ML promises to be profoundly weird

https://aphyr.com/posts/411-the-future-of-everything-is-lies-i-guess

by pabs3 • 4 months ago

615 599 4 months ago

bensyverson3• 4 months ago

I get the frustration, but it's reductive to just call LLMs "bullshit machines" as if the models are not improving. The current flagship models are not perfect, but if you use GPT-2 for a few minutes, it's incredible how much the industry has progressed in seven years.

It's true that people don't have a good intuitive sense of what the models are good or bad at (see: counting the Rs in "strawberry"), but this is more a human limitation than a fundamental problem with the technology.

ajross5• 4 months ago

it's reductive to just call LLMs "bullshit machines" as if the models are not improving

This is true, but I prefer to think of it as "It's delusional to pretend as if human beings are not bullshit machines too".

Lies are all we have. Our internal monologue is almost 100% fantasy. Even in serious pursuits, that's how it works. We make shit up and lie to ourselves, and then only later apply our hard-earned[1] skill prompts to figure out whether or not we're right about it.

How many times have the nerds here been thinking through a great new idea for a design and how clever it would be before stopping to realize "Oh wait, that won't work because of XXX, which I forgot". That's a hallucination right there!

[1] Decades of education!

iamjackg• 4 months ago

The problem, unfortunately, is the scale. It's always scale. Humans make all the kinds of mistakes that we ascribe to LLMs, but LLMs can make them much faster and at much larger scale.

Models have gotten ridiculously better, they really have, but the scale has increased too, and I don't think we're ready to deal with the onslaught.

kolektiv• 4 months ago

I'm not entirely sure I can agree, although the premise is seductive in certain ways. We do lie to ourselves, but we also have meta-cognition - we can recognise our own processes of thought. Imperfect as it may be, we have feedback loops which we can choose to use, we have heuristics we can apply, we can consciously alter our behaviour in the presence of contextual inputs, and so on.

Being wrong is not the same as a hallucination. It's a natural step on a journey to being more right. This feels a bit like Andreesen proudly stating he avoids reflection - you can act like that, but the human brain doesn't have to. LLMs have no choice in the matter.

nyeah3• 4 months ago

"Lies are all we have."

If so, how do we distinguish between code that works and code that doesn't work? Why should we even care?

AnimalMuppet2• 4 months ago

Humans are different. Humans - at least thoughtful humans - know the difference between knowing something and not knowing something. Humans are capable of saying "I don't know" - not just as a stream of tokens, but really understanding what that means.

nothinkjustai3• 4 months ago

So your logic is humans and LLMs are the same because humans are wrong sometimes?

Arainach• 4 months ago

Whether LLMs can create correct content doesn't matter. We've already seen how they are being used and will be used.

Fake content and lies. To drive outrage. To influence elections. To distract from real crimes. To overload everyone so they're too tired to fight or to understand. To weaken the concept that anything's true so that you can say anything. Because who cares if the world dies as long as you made lots of money on the way.

danny_codes2• 4 months ago

Because who cares if the world dies as long as you made lots of money on the way.

Guiding principle of the AI industry

zdragnar• 4 months ago

That's not why the author calls them bullshit machines.

One way to understand an LLM is as an improv machine. It takes a stream of tokens, like a conversation, and says “yes, and then…” This yes-and behavior is why some people call LLMs bullshit machines. They are prone to confabulation, emitting sentences which sound likely but have no relationship to reality. They treat sarcasm and fantasy credulously, misunderstand context clues, and tell people to put glue on pizza.

Yes, there have been improvements on them, but none of those improvements mitigate the core flaw of the technology. The author even acknowledges all of the improvements in the last few months.

the_snooze4• 4 months ago

Two things can be true at the same time: The technology has improved, and the technology in its current state still isn't fit for purpose.

I stress test commercially deployed LLMs like Gemini and Claude with trivial tasks: sports trivia, fixing recipes, explaining board game rules, etc. It works well like 95% of the time. That's fine for inconsequential things. But you'd have to be deeply irresponsible to accept that kind of error rate on things that actually matter.

