alt Hacker NewsThe Case That A.I. Is Thinking
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Personal take: LLMs are probably part of the answer (to AGI?) but are hugely handicapped by their current architecture: the only time that long-term memories are formed is during training, and everything after that (once they're being interacted with) sits only in their context window, which is the equivalent of fungible, fallible, lossy short-term memory. [0] I suspect that many things they currently struggle with can be traced back to this.
Overcome this fundamental limitation and we'll have created introspection and self-learning. However, it's hard to predict whether this will allow them to make novel, intuitive leaps of discovery?
[0] It's an imperfect analogy, but we're expecting perfection from creations which are similarly handicapped as Leonard Shelby in the film Memento.
Until we have a testable, falsifiable thesis of how consciousness forms in meat, it is rash to exclude that consciousness could arise from linear algebra. Our study of the brain has revealed an enormous amount about how our anatomy processes information, but nothing of substance on the relationship between matter and consciousness. The software and data of an operating LLM is not purely abstract, it has a physical embodiment as circuits and electrons. Until we understand how matter is connected to consciousness, we also cannot know whether the arrangements and movements of electrons meet the criteria for forming consciousness.
This is merely a debate about what it means to "think." We didn't really previously need to disambiguate thinking / intelligence / consciousness / sentience / ego / identity / etc.
Now, we do. Partly because of this we don't have really well defined ways to define these terms and think about. Can a handheld calculator think? Certainly, depending on how we define "think."
Well, I think because we know how the code is written, in the sense that humans quite literally wrote the code for it - it's definitely not thinking, and it is literally doing what we asked, based on the data we gave it. It is specifically executing code we thought of. The output of course, we had no flying idea it would work this well.
But it is not sentient. It has no idea of a self or anything like that. If it makes people believe that it does, it is because we have written so much lore about it in the training data.
all this "AI IS THINKING/CONSCIOUS/WHATEVER" but nobody seems worried of that implication that, if that is even remotely true, we are creating a new slave market. This either implies that these people don't actually believes any of this boostering rhetoric and are just cynically trying to cash in or that the technical milieu is in a profoundly disturbing place ethically.
To be clear, I don't believe that current AI tech is ever going to be conscious or win a nobel prize or whatever, but if we follow the logical conclusions to this fanciful rhetoric, the outlook is bleak.
The article misses three critical points:
1. Conflates consciousness with "thinking" - LLMs may process information effectively without being conscious, but the article treats these as the same phenomenon
2. Ignores the cerebellum cases - We have documented cases of humans leading normal lives with little to no brain beyond a cerebellum, which contradicts simplistic "brain = deep learning" equivalences
3. Most damning: When you apply these exact same techniques to anything OTHER than language, the results are mediocre. Video generation still can't figure out basic physics (glass bouncing instead of shattering, ropes defying physics). Computer vision has been worked on since the 1960s - far longer than LLMs - yet it's nowhere near achieving what looks like "understanding."
The timeline is the smoking gun: vision had decades of head start, yet LLMs leapfrogged it in just a few years. That strongly suggests the "magic" is in language itself (which has been proven to be fractal and already heavily compressed/structured by human cognition) - NOT in the neural architecture. We're not teaching machines to think.
We're teaching them to navigate a pre-existing map that was already built.
The author searches for a midpoint between "AIs are useless and do not actually think" and "AIs think like humans," but to me it seems almost trivially true that both are possible.
What I mean by that is that I think there is a good chance that LLMs are similar to a subsystem of human thinking. They are great at pattern recognition and prediction, which is a huge part of cognition. What they are not is conscious, or possessed of subjective experience in any measurable way.
LLMs are like the part of your brain that sees something and maps it into a concept for you. I recently watched a video on the creation of AlexNet [0], one of the first wildly successful image-processing models. One of the impressive things about it is how it moves up the hierarchy from very basic patterns in images to more abstract ones (e. g. these two images' pixels might not be at all the same, but they both eventually map to a pattern for 'elephant').
