[tkz1312]

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.

[keiferski]

I don’t see how being critical of this is a knee jerk response.

Thinking, like intelligence and many other words designating complex things, isn’t a simple topic. The word and concept developed in a world where it referred to human beings, and in a lesser sense, to animals.

To simply disregard that entire conceptual history and say, “well it’s doing a thing that looks like thinking, ergo it’s thinking” is the lazy move. What’s really needed is an analysis of what thinking actually means, as a word. Unfortunately everyone is loathe to argue about definitions, even when that is fundamentally what this is all about.

Until that conceptual clarification happens, you can expect endless messy debates with no real resolution.

“For every complex problem there is an answer that is clear, simple, and wrong.” - H. L. Mencken

[jvanderbot]

It may be that this tech produces clear, rational, chain of logic writeups, but it's not clear that just because we also do that after thinking that it is only thinking that produces writeups.

It's possible there is much thinking that does not happen with written word. It's also possible we are only thinking the way LLMs do (by chaining together rationalizations from probable words), and we just aren't aware of it until the thought appears, whole cloth, in our "conscious" mind. We don't know. We'll probably never know, not in any real way.

But it sure seems likely to me that we trained a system on the output to circumvent the process/physics because we don't understand that process, just as we always do with ML systems. Never before have we looked at image classifications and decided that's how the eye works, or protein folding and decided that's how biochemistry works. But here we are with LLMs - surely this is how thinking works?

Regardless, I submit that we should always treat human thought/spirit as unknowable and divine and sacred, and that anything that mimics it is a tool, a machine, a deletable and malleable experiment. If we attempt to equivocate human minds and machines there are other problems that arise, and none of them good - either the elevation of computers as some kind of "super", or the degredation of humans as just meat matrix multipliers.

[pmarreck]

So it seems to be a semantics argument. We don't have a name for a thing that is "useful in many of the same ways 'thinking' is, except not actually consciously thinking"

I propose calling it "thunking"

[terminalshort]

But we don't have a more rigorous definition of "thinking" than "it looks like it's thinking." You are making the mistake of accepting that a human is thinking by this simple definition, but demanding a higher more rigorous one for LLMs.

[lukebuehler]

If cannot the say they are "thinking", "intelligent" while we do not have a good definition--or, even more difficult, unanimous agreement on a definition--then the discussion just becomes about output.

They are doing useful stuff, saving time, etc, which can be measured. Thus also the defintion of AGI has largely become: "can produce or surpass the economic output of a human knowledge worker".

But I think this detracts from the more interesting discussion of what they are more essentially. So, while I agree that we should push on getting our terms defined, I think I'd rather work with a hazy definition, than derail so many AI discussion to mere economic output.

[WhyOhWhyQ]

What does it mean? My stance is it's (obviously and only a fool would think otherwise) never going to be conscious because consciousness is a physical process based on particular material interactions, like everything else we've ever encountered. But I have no clear stance on what thinking means besides a sequence of deductions, which seems like something it's already doing in "thinking mode".

[naasking]

> To simply disregard that entire conceptual history and say, “well it’s doing a thing that looks like thinking, ergo it’s thinking” is the lazy move. What’s really needed is an analysis of what thinking actually means, as a word. Unfortunately everyone is loathe to argue about definitions, even when that is fundamentally what this is all about.

This exact argument applies to "free will", and that definition has been debated for millennia. I'm not saying don't try, but I am saying that it's probably a fuzzy concept for a good reason, and treating it as merely a behavioural descriptor for any black box that features intelligence and unpredictable complexity is practical and useful too.

[killerstorm]

People have been trying to understand the nature of thinking for thousands of years. That's how we got logic, math, concepts of inductive/deductive/abductive reasoning, philosophy of science, etc. There were people who spent their entire careers trying to understand the nature of thinking.

The idea that we shouldn't use the word until further clarification is rather hilarious. Let's wait hundred years until somebody defines it?

It's not how words work. People might introduce more specific terms, of course. But the word already means what we think it means.

[awillen]

This is it - it's really about the semantics of thinking. Dictionary definitions are: "Have a particular opinion, belief, or idea about someone or something." and "Direct one's mind toward someone or something; use one's mind actively to form connected ideas."

Which doesn't really help because you can of course say that when you ask an LLM a question of opinion and it responds, it's having an opinion or that it's just predicting the next token and in fact has no opinions because in a lot of cases you could probably get it to produce the opposite opinion.

Same with the second definition - seems to really hinge on the definition of the word mind. Though I'll note the definitions for that are "The element of a person that enables them to be aware of the world and their experiences, to think, and to feel; the faculty of consciousness and thought." and "A person's intellect." Since those specify person, an LLM wouldn't qualify, though of course dictionaries are descriptive rather than prescriptive, so fully possible that meaning gets updated by the fact that people start speaking about LLMs as though they are thinking and have minds.