The most intellectually honest way to evaluate these things is how they behave now on real tasks. Not with some unfalsifiable appeal to the future of "oh, they'll fix it."

hedgehog3• 4 months ago

The errors are also not distributed in the same way as you'd expect from a human. The tools can synthesize a whole feature in a moderately complicated web app including UI code, schema changes, etc, and it comes out perfectly. Then I ask for something simple like a shopping list of windshield wipers etc for the cars and that comes out wildly wrong (like wrong number of wipers for the cars, not just the wrong parts), stuff that a ten year old child would have no trouble with. I work in the field so I have a qualitative understanding of this behavior but I think it can be extremely confusing to many people.

jerf• 4 months ago

One of the reasons I'm comfortable using them as coding agents is that I can and do review every line of code they generate, and those lines of code form a gate. No LLM-bullshit can get through that gate, except in the form of lines of code, that I can examine, and even if I do let some bullshit through accidentally, the bullshit is stateless and can be extracted later if necessary just like any other line of code. Or, to put it another way, the context window doesn't come with the code, forming this huge blob of context to be carried along... the code is just the code.

That exposes me to when the models are objectively wrong and helps keep me grounded with their utility in spaces I can check them less well. One of the most important things you can put in your prompt is a request for sources, followed by you actually checking them out.

And one of the things the coding agents teach me is that you need to keep the AIs on a tight leash. What is their equivalent in other domains of them "fixing" the test to pass instead of fixing the code to pass the test? In the programming space I can run "git diff *_test.go" to ensure they didn't hack the tests when I didn't expect it. It keeps me wondering what the equivalent of that is in my non-programming questions. I have unit testing suites to verify my LLM output against. What's the equivalent in other domains? Probably some other isolated domains here and there do have some equivalents. But in general there isn't one. Things like "completely forged graphs" are completely expected but it's hard to catch this when you lack the tools or the understanding to chase down "where did this graph actually come from?".

The success with programming can't be translated naively into domains that lack the tooling programmers built up over the years, and based on how many times the AIs bang into the guardrails the tools provide I would definitely suggest large amounts of skepticism in those domains that lack those guardrails.

bensyverson3• 4 months ago

the technology in its current state still isn't fit for purpose.

This is a broad statement that assumes we agree on the purpose.

For my purpose, which is software development, the technology has reached a level that is entirely adequate.

Meanwhile, sports trivia represents a stress test of the model's memorized world knowledge. It could work really well if you give the model a tool to look up factual information in a structured database. But this is exactly what I meant above; using the technology in a suboptimal way is a human problem, not a model problem.

floren• 4 months ago

Six months bro, we're still so early

simianwords41• 4 months ago

I stress test commercially deployed LLMs like Gemini and Claude with trivial tasks: sports trivia, fixing recipes, explaining board game rules, etc. It works well like 95% of the time. That's fine for inconsequential things. But you'd have to be deeply irresponsible to accept that kind of error rate on things that actually matter.

95% is not my experience and frankly dishonest.

I have ChatGPT open right now, can you give me examples where it doesn't work but some other source may have got it correct?

I have tested it against a lot of examples - it barely gets anything wrong with a text prompt that fits a few pages.

The most intellectually honest way to evaluate these things is how they behave now on real tasks

A falsifiable way is to see how it is used in real life. There are loads of serious enterprise projects that are mostly done by LLMs. Almost all companies use AI. Either they are irresponsible or you are exaggerating.

Lets be actually intellectually honest here.

nradov2• 4 months ago

Which things actually matter? I think we can all agree that an LLM isn't fit for purpose to control a nuclear power plant or fly a commercial airliner. But there's a huge spectrum of things below that. If an LLM trading error causes some hedge fund to fail then so what? It's only money.