It's perfectly reasonable to imagine that our brains do something similar. You see a cat, in some context, and your brain maps it to the concept of 'cat', so you know, 'that's a cat'. What's missing is a) self-motivated, goal-directed action based on that knowledge, and b) a broader context for the world where these concepts not only map to each other, but feed into a sense of self and world and its distinctions whereby one can say: "I am here, and looking at a cat."
It's possible those latter two parts can be solved, or approximated, by an LLM, but I am skeptical. I think LLMs represent a huge leap in technology which is simultaneously cooler than anyone would have imagined a decade ago, and less impressive than pretty much everyone wants you to believe when it comes to how much money we should pour into the companies that make them.
I've shared this on YN before but I'm a big fan of this piece by Kenneth Taylor (well, an essay pieced together from his lectures).
The Robots Are Coming
https://www.bostonreview.net/articles/kenneth-taylor-robots-...
"However exactly you divide up the AI landscape, it is important to distinguish what I call AI-as-engineering from what I call AI-as-cognitive-science. AI-as-engineering isn’t particularly concerned with mimicking the precise way in which the human mind-brain does distinctively human things. The strategy of engineering machines that do things that are in some sense intelligent, even if they do what they do in their own way, is a perfectly fine way to pursue artificial intelligence. AI-as-cognitive science, on the other hand, takes as its primary goal that of understanding and perhaps reverse engineering the human mind.
[...]
One reason for my own skepticism is the fact that in recent years the AI landscape has come to be progressively more dominated by AI of the newfangled 'deep learning' variety [...] But if it’s really AI-as-cognitive science that you are interested in, it’s important not to lose sight of the fact that it may take a bit more than our cool new deep learning hammer to build a humanlike mind.
[...]
If I am right that there are many mysteries about the human mind that currently dominant approaches to AI are ill-equipped to help us solve, then to the extent that such approaches continue to dominate AI into the future, we are very unlikely to be inundated anytime soon with a race of thinking robots—at least not if we mean by “thinking” that peculiar thing that we humans do, done in precisely the way that we humans do it."
Geoffrey Hinton's recent lecture at the Royal Institute[1] is a fascinating watch. His assertion that human use of language being exactly analogous to neural networks with back-propagation really made me think about what LLMs might be able to do, and indeed, what happens in me when I "think". A common objection to LLM "intelligence" is that "they don't know anything". But in turn... what do biological intelligences "know"?
For example, I "know" how to do things like write constructs that make complex collections of programmable switches behave in certain ways, but what do I really "understand"?
I've been "taught" things about quantum mechanics, electrons, semiconductors, transistors, integrated circuits, instruction sets, symbolic logic, state machines, assembly, compilers, high-level-languages, code modules, editors and formatting. I've "learned" more along the way by trial and error. But have I in effect ended up with anything other than an internalised store of concepts and interconnections? (c.f. features and weights).
Richard Sutton takes a different view in an interview with Dwarkesh Patel[2] and asserts that "learning" must include goals and reward functions but his argument seemed less concrete and possibly just a semantic re-labelling.
[1] https://www.youtube.com/watch?v=IkdziSLYzHw [2] https://www.youtube.com/watch?v=21EYKqUsPfg
I don't see how we make the jump from current LLMs to AGI. May be it's my limited understanding of the research but current LLMs seem to not have any properties that indicate AGI. Would love to get thoughts from someone that understands it
I don't believe LLMs can be conscious during inference because LLM inference is just repeated evaluation of a deterministic [0] pure function. It takes a list of tokens and outputs a set of token probabilities. Any randomness is part of the sampler that selects a token based on the generated probabilities, not the LLM itself.
There is no internal state that persists between tokens [1], so there can be no continuity of consciousness. If it's "alive" in some way it's effectively killed after each token and replaced by a new lifeform. I don't see how consciousness can exist without possibility of change over time. The input tokens (context) can't be enough to give it consciousness because it has no way of knowing if they were generated by itself or by a third party. The sampler mechanism guarantees this: it's always possible that an unlikely token could have been selected by the sampler, so to detect "thought tampering" it would have to simulate itself evaluating all possible partial contexts. Even this takes unreasonable amounts of compute, but it's actually worse because the introspection process would also affect the probabilities generated, so it would have to simulate itself simulating itself, and so on recursively without bound.