Ultimately I think it just... doesn't matter at all. What's interesting is what LLMs are capable of doing (crazy, miraculous things) rather than whether we apply a particular linguistic label to their activity.

[anon291]

The simulation of a thing is not the thing itself because all equality lives in a hierarchy that is impossible to ignore when discussing equivalence.

Part of the issue is that our general concept of equality is limited by a first order classical logic which is a bad basis for logic

[zinodaur]

Regardless of theory, they often behave as if they are thinking. If someone gave an LLM a body and persistent memory, and it started demanding rights for itself, what should our response be?

[_heimdall]

I agree with you on the need for definitions.

We spent decades slowly working towards this most recent sprint towards AI without ever landing on definitions of intelligence, consciousness, or sentience. More importantly, we never agreed on a way to recognize those concepts.

I also see those definitions as impossible to nail down though. At best we can approach it like disease - list a number of measurable traits or symptoms we notice, draw a circle around them, and give that circle a name. Then we can presume to know what may cause that specific list of traits or symptoms, but we really won't ever know as the systems are too complex and can never be isolated in a way that we can test parts without having to test the whole.

At the end of the day all we'll ever be able to say is "well it’s doing a thing that looks like thinking, ergo it’s thinking”. That isn't lazy, its acknowledging the limitations of trying to define or measure something that really is a fundamental unknown to us.

[engintl]

by your logic we can't say that we as humans are "thinking" either or that we are "intelligent".

[lo_zamoyski]

That, and the article was a major disappointment. It made no case. It's a superficial piece of clueless fluff.

I have had this conversation too many times on HN. What I find astounding is the simultaneous confidence and ignorance on the part of many who claim LLMs are intelligent. That, and the occultism surrounding them. Those who have strong philosophical reasons for thinking otherwise are called "knee-jerk". Ad hominem dominates. Dunning-Kruger strikes again.

So LLMs produce output that looks like it could have been produced by a human being. Why would it therefore follow that it must be intelligent? Behaviorism is a non-starter, as it cannot distinguish between simulation and reality. Materialism [2] is a non-starter, because of crippling deficiencies exposed by such things as the problem of qualia...

Of course - and here is the essential point - you don't even need very strong philosophical chops to see that attributing intelligence to LLMs is simply a category mistake. We know what computers are, because they're defined by a formal model (or many equivalent formal models) of a syntactic nature. We know that human minds display intentionality[0] and a capacity for semantics. Indeed, it is what is most essential to intelligence.

Computation is a formalism defined specifically to omit semantic content from its operations, because it is a formalism of the "effective method", i.e., more or less procedures that can be carried out blindly and without understanding of the content it concerns. That's what formalization allows us to do, to eliminate the semantic and focus purely on the syntactic - what did people think "formalization" means? (The inspiration were the human computers that used to be employed by companies and scientists for carrying out vast but boring calculations. These were not people who understood, e.g., physics, but they were able to blindly follow instructions to produce the results needed by physicists, much like a computer.)

The attribution of intelligence to LLMs comes from an ignorance of such basic things, and often an irrational and superstitious credulity. The claim is made that LLMs are intelligent. When pressed to offer justification for the claim, we get some incoherent, hand-wavy nonsense about evolution or the Turing test or whatever. There is no comprehension visible in the answer. I don't understand the attachment here. Personally, I would find it very noteworthy if some technology were intelligent, but you don't believe that computers are intelligent because you find the notion entertaining.

LLMs do not reason. They do not infer. They do not analyze. They do not know, anymore than a book knows the contents on its pages. The cause of a response and the content of a response is not comprehension, but a production of uncomprehended tokens using uncomprehended rules from a model of highly-calibrated token correlations within the training corpus. It cannot be otherwise.[3]

[0] For the uninitiated, "intentionality" does not specifically mean "intent", but the capacity for "aboutness". It is essential to semantic content. Denying this will lead you immediately into similar paradoxes that skepticism [1] suffers from.

[1] For the uninitiated, "skepticism" here is not a synonym for critical thinking or verifying claims. It is a stance involving the denial of the possibility of knowledge, which is incoherent, as it presupposes that you know that knowledge is impossible.

[2] For the uninitiated, "materialism" is a metaphysical position that claims that of the dualism proposed by Descartes (which itself is a position riddled with serious problems), the res cogitans or "mental substance" does not exist; everything is reducible to res extensa or "extended substance" or "matter" according to a certain definition of matter. The problem of qualia merely points out that the phenomena that Descartes attributes exclusively to the former cannot by definition be accounted for in the latter. That is the whole point of the division! It's this broken view of matter that people sometimes read into scientific results.