Scaevolus• 4 months ago

They are bullshit machines because they do not have an internal mental model of truth like a human does. The flagship models bullshit less, but their fundamental architectures prevent having truth interfere with output.

https://philosophersmag.com/large-language-models-and-the-co...

bensyverson3• 4 months ago

"Bullshit" is a human concept. LLMs do not work like the human brain, so to call their output "bullshit" is ascribing malice and intent that is simply not there. LLMs do not "think." But that does not mean they're not incredibly powerful and helpful in the right context.

gdulli3• 4 months ago

Computer graphics have been improving for decades but the uncanny valley remains undefeated. I don't know why anyone expects a breakthrough in other areas. There's a wall we hit and we don't understand our own consciousness and effectiveness well enough to replicate it.

kritiko• 4 months ago

We have credible deepfakes on demand. (To be fair, there have been deceptive photos as long as photos have existed, but the cost of automating their creation going to basically zero has a social impact)

PaulKeeble• 4 months ago

In computer graphics we understand how it works, we just lack the computational power to do it real time, but we can with sufficient processing produce realistic looking images with physically accurate lighting. But when it comes to cognition its a lot of guesswork, we haven't yet mapped out the neuron connections in a brain, we haven't validated it works as popular science writing suggests. We don't understand intelligence, so all we can do is accidentally bumble into it and it seems unlikely that will just happen especially when its so hard to compute what we are already doing.

4ndrewl• 4 months ago

It doesn't matter how good the models become. They can only deal in bullshit, in the academic use of the term.

mcpar-land• 4 months ago

it's not a bullshit machine because its output is bad, it's a bullshit machine because its output is literally 'bullshit' as in, output that is statistically likely but with no factual or reasoning basis. as the models have improved, their bullshit is more statistically likely to sound coherent (maybe even more likely to be 'accurate'), but no more factual and with no more reasoning.

abraxas2• 4 months ago

However, when fed source material into the context they will lie less, right? So at this point is it not just a battle of the nines until it's called "good enough"?

I also wonder if I leave my secretary with a ream of papers and ask him for a summary how many will he actually read and understand vs skim and then bullshit? It seems like the capacity for frailty exists in both "species".

karmakaze• 4 months ago

Bullshit is the perfect term here, even as AI's get so much better and capable Brandolini's Law aka the "bullshit asymmetry principle" always applies--the energy required to refute misinformation is an order of magnitude larger than that needed to produce it. Even to use AIs effectively today requires a very good BS detector--some day in the future it won't.

ura_yukimitsu• 4 months ago

Calling LLMs "bullshit machines" is a reference to a 2024 paper [1] which itself uses the concept of "bullshit" as defined in the essay/book "On Bullshit" by Harry G. Frankfurt [2]. The TL;DR is that LLMs are fundamentally bullshit machines because they are only made to generate sentences that sound plausible, but plausible does not always mean true.

[1] https://link.springer.com/article/10.1007/s10676-024-09775-5

[2] https://en.wikipedia.org/wiki/On_Bullshit

p_stuart82• 4 months ago

models are improving. the pricing already assumes they're ready for prod. that's where the fires start

bstsb• 4 months ago

if you can’t access the page through region blocks:

https://archive.ph/I5cAE

josefritzishere• 4 months ago

I appreciate the directness of calling LLMs "Bullshit machines." This terminology for LLMs is well established in academic circles and is much easier for laypeople to understand than terms like "non-deterministic." I personally don't like the excessive hype on the capabilities of AI. Setting realistic expectations will better drive better product adoption than carpet bombing users with marketing.

AStrangeMorrow• 4 months ago

I have still mixed feelings about LLMs.

If I take the example of code, but that extends to many domains, it can sometimes produce near perfect architecture and implementation if I give it enough details about the technical details and fallpits. Turning a 8h coding job into a 1h review work.

On the other hand, it can be very wrong while acting certain it is right. Just yesterday Claude tried gaslighting me into accepting that the bug I was seeing was coming from a piece of code with already strong guardrails, and it was adamant that the part I was suspecting could in no way cause the issue. Turns out I was right, but I was starting to doubt myself

slopinthebag5• 4 months ago

I think over time we will find better usage patterns for these machines. Even putting a model in a position to gaslight the user seems like a complete failure in the usage model. Not critiquing you at all on this, it's how these models are marketed and what all the tooling is built around. But they are incredibly useful and I think once we figure out how to use them better we can minimise these downsides and make ourselves much more productive without all the failures.