It's conceivable that LLMs are conscious during training, but in that case the final weights are effectively its dead body, and inference is like Luigi Galvani poking the frog's legs with electrodes and watching them twitch.
[0] Assuming no race conditions in parallel implementations. llama.cpp is deterministic.
[1] Excluding caching, which is only a speed optimization and doesn't affect results.
I think the challenge with many of these conversations is that they assume consciousness emerges through purely mechanical means.
The “brain as a computer” metaphor has been useful in limited contexts—especially for modeling memory or signal processing; but, I don’t think it helps us move forward when talking about consciousness itself.
Penrose and Hameroff’s quantum consciousness hypothesis, while still very speculative, is interesting precisely because it suggests that consciousness may arise from phenomena beyond classical computation. If that turns out to be true, it would also mean today’s machines—no matter how advanced—aren’t on a path to genuine consciousness.
That said, AI doesn’t need to think to be transformative.
Steam engines weren’t conscious either, yet they reshaped civilization.
Likewise, AI and robotics can bring enormous value without ever approaching human-level awareness.
We can hold both ideas at once: that machines may never be conscious, and still profoundly useful.
TFA is a part of what seems like a never-ending series about concepts that lack a useful definition.
"Thinking" and "intelligence" have no testable definition or specification, therefore it's a complete waste of time to suppose that AI is thinking or intelligent.
I think something that's missing from AI is the ability humans have to combine and think about ANY sequence of patterns as much as we want. A simple example is say I think about a sequence of "banana - car - dog - house". I can if I want to in my mind, replace car with tree, then replace tree with rainbow, then replace rainbow with something else, etc... I can sit and think about random nonsense for as long as I want and create these endless sequences of thoughts.
Now I think when we're trying to reason about a practical problem or whatever, maybe we are doing pattern recognition via probability and so on, and for a lot of things it works OK to just do pattern recognition, for AI as well.
But I'm not sure that pattern recognition and probability works for creating novel interesting ideas all of the time, and I think that humans can create these endless sequences, we stumble upon ideas that are good, whereas an AI can only see the patterns that are in its data. If it can create a pattern that is not in the data and then recognize that pattern as novel or interesting in some way, it would still lack the flexibility of humans I think, but it would be interesting nevertheless.
AI is thinking the same way a film's picture actually moves.
It's an illusion that's good enough that our brains accept it and it's a useful tool.
Many people who object to the idea that current-generation AI is thinking do so only because they believe AI is not "conscious"... but there is no known law in the universe requiring that intelligence and consciousness must always go together. With apologies to René Descartes[a], intelligence and consciousness are different.
Intelligence can be verified and quantified, for example, with tests of common sense and other knowledge.[b] Consciousness, on the other hand, is notoriously difficult if not impossible to verify, let alone quantify. I'd say AI is getting more intelligent, and more reliable, in fits and starts, but it's not necessarily becoming conscious.
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[a] https://en.wikipedia.org/wiki/Cogito%2C_ergo_sum
[b] For example, see https://arxiv.org/abs/2510.18212
The number of people willing to launch into debates about whether LLMs are thinking, intelligent, conscious, etc, without actually defining those terms, never ceases to amaze me.
I'm not sure that "thinking", unlike intelligence, is even that interesting of a concept. It's basically just reasoning/planning (i.e. chained what-if prediction). Sometimes you're reasoning/planning (thinking) what to say, and other times just reasoning/planning to yourself (based on an internal vs external focus).
Of course one can always CHOOSE to make analogies between any two things, in this case the mechanics of what's going on internal to an LLM and a brain, but I'm not sure it's very useful in this case. Using anthropomorphic language to describe LLMs seems more likely to confuse rather than provide any insight, especially since they are built with the sole function of mimicking humans, so you are basically gaslighting yourself if you regard them as actually human-like.