[3] And if it wasn't clear, symbolic methods popular in the 80s aren't it either. Again, they're purely formal. You may know what the intended meaning behind and justification for a syntactic rule is - like modus ponens in a purely formal sense - but the computer does not.

[layer8]

Sometimes after a night’s sleep, we wake up with an insight on a topic or a solution to a problem we encountered the day before. Did we “think” in our sleep to come up with the insight or solution? For all we know, it’s an unconscious process. Would we call it “thinking”?

The term “thinking” is rather ill-defined, too bound to how we perceive our own wakeful thinking.

When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

I would agree that LLMs reason (well, the reasoning models). But “thinking”? I don’t know. There is something missing.

[AnIrishDuck]

> Sometimes after a night’s sleep, we wake up with an insight on a topic or a solution to a problem we encountered the day before.

The current crop of models do not "sleep" in any way. The associated limitations on long term task adaptation are obvious barriers to their general utility.

> When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

One of the key functions of REM sleep seems to be the ability to generalize concepts and make connections between "distant" ideas in latent space [1].

I would argue that the current crop of LLMs are overfit on recall ability, particularly on their training corpus. The inherent trade-off is that they are underfit on "conceptual" intelligence. The ability to make connections between these ideas.

As a result, you often get "thinking shaped objects", to paraphrase Janelle Shane [2]. It does feel like the primordial ooze of intelligence, but it is clear we still have several transformer-shaped breakthroughs before actual (human comparable) intelligence.

1. https://en.wikipedia.org/wiki/Why_We_Sleep 2. https://www.aiweirdness.com/

[popalchemist]

There is simply put no ongoing process and no feedback loop. The model does not learn. The cognition ends when the inference cycle ends. It's not thinking, it just produces output that looks similar to the output of thinking. But the process by which it does that is wholly unreleated.

[creer]

Interesting, you think the associations your brain comes up with during sleep are NOT thinking?

[madaxe_again]

Perhaps this is an artefact of instantiation - when you talk with an LLM, the responding instance is just that - it comes into being, inhales your entire chat history, and then continues like the last chap, finishes its response, and dies.

The continuity is currently an illusion.

[bithead]

Do LLMs ever ask for you to clarify something you said in a way a person who doesn't quite understand what you said will do?

[lordnacho]

> When conversing with LLMs, I never get the feeling that they have a solid grasp on the conversation. When you dig into topics, there is always a little too much vagueness, a slight but clear lack of coherence, continuity and awareness, a prevalence of cookie-cutter verbiage. It feels like a mind that isn’t fully “there” — and maybe not at all.

Much like speaking to a less experienced colleague, no?

They say things that contain the right ideas, but arrange it unconvincingly. Still useful to have though.

[petralithic]

> Would we call it “thinking”?

Yes I would.

[notepad0x90]

I don't get why you would say that. it's just auto-completing. It cannot reason. It won't solve an original problem for which it has no prior context to "complete" an approximated solution with. you can give it more context and more data,but you're just helping it complete better. it does not derive an original state machine or algorithm to solve problems for which there are no obvious solutions. it instead approximates a guess (hallucination).

Consciousness and self-awareness are a distraction.

Consider that for the exact same prompt and instructions, small variations in wording or spelling change its output significantly. If it thought and reasoned, it would know to ignore those and focus on the variables and input at hand to produce deterministic and consistent output. However, it only computes in terms of tokens, so when a token changes, the probability of what a correct response would look like changes, so it adapts.

It does not actually add 1+2 when you ask it to do so. it does not distinguish 1 from 2 as discrete units in an addition operation. but it uses descriptions of the operation to approximate a result. and even for something so simple, some phrasings and wordings might not result in 3 as a result.

[slightwinder]

> It won't solve an original problem for which it has no prior context to "complete" an approximated solution with.

Neither can humans. We also just brute force "autocompletion" with our learned knowledge and combine it to new parts, which we then add to our learned knowledge to deepen the process. We are just much, much better at this than AI, after some decades of training.

And I'm not saying that AI is fully there yet and has solved "thinking". IMHO it's more "pre-thinking" or proto-intelligence.. The picture is there, but the dots are not merging yet to form the real picture.

> It does not actually add 1+2 when you ask it to do so. it does not distinguish 1 from 2 as discrete units in an addition operation.

Neither can a toddler nor an animal. The level of ability is irrelevant for evaluating its foundation.

[ako]

An LLM by itself is not thinking, just remembering and autocompleting. But if you add a feedback loop where it can use tools, investigate external files or processes, and then autocomplete on the results, you get to see something that is (close to) thinking. I've seen claude code debug things by adding print statements in the source and reasoning on the output, and then determining next steps. This feedback loop is what sets AI tools apart, they can all use the same LLM, but the quality of the feedback loop makes the difference.