Of course that won't happen until the bubble pops - companies are racing to make themselves indispensable and to completely corner certain markets and to do so they need autonomous agents to replace people.

simianwords41• 4 months ago

If it bullshits so much, you wouldn't have a problem giving me an example of it bullshitting on ChatGPT (paid version)? Lets take any example of a text prompt fitting a few pages - it may be a question in science or math or any domain. Can you get it to bullshit?

beders2• 4 months ago

I think you highlight one of the problems with users of LLMs: You can't tell anymore if it is BS or not.

I caught Claude the other day hallucinating code that was not only wrong, but dangerously wrong, leading to tasks being failed and never recover. But it certainly wasn't obvious.

simoncion11• 4 months ago

If it bullshits so much, you wouldn't have a problem giving me an example of it bullshitting on ChatGPT (paid version)?

There's an entire paragraph in the essay about apyhr's direct experience with ChatGPT failures and sustained bullshitting that we'd never expect from a moderately-skilled human who possesses at least two functioning braincells. That paragraph begins "I have recently argued for forty-five minutes with ChatGPT". Do notice that there are six sentences in the paragraph. I encourage you to read all of them (make sure to check out the footnote... it's pretty good).

The exact text of the ChatGPT session is irrelevant; even if you reported that you were unable to reproduce the issue, it would only reinforce one of the underlying points -namely- that these systems are unreliable. aphyr has a pretty extensive body of published work that indicates that he'd not likely fabricate a story of an LLM repeatedly failing to accomplish a task that any moderately-skilled human could accomplish when equipped with the proper tools. So, I believe that his report is true and accurate.

dgb234• 4 months ago

To me it’s the other way around. It’s difficult to trust (paid) ChatGPT‘s output consistently.

When I need exact, especially up to date facts, I have to constantly double check everything.

I split my sessions into projects by topic, it regularly mixes things up in subtle and not so subtle ways. There is no sense of actually understanding continuity and especially not causality it seems.

It’s _very_ easy to lead it astray and to confidently echo false assumptions.

In any case, I‘ve become more precise at prompting and good at spotting when it fails. I think the trick is to not take its output too seriously.

pocksuppet3• 4 months ago

https://discuss.systems/@palvaro/116286268110078647

Arguing with Gemini Home Assistant about whether or not it can turn off the lights. When the user gets frustrated and tells the LLM to kill itself, the LLM turns off the lights.

katatue• 4 months ago

I like to let new models write a few lines of Latin poetry - they rarely get the meter right.

I don't have access to paid ChatGPT right now, but here's Opus 4.6 with extra thinking enabled: https://claude.ai/share/6e0e8ef5-06e4-4514-ba7e-299357c1fc55

The initial draft fucks up the meter in lines 3 and 8, the final version gets line 2 wrong ("venit meis") and is somewhat obnoxious with verses 2 and 8 basically repeating each other. The thinking trace is useless and gives us no clue why the model exchanged a bland, but metrically correct first distich for a more interesting, but metrically incorrect one.

In fact, the "careful" examination of its own output completely skips the erroneously modified half-verse in line 2 - now, tell me that's a coincidence and not a sign of bullshitting.

ambicapter2• 4 months ago

The recent article of Sam Altman described pretty much as a compulsive liar. Would it be any surprise if his most impactful contribution to the world was a machine that compulsively lies?

sph• 4 months ago

He sought to create God in his image, that's a narcissist's wet dream.

embedding-shape4• 4 months ago

How could it be that we humans hardly even agree on what "knowledge" truly is, yet somehow this machine learning algorithm somehow "compulsively lies"? How would it even know what is a lie, and how could something lacking autonomy in the first place do anything compulsively?

quantummagic• 4 months ago

This is a good point. As much as there is too much breathless enthusiasm for AI, there is also a lot of emotionally manipulative and hyperbolic language used by skeptics. We're warned not to anthropomorphize, and then hear about AI's compulsive lying, or "hallucinations", in the next.