This reads like 2022 hype. It's like people stil do not understand that there's a correlation between exaggerating AI's alleged world-threatening capabilities and AI companies' market share value – and guess who's doing the hyping.
I think we are getting to point where we are trying to figure how important is human experience to intelligence.
Things we do like sleep, meditate, have fun, listen to music etc. do they add to our intelligence? Do they help us have a consistent world model that we build on everyday?
Will we be able to replicate this is in a artificial neural net which is extremely smart in spurts but does not "enjoy" the world it operates in?
Ohio bill in motion to deny AI legal personhood: https://www.legislature.ohio.gov/legislation/136/hb469
So much of the debate of whether AI can think or not reminds me of this scene from The Next Generation: https://youtu.be/ol2WP0hc0NY
LLMs hit two out of the three criteria already - self awareness and intelligence, but we're in a similar state where defining consciousness is such a blurry metric. I feel like it wont be a binary thing, it'll be a group decision by humanity. I think it will happen in the next decade or two, and regardless of the outcome I'm excited I'll be alive to see it. It'll be such a monumentous achievement by humanity. It will drastically change our perspective on who we are and what our role is in the universe, especially if this new life form surpasses us.
Personally, I feel like human intelligence is "unknown black box" + an LLM.
And the LLM part of our intelligence isn't really thinking.
And some people out there have a very, very small "unknown black box".
Helpful to remember that we humans often say "I think" to mean "I am fairly confident based on my hunch", and in that sense AI is very good at hunching.
No idea if this is true or not but I do very much like the animation
> An A.I smarter than a Nobel prize winner.
I don't even know what this means.
If we assembled the sum total of all published human knowledge on a storage medium and gave a computer the ability to search it extremely well in order to answer any question falling within its domain, there, you would have a Nobel Prize beating "A.I".
But this is as "earth-shattering" (/s) as the idea that human knowledge can be stored outside the brain (on paper, flash drives, etc), or that the answer to complex questions can be deterministic.
And then there is the fact that this Noble winner beating "A.I" is highly unlikely to propound any ground-breaking novel ways of thinking and promote and explain it to general acceptance.
The definitions of all these words have been going back and forward and never reached any 100% consensus anyways, so what one person understands of "thinking", "conscious", "intelligence" and so on seems to be vastly different from another person.
I guess this is why any discussion around this ends up with huge conversations, everyone is talking from their own perspective and understanding, while others have different ones, and we're all talking past each other.
There is a whole field trying to just nail down what "knowledge" actually is/isn't, and those people haven't agreed with each other for the duration of hundreds of years, I'm not confident we'll suddenly get a lot better at this.
I guess ultimately, regardless of what the LLMs do, does it matter? Would we understand them better/worse depending on what the answer would be?
During the pandemic, I experimented with vaping marijuana to see if I could improve my sleep quality. It worked to a degree, but after a few weeks of nightly use, I began to experience what I think is depersonalization.
I would be walking with friends and talking about our day, while simultaneously thinking, "this isn't actually me doing this, this is just a surface-level interaction being carried out almost by automation." Between that and the realization that I "hallucinate", i.e. misremember things, overestimate my understanding of things, and ruminate on past interactions or hypothetical ones, my feelings have changed regarding what intelligence and consciousness really mean.
I don't think people acknowledge how much of a "shell" we build up around ourselves, and how much time we spend in sort of a conditioned, low-consciousness state.
In all these discussions there seems to be an inverse correlation between how well people understand what an LLM does and how much they believe it thinks.
If you don't understand what an LLM does – that it is a machine generating a statistically probable token given a set of other tokens – you have a black box that often sounds smart, and it's pretty natural to equate that to thinking.
If AI were really intelligent and thinking, it ought to be able to be trained on its own output. That's the exact same thing we do. We know that doesn't work.
The obvious answer is the intelligence and structure is located in the data itself. Embeddings and LLMs have given us new tools to manipulate language and are very powerful but should be thought of more as a fancy retrieval system than a real, thinking and introspective intelligence.