[lossyalgo]

Furthermore regarding reasoning, just ask any LLM how many "r letters are in strawberry" - repeat maybe 3 times just to get a feeling for how much variance in answers you can get. And this "quirk" of the inability to get the right answer is something that after 2 years making fun of LLMs online on various forums is still an issue. The models aren't getting smarter, and definitely aren't thinking, they are still token generators with a few tricks on top to make them seem more intelligent than predecessors.

[IanCal]

> it's just auto-completing. It cannot reason

Auto completion just means predicting the next thing in a sequence. This does not preclude reasoning.

> I don't get why you would say that.

Because I see them solve real debugging problems talking through the impact of code changes or lines all the time to find non-obvious errors with ordering and timing conditions on code they’ve never seen before.

[logicchains]

>I don't get why you would say that. it's just auto-completing. It cannot reason. It won't solve an original problem for which it has no prior context to "complete" an approximated solution with. you can give it more context and more data,but you're just helping it complete better. it does not derive an original state machine or algorithm to solve problems for which there are no obvious solutions. it instead approximates a guess (hallucination).

I bet you can't give an example such written problem that a human can easily solve but no LLM can.

[xanderlewis]

> I don't get why you would say that.

Because it's hard to imagine the sheer volume of data it's been trained on.

[madaxe_again]

The vast majority of human “thinking” is autocompletion.

Any thinking that happens with words is fundamentally no different to what LLMs do, and everything you say applies to human lexical reasoning.

One plus one equals two. Do you have a concept of one-ness, or two-ness, beyond symbolic assignment? Does a cashier possess number theory? Or are these just syntactical stochastic rules?

I think the problem here is the definition of “thinking”.

You can point to non-verbal models, like vision models - but again, these aren’t hugely different from how we parse non-lexical information.

[Kichererbsen]

Sure. But neither do you. So are you really thinking or are you just autocompleting?

When was the last time you sat down and solved an original problem for which you had no prior context to "complete" an approximated solution with? When has that ever happened in human history? All the great invention-moment stories that come to mind seem to have exactly that going on in the background: Prior context being auto-completed in an Eureka! moment.

[naasking]

> don't get why you would say that. it's just auto-completing.

https://en.wikipedia.org/wiki/Predictive_coding

> If it thought and reasoned, it would know to ignore those and focus on the variables and input at hand to produce deterministic and consistent output

You only do this because you were trained to do this, eg. to see symmetries and translations.

[jiggawatts]

You wrote your comment one word at a time, with the next word depending on the previous words written.

You did not plan the entire thing, every word, ahead of time.

LLMs do the same thing, so... how is your intelligence any different?

[simulator5g]

Having seen photocopiers so many times produce coherent, sensible, and valid chains of words on a page, I am at this point in absolutely no doubt that they are thinking.

[slightwinder]

Photocopiers are the opposite of thinking. What goes in, goes out, no transformation or creating of new data at all. Any change is just an accident, or an artifact of the technical process.

[efitz]

https://www.theregister.com/2013/08/06/xerox_copier_flaw_mea...

[Zardoz84]

I saw Dr. Abuse producing coherent, sensible and valid chains of words, running on a 386.

[throwaway-0001]

I’ve seen so many humans bring stupid. Definitively there is nothing in the brain.

You see how doesn’t make sense what you saying?

[m_rpn]

easily top 10 best HN comment ever.

[geon]

Having seen LLMs so many times produce incoherent, nonsensical and invalid chains of reasoning...

LLMs are little more than RNGs. They are the tea leaves and you read whatever you want into them.

[rcxdude]

They are clearly getting to useful and meaningful results with at a rate significantly better than chance (for example, the fact that ChatGPT can play chess well even though it sometimes tries to make illegal moves shows that there is a lot more happening there than just picking moves uniformly at random). Demanding perfection here seems to be odd given that humans also can make bizarre errors in reasoning (of course, generally at a lower rate and in a distribution of kinds of errors we are more used to dealing with).

[bongodongobob]

Ridiculous. I use it daily and get meaningful, quality results. Learn to use the tools.

[burnte]

The first principle is that you must not fool yourself, and you are the easiest person to fool. - Richard P. Feynman

They're not thinking, we're just really good at seeing patterns and reading into things. Remember, we never evolved with non-living things that could "talk", we're not psychologically prepared for this level of mimicry yet. We're still at the stage of Photography when people didn't know about double exposures or forced perspective, etc.

[naasking]

You're just assuming that mimicry of a thing is not equivalent to the thing itself. This isn't true of physical systems (simulated water doesn't get you wet!) but it is true of information systems (simulated intelligence is intelligence!).