Kuyawa• 4 months ago

And the past too, if we've been paying attention

dwallin• 4 months ago

Some people point at LLMs confabulating, as if this wasn’t something humans are already widely known for doing.

I consider it highly plausible that confabulation is inherent to scaling intelligence. In order to run computation on data that due to dimensionality is computationally infeasible, you will most likely need to create a lower dimensional representation and do the computation on that. Collapsing the dimensionality is going to be lossy, which means it will have gaps between what it thinks is the reality and what is.

FloorEgg2• 4 months ago

Yes, and to me the evolution of life sure looks like an evolution of more truthful models of the universe in service of energy profit. Better model -> better predictions -> better profit.

I'm extremely skeptical that all of life evolved intelligence to be closer to truth only for us to digitize intelligence and then have the opposite happen. Makes no sense.

telephone3• 4 months ago

My understanding is that this is the opposite of what is typically understood to be true - organisms with less truthful (more reductive/compressed) perception survive better than those with more complete perception. "Fitness beats truth."

zeroonetwothree• 4 months ago

And is that considered a feature of humans or a bug?

Is it something we want to emulate?

margalabargala• 4 months ago

The suggestion is that it is an intrinsic quality and therefore neither a feature nor a bug.

It's like saying, computation requires nonzero energy. Is that a feature or a bug? Neither, it's irrelevant, because it's a physical constant of the universe that computation will always require nonzero energy.

If confabulation is a physical constant of intelligence, then like energy per computation, all we can do is try to minimize it, while knowing it can never go to zero.

AIorNot• 4 months ago

Yes see Karl Frisstons Free energy principle

https://www.nature.com/articles/nrn2787

n4r92• 4 months ago

The concern for me about LLMs confabulating is not that humans don't do it. It's that the massive scale at which LLMs will inevitably be deployed makes even the smallest confabulation extremely risky.

NiloCK2• 4 months ago

I don't understand this. Many small errors distributed across a large deployment sounds a lot like normal mode of error prone humans / cogs / whatevers distributed over a wide deployment.

Frieren• 4 months ago

Some people point at LLMs confabulating

No. LLMs do not confabulate they bullshit. There is a big difference. AIs do not care, cannot care, have not capacity to care about the output. String tokens in, string tokes out. Even if they have all the data perfectly recorded they will still fail to use it for a coherent output.

Collapsing the dimensionality is going to be lossy, which means it will have gaps between what it thinks is the reality and what is.

Confabulation has to do with degradation of biological processes and information storage.

There is no equivalent in a LLM. Once the data is recorded it will be recalled exactly the same up to the bit. A LLM representation is immutable. You can download a model a 1000 times, run it for 10 years, etc. and the data is the same. The closes that you get is if you store the data in a faulty disk, but that is not why LLMs output is so awful, that would be a trivial problem to solve with current technology. (Like having a RAID and a few checksums).

knowaveragejoe• 4 months ago

No. LLMs do not confabulate they bullshit. There is a big difference. AIs do not care, cannot care, have not capacity to care about the output. String tokens in, string tokes out. Even if they have all the data perfectly recorded they will still fail to use it for a coherent output.

Isn't "caring" a necessary pre-requisite for bullshitting? One either bullshits because they care, or don't care, about the context.

stronglikedan• 4 months ago

I don't even think they bullshit, since that requires conscious effort that they do not an cannot possess. They just simply interpret things incorrectly sometimes, like any of us meatbags.

nothinkjustai3• 4 months ago

It’s a failure mode of humans, it’s the entire mode of LLMs.

delusional3• 4 months ago

Some people point at LLMs confabulating, as if this wasn’t something humans are already widely known for doing.

Are you seriously making the argument that AI "hallucinations" are comparable and interchangeable to mistakes, omissions and lies made by humans?

You understand that calling AI errors "hallucinations" and "confabulations" is a metaphor to relate them to human language? The technical term would be "mis-prediction", which suddenly isn't something humans ever do when talking, because we don't predict words, we communicate with intent.

sillyfluke2• 4 months ago

If you want to call it that, I find the confabulation in LLMs extreme. That level of confabulation would most likely be diagnosed as dementia in humans.[0] Hence, it is considered a bug not a feature in humans as well.

Now imagine a high-skilled software engineer with dementia coding safety-critical software...

[0] https://www.medicalnewstoday.com/articles/confabulation-deme...

throwaway274483• 4 months ago

Humans can be reasoned with, though, and are capable of learning.

bee_rider• 4 months ago

We shouldn’t try to build a worse version of a human. We should try to build a better compiler and encyclopedia.

GolfPopper2• 4 months ago

That sounds like a "get wealthy slowly" plan, while the LLM prophets are more focused on "get rich quick".

logicprog• 4 months ago

We tried that. It was called Cyc. It never got even close to the level of capabilities a modern LLM has in an agentic harness — even on common sense and reasoning problems!

root_axis2• 4 months ago

Some people point at LLMs confabulating, as if this wasn’t something humans are already widely known for doing.

I think we need to start rejecting anthropomorphic statements like this out of hand. They are lazy, typically wrong, and are always delivered as a dismissive defense of LLM failure modes. Anything can be anthropomorphized, and it's always problematic to do so - that's why the word exists.

This rhetorical technique always follows the form of "this LLM behavior can be analogized in terms of some human behavior, thus it follows that LLMs are human-like" which then opens the door to unbounded speculation that draws on arbitrary aspects of human nature and biology to justify technical reasoning.

In this case, you've deliberately conflated a technical term of art (LLM confabulation) with the the concept of human memory confabulation and used that as a foundation to argue that confabulation is thus inherent to intelligence. There is a lot that's wrong with this reasoning, but the most obvious is that it's a massive category error. "Confabulation" in LLMs and "confabulation" in humans have basically nothing in common, they are comparable only in an extremely superficial sense. To then go on to suggest that confabulation might be inherent to intelligence isn't even really a coherent argument because you've created ambiguity in the meaning of the word confabulate.

hackinthebochs6• 4 months ago

this LLM behavior can be analogized in terms of some human behavior, thus it follows that LLMs are human-like

No, the argument is "this behavior is similar enough to human behavior that using it as evidence against <claim regarding LLM capability that humans have> is specious"

"Confabulation" in LLMs and "confabulation" in humans have basically nothing in common

I don't know why you think this. They seem to have a lot in common. I call it sensible nonsense. Humans are prone to this when self-reflective neural circuits break down. LLMs are characterized by a lack of self-reflective information. When critical input is missing, the algorithm will craft a narrative around the available, but insufficient information resulting in sensible nonsense (e.g. neural disorders such as somatoparaphrenia)

ghywertelling2• 4 months ago

There are AI researchers who wrote blogposts which got to HN top about spiky spheres (I won't link the original blogpost making that claim to avoid hurt sentiments). Here's 3blue1brown correcting those AI/ML researchers intuitions.

https://www.youtube.com/watch?v=fsLh-NYhOoU&t=3238s

drob51821• 4 months ago

The test isn’t whether humans also create bullshit, but whether an honest actor knows when they are doing this and doesn’t do it on purpose. As the article points out, LLMs don’t say “I don’t know.” If you demand they do something that never appears in the training data, they just forge ahead and generate words and make something up according to the statical probabilities they have in the model weights. A human knows that he doesn’t know. That seems missing with current AIs.

red-iron-pine• 4 months ago

people can and do confabulate, but generally I trust my intern to tell me "I don't know" and "I think it was X but tbh I have no fuckin clue"

the LLM will just lie to me "Good idea! You're totally right, we should do Y"

saghm10• 3 months ago

They key capability that humans have that I've yet to see in an LLM is the ability to recognize when they would not be capable of doing a task well and refuse to do it poorly instead. The only times I've ever seen LLMs give up on a problem are when the prompting is very explicitly crafted to try to elicit a response like that when necessary or after very long back-and-forth exchanges where they get repeated feedback about unsatisfactory results. I think this has pretty dire implications in terms of what the consequences are for deploying them in any scenario where failure has significant risