting that tricking, amazing, or beating a human is a reliable sign of human-like intelligence. We already know that is untrue from decades of past experience."
You're asserting that tricking, amazing, or beating a human is a reliable sign of human-like intelligence.
I don't know who's asserting that (other than Alan Turing, I guess); certainly not me. Humans are, if anything, easier to fool than our current crude AI models are. Heck, ELIZA was enough to fool non-specialist humans.
In any case, nobody was "tricked" at the IMO. What happened there required legitimate reasoning abilities. The burden of proof falls decisively on those who assert otherwise.