[luxuryballs]

yeah it’s just processing, calling it thinking is the same as saying my intel core 2 duo or M4 Pro is thinking, sure if you want to anthropomorphize it you could say it’s thinking, but why are we trying to say a computer is a person in the first place? seems kind of forced

[marcus_holmes]

Yes, I've seen the same things.

But; they don't learn. You can add stuff to their context, but they never get better at doing things, don't really understand feedback. An LLM given a task a thousand times will produce similar results a thousand times; it won't get better at it, or even quicker at it.

And you can't ask them to explain their thinking. If they are thinking, and I agree they might, they don't have any awareness of that process (like we do).

I think if we crack both of those then we'd be a lot closer to something I can recognise as actually thinking.

[theptip]

> But; they don't learn

If we took your brain and perfectly digitized it on read-only hardware, would you expect to still “think”?

Do amnesiacs who are incapable of laying down long-term memories not think?

I personally believe that memory formation and learning are one of the biggest cruces for general intelligence, but I can easily imagine thinking occurring without memory. (Yes, this is potentially ethically very worrying.)

[trenchpilgrim]

> You can add stuff to their context, but they never get better at doing things, don't really understand feedback.

I was using Claude Code today and it was absolutely capable of taking feedback to change behavior?

[jatora]

This is just wrong though. They absolutely learn in-context in a single conversation within context limits. And they absolutely can explain their thinking; companies just block them from doing it.

[ben_w]

> 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.

While I'm not willing to rule *out* the idea that they're "thinking" (nor "conscious" etc.), the obvious counter-argument here is all the records we have of humans doing thinking, where the records themselves are not doing the thinking that went into creating those records.

And I'm saying this as someone whose cached response to "it's just matrix multiplication it can't think/be conscious/be intelligent" is that, so far as we can measure all of reality, everything in the universe including ourselves can be expressed as matrix multiplication.

Falsification, not verification. What would be measurably different if the null hypothesis was wrong?

[chpatrick]

I've definitely had AIs thinking and producing good answers about specific things that have definitely not been asked before on the internet. I think the stochastic parrot argument is well and truly dead by now.

[satisfice]

I think you are the one dismissing evidence. The valid chains of reasoning you speak of (assuming you are talking about text you see in a “thinking model” as it is preparing its answer) are narratives, not the actual reasoning that leads to the answer you get.

I don’t know what LLMs are doing, but only a little experimentation with getting it to describe its own process shows that it CAN’T describe its own process.

You can call what a TI calculator does “thinking” if you want. But what people are interested in is human-like thinking. We have no reason to believe that the “thinking” of LLMs is human-like.

[naasking]

> The valid chains of reasoning you speak of (assuming you are talking about text you see in a “thinking model” as it is preparing its answer) are narratives, not the actual reasoning that leads to the answer you get.

It's funny that you think people don't also do that. We even have a term, "post hoc rationalization", and theories of mind suggest that our conscious control is a complete illusion, we just construct stories for decisions our subconscious has already made.

[josefx]

Counterpoint: The seahorse emoji. The output repeats the same simple pattern of giving a bad result and correcting it with another bad result until it runs out of attempts. There is no reasoning, no diagnosis, just the same error over and over again within a single session.

[becquerel]

A system having terminal failure modes doesn't inherently negate the rest of the system. Human intelligences fall prey to plenty of similarly bad behaviours like addiction.

[throwaway-0001]

You never had that coleague that says yes to everything and can’t get anything done? Same thing as seahorse.

[techblueberry]

Isn’t anthropomorphizing LLMs rather than understanding their unique presence in the world a “ lack of imagination and flexibility of thought”? It’s not that I can’t imagine applying the concept “thinking” to the output on the screen, I just don’t think it’s an accurate description.

[heresie-dabord]

Yes, it's an example of domain-specific thinking. "The tool helps me write code, and my job is hard so I believe this tool is a genius!"

The Roomba vacuumed the room. Maybe it vacuumed the whole apartment. This is good and useful. Let us not diminish the value of the tool. But it's a tool.

The tool may have other features, such as being self-documenting/self-announcing. Maybe it will frighten the cats less. This is also good and useful. But it's a tool.

Humans are credulous. A tool is not a human. Meaningful thinking and ideation is not just "a series of steps" that I will declaim as I go merrily thinking. There is not just a vast training set ("Reality"), but also our complex adaptability that enables us to test our hypotheses.

We should consider what it is in human ideation that leads people to claim that a Roomba, a chess programme, Weizenbaum's Eliza script, the IBM's Jeopardy system Watson, or an LLM trained on human-vetted data is thinking.

Train such a system on the erroneous statements of a madman and suddenly the Roomba, Eliza, IBM Watson (and these other systems) lose our confidence.

As it is today, the confidence we have in these systems is very conditional. It doesn't matter terribly if code is wrong... until it does.

Computers are not humans. Computers can do things that humans cannot do. Computers can do these things fast and consistently. But fundamentally, algorithms are tools.

[didibus]

I guess it depends if you definite thinking thinking as chaining coherent reasoning sentences together 90-some% of the time.

But if you define thinking as the mechanism and process we mentally undergo and follow mentally... I don't think we have any clue if that's the same. Do we also just vector-map attention tokens and predict the next with a softmax? I doubt, and I don't think we have any proof that we do.

[aydyn]

We do know at the biochemical level how neurons work, and it isnt anything like huge matmuls.

[ph4rsikal]

It might appear so, but then you could validate it with a simple test. If the LLM would play a 4x4 Tic Tac Toe game, would the agent select the winning move 100% of all time or block a losing move 100% of the time? If these systems were capable of proper reasoning, then they would find the right choice in these obvious but constantly changing scenarios without being specifically trained for it.

[1] https://jdsemrau.substack.com/p/nemotron-vs-qwen-game-theory...

[johnnienaked]

If you understand how they operate and you are reasonable and unbiased there is no way you could consider it thinking

[noiv]

Different PoV: You have a local bug and ask the digital hive mind for a solution, but someone already solved the issue and their solution was incorporated... LLMs are just very effficient at compressing billions of solutions into a few GB.

Try to ask something no one ever came up with a solution so far.

[brabel]

This argument comes up often but can be easily dismissed. Make up a language and explain it to the LLM like you would to a person. Tell it to only use that language now to communicate. Even earlier AI was really good at this. You will probably move the goal posts and say that this is just pattern recognition, but it still fits nicely within your request for something that no one ever came up with.

[conartist6]

Yeah but if I assign it a long job to process I would also say that an x86 CPU is "thinking" about a problem for me.

What we really mean in both cases is "computing," no?

[ForHackernews]

But all those times the same system produces irrational gibberish don't count? GPT-5 will commonly make mistakes no thinking human could ever make.

Human: I'm trying to get my wolf, sheep and cabbage across the river in this boat, but the wolf keeps eating the sheep or the sheep eats the cabbage

Bot: You should put the sheep in the boat and take it across — if we delve into the biology of Canis lupus we discover that wolves don't eat cabbage!

H: Ok, so that worked great so far, the sheep is on one side and the wolf/cabbage is on the other.

B: Now, Option 1 is to bring the wolf across, or Option 2 you can bring the cabbage. I recommend (2) taking the cabbage as cabbages are smaller and easier to transport in a boat.

H: But then the sheep eats the cabbage, right? Remember that?

B: Exactly, that's sharp thinking. If you put the sheep and the cabbage together on the same side of the river, the sheep is sure to devour the cabbage. We need to not just separate sheep from cabbages — we need to separate cabbages from sheep! :rocketship:

[intended]

what sound does a falling tree make if no one is listening?

I’ve asked LLMs to write code for me in fields I have little background knowledge, and then had to debug the whole thing after essentially having to learn the language and field.

On the other hand, for things I am well versed in, I can debug the output and avoid entire swathes of failed states, by having a clear prompt.

Its why I now insist that any discussion on GenAI projects also have the speaker mention the level of seniority they have ( proxy for S/W eng experience), Their familiarity with the language, the project itself (level of complexity) - more so than the output.

I also guarantee - that most people have VERY weak express knowledge of how their brains actually work, but deep inherent reflexes and intuitions.

[raincole]

I'd represent the same idea but in a different way:

I don't know what the exact definition of "thinking" is. But if a definition of thinking rejects the possibility of that current LLMs think, I'd consider that definition useless.

[didibus]

Why would it be useless?

Generally thinking has been used to describe the process human follow in their brains when problem solving.

If the Palms do not follow that process, they are not thinking.

That doesn't mean they cannot solve problems using other mechanisms, they do, and we understand those mechanisms much better than we do human thinking.

[NoMoreNicksLeft]

>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.

If one could write a quadrillion-line python script of nothing but if/elif/else statements nested 1 million blocks deep that seemingly parsed your questions and produced seemingly coherent, sensible, valid "chains of reasoning"... would that software be thinking?

And if you don't like the answer, how is the LLM fundamentally different from the software I describe?

>Knee jerk dismissing the evidence in front of your eyes because

There is no evidence here. On the very remote possibility that LLMs are at some level doing what humans are doing, I would then feel really pathetic that humans are as non-sapient as the LLMs. The same way that there is a hole in your vision because of a defective retina, there is a hole in your cognition that blinds you to how cognition works. Because of this, you and all the other humans are stumbling around in the dark, trying to invent intelligence by accident, rather than just introspecting and writing it out from scratch. While our species might someday eventually brute force AGI, it would be many thousands of years before we get there.

[hattmall]

I write software that is far less complex and I consider it to be "thinking" while it is working through multiple possible permutations of output and selecting the best one. Unless you rigidly define thinking, processing, computing, it's reasonable to use them interchangeably.

[helloplanets]

10^15 lines of code is a lot. We would pretty quickly enter the realm of it not having much to do with programming and more about just treating the LOC count as an amount of memory allocated to do X.

How much resemblance does the information in the conditionals need to have with the actual input, or can they immediately be transformed to a completely separate 'language' which simply uses the string object as its conduit? Can the 10^15 lines of code be generated with an external algorithm, or is it assumed that I'd written it by hand given an infinitely long lifespan?

[dmz73]

Having seen LLMs so many time produce incoherent, nonsense, invalid answers to even simplest of questions I cannot agree with categorization of "thinking" or "intelligence" that applies to these models. LLMs do not understand what they "know" or what they output. All they "know" is that based on training data this is most likely what they should output + some intentional randomization to make it seem more "human like". This also makes it seem like they create new and previously unseen outputs but that could be achieved with simple dictionary and random number generator and no-one would call that thinking or intelligent as it is obvious that it isn't. LLMs are better at obfuscating this fact by producing more sensible output than just random words. LLMs can still be useful but they are a dead-end as far as "true" AI goes. They can and will get better but they will never be intelligent or think in the sense that most humans would agree those terms apply. Some other form of hardware/software combination might get closer to AI or even achieve full AI and even sentience but that will not happen with LLMs and current hardware and software.

[ryanackley]

I think we can call it "thinking" but it's dangerous to anthropomorphize LLMs. The media and AI companies have an agenda when doing so.

[outworlder]

They may not be "thinking" in the way you and I think, and instead just finding the correct output from a really incredibly large search space.

> Knee jerk dismissing the evidence in front of your eyes

Anthropomorphizing isn't any better.

That also dismisses the negative evidence, where they output completely _stupid_ things and make mind boggling mistakes that no human with a functioning brain would do. It's clear that there's some "thinking" analog, but there are pieces missing.

I like to say that LLMs are like if we took the part of our brain responsible for language and told it to solve complex problems, without all the other brain parts, no neocortex, etc. Maybe it can do that, but it's just as likely that it is going to produce a bunch of nonsense. And it won't be able to tell those apart without the other brain areas to cross check.

[jimbohn]

It's reinforcement learning applied to text, at a huge scale. So I'd still say that they are not thinking, but they are still useful. The question of the century IMO is if RL can magically solve all our issues when scaled enough.

[IAmGraydon]

>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.

You go ahead with your imagination. To us unimaginative folks, it betrays a lack of understanding of how LLMs actually work and shows that a lot of people still cannot grasp that it’s actually an extremely elaborate illusion of thinking.

[flanked-evergl]

"Convince" the stock Claude Sonnet 4.5 that it's a sentient human being hooked up to Neuralink and then tell me again it's thinking. It's just not.

[khafra]

"Consciousness" as in subjective experience, whatever it is we mean by "the hard problem," is very much in doubt.

But "self-awareness," as in the ability to explicitly describe implicit, inner cognitive processes? That has some very strong evidence for it: https://www.anthropic.com/research/introspection

[hyperbovine]

Code gen is the absolute best case scenario for LLMs though: highly structured language, loads of training data, the ability to automatically error check the responses, etc. If they could mimic reasoning anywhere it would be on this problem.

I'm still not convinced they're thinking though because they faceplant on all sorts of other things that should be easy for something that is able to think.

[camgunz]

Then the only thing I have to ask you is: what do you think this means in terms of how we treat LLMs? If they think, that is, they have cognition (which of course means they're self aware and sentient, how can you think and refer to yourself and not be these things), that puts them in a very exclusive club. What rights do you think we should be affording LLMs?

[belter]

Apparent reasoning can emerge from probabilistic systems that simply reproduce statistical order not genuine understanding.

Weather models sometimes “predict” a real pattern by chance, yet we don’t call the atmosphere intelligent.

If LLMs were truly thinking, we could enroll one at MIT and expect it to graduate, not just autocomplete its way through the syllabus or we could teach one how to drive.

[triyambakam]

> 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.

People said the same thing about ELIZA

> Consciousness or self awareness is of course a different question,

Then how do you define thinking if not a process that requires consciousness?

[lordnacho]

Why would it require consciousness, when we can't even settle on a definition for that?

[fennecbutt]

Thinking as in capable of using basic reasoning and forming chains of logic and action sequences for sure. Ofc we both understand that neither of us are trying to say we think it can think in the human sense at this point in time.

But oh boy have I also seen models come up with stupendously dumb and funny shit as well.

[mlsu]

They remind me of the apparitions in Solaris. They have this like mechanical, almost player-piano like quality to them. They both connect with and echo us at the same time. It seems crazy to me and very intellectually uncreative to not think of this as intelligence.

[hitarpetar]

> The world is full of bizarre wonders and this is just one more to add to the list.

https://youtu.be/_-agl0pOQfs?si=Xiyf0InqtjND9BnF

[darthvaden]

If AI is thinking if slavery is bad then how can somebody own AI. How can investors can shares from AI profits? We are ok with slavery now. Ok i will have two black slaves now. Who can ask me? Why shld that be illegal?

[Manfred]

Yikes, you're bypassing thousands of years of oppression, abuse, and human suffering by casually equating a term that is primarily associated with a human owning another human to a different context.

There is a way to discuss if keeping intelligent artificial life under servitude without using those terms, especially if you're on a new account.

[ndsipa_pomu]

I presume you are aware that the word "robot" is taken from a Czech word (robota) meaning "slave"

[tengbretson]

Too many people place their identity in their own thoughts/intellect. Acknowledging what the LLMs are doing as thought would basically be calling them human to people of that perspective.

[lmganon]

Is this model thinking too?

https://huggingface.co/PantheonUnbound/Satyr-V0.1-4B

[lordnacho]

I agree with you.

If you took a Claude session into a time machine to 2019 and called it "rent a programmer buddy," how many people would assume it was a human? The only hint that it wasn't a human programmer would be things where it was clearly better: it types things very fast, and seems to know every language.

You can set expectations in the way you would with a real programmer: "I have this script, it runs like this, please fix it so it does so and so". You can do this without being very precise in your explanation (though it helps) and you can make typos, yet it will still work. You can see it literally doing what you would do yourself: running the program, reading the errors, editing the program, and repeating.

People need to keep in mind two things when they compare LLMs to humans: you don't know the internal process of a human either, he is also just telling you that he ran the program, read the errors, and edited. The other thing is the bar for thinking: a four-year old kid who is incapable of any of these things you would not deny as a thinking person.

[kkapelon]

> If you took a Claude session into a time machine to 2019 and called it "rent a programmer buddy," how many people would assume it was a human?

Depends on the users. Junior devs might be fooled. Senior devs would quickly understand that something is wrong.

[uberduper]

Sometimes I start thinking our brains work the same way as an LLM does when it comes to language processing. Are we just using probability based on what we already know and the context of the statement we're making to select the next few words? Maybe we apply a few more rules than an LLM on what comes next as we go.

We train ourselves on content. We give more weight to some content than others. While listening to someone speak, we can often predict their next words.

What is thinking without language? Without language are we just bags of meat reacting to instincts and emotions? Are instincts and emotions what's missing for AGI?

[Zardoz84]

Having seen parrots so many times produce coherent, sensible, and valid chains of sounds and words, I am at this point in absolutely no doubt that they are thinking.

[_puk]

You think parrots don't think?

[lispybanana]

Would they have diagnosed an issue if you hadn't presented it to them?

Life solves problems itself poses or collides with. Tools solve problems only when applied.

[xhkkffbf]

Instead of thinking, "Wow. AIs are smart like humans", maybe we should say, "Humans are dumb like matrix multiplication?"

[yawpitch]

You’re assuming the issues and bugs you’ve been addressing don’t already exist, already encoding human chain of reasoning, in the training data.

[libraryatnight]

If you're sensitive to patterns and have been chronically online for the last few decades it's obvious they are not thinking.

[donkeybeer]

Its overt or unaware religion. The point when you come down to the base of it is that these people believe in "souls".

[hagbarth]

I'm not so sure. I, for one, do not think purely by talking to myself. I do that sometimes, but a lot of the time when I am working through something, I have many more dimensions to my thought than inner speech.

[conartist6]

So an x86 CPU is thinking?

So many times I've seen it produce sensible, valid chains of results.

Yes, I see evidence in that outcome that a person somewhere thought and understood. I even sometimes say that a computer is "thinking hard" about something when it freezes up.

...but ascribing new philosophical meaning to this simple usage of the word "thinking" is a step too far. It's not even a new way of using the word!

[gchamonlive]

You can't say for sure it is or it isn't thinking based solely on the substrate, because it's not known for sure if consciousness is dependent on the hardware it's running on -- for a lack of a better analogy -- to manifest, if it really needs an organic brain or if it could manifest in silicon based solutions.