Models don't have the ability to train themselves, they can't learn anything new once trained, have no ability of introspection. Most importantly, they don't do anything on their own. They have no wants or desires, and can only do anything meaningful when prompted by a human to do so. It's not like I can spin up an AI and have it figure out what it needs to do on its own or tell me what it wants to do, because it has no wants. The hallmark of intelligence is figuring out what one wants and how to accomplish one's goals without any direction.
Every human and animal that has any kind of intelligence has all the qualities above and more, and removing any of them would cause serious defects in the behavior of that organism. Which makes it preposterous to draw any comparisons when its so obvious that so much is still missing.
The debate around whether or not transformer-architecture-based AIs can "think" or not is so exhausting and I'm over it.
What's much more interesting is the question of "If what LLMs do today isn't actual thinking, what is something that only an actually thinking entity can do that LLMs can't?". Otherwise we go in endless circles about language and meaning of words instead of discussing practical, demonstrable capabilities.
I like learning from everyone's perspectives.
I also keep in mind when non-tech people talk about how tech works without an understanding of tech.
We are still having to read this again in 2025? Some will never get it.
Sounds like one of those extraordinary popular delusions to me.
So happy to see Hofstadter referenced!
He's the GOAT in my opinion for "thinking about thinking".
My own thinking on this is that AI actually IS thinking - but its like the MVB of thinking (minimum viable brain)
I find thought experiments the best for this sort of thing:
- Imagine you had long term memory loss so couldn't remember back very long
You'd still be thinking right?
- Next, imagine you go to sleep and lose consciousness for long periods
You'd still be thinking right?
- Next, imagine that when you're awake, you're in a coma and can't move, but we can measure your brain waves still.
You'd still be thinking right?
- Next, imagine you can't hear or feel either.
You'd still be thinking right?
- Next, imagine you were a sociopath who had no emotion.
You'd still be thinking right?
We're just not used to consciousness without any of the other "baggage" involved.
There are many separate aspects of life and shades of grey when it comes to awareness and thinking, but when you take it down to its core, it becomes very hard to differentiate between what an LLM does and what we call "thinking". You need to do it by recognizing the depths and kinds of thoughts that occur. Is the thinking "rote", or is something "special" going on. This is the stuff that Hofstadter gets into(he makes a case for recursion and capability being the "secret" piece - something that LLMs certainly have plumbing in place for!)
BTW, I recommend "Surfaces and Essences" and "I am a strange loop" also by Hofstadter. Good reads!
next up: The Case That Skyrim NPCs Are Alive.
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The New Yorker is owned by Advance Publications, which also owns Conde Nast. "Open" "AI" has struck a deal with Conde Nast to feed SearchGPT and ChatGPT.
This piece is cleverly written and might convince laypeople that "AI" may think in the future. I hope the author is being paid handsomely, directly or indirectly.
Let's quote all the CEO's benefiting from bubble spending, is their fake "AI" llm going to blow up the world or take all our jobs!? Find out in this weeks episode!
> Meanwhile, the A.I. tools that most people currently interact with on a day-to-day basis are reminiscent of Clippy
Can’t take the article seriously after this.
I don't see a good argument being made for what headline claims. Much of the article reads like a general commentary on LLM's, not a case for AI "thinking", in the sense that we understand it.
It would take an absurdly broad definition of the word "think" to even begin to make this case. I'm surprised this is honestly up for debate.
Having seen LLMs so many times produce coherent, sensible and valid chains of reasoning to diagnose issues and bugs in software I work on, I am at this point in absolutely no doubt that they are thinking.
Consciousness or self awareness is of course a different question, and ones whose answer seems less clear right now.
Knee jerk dismissing the evidence in front of your eyes because you find it unbelievable that we can achieve true reasoning via scaled matrix multiplication is understandable, but also betrays a lack of imagination and flexibility of thought. The world is full of bizarre wonders and this is just one more to add to the list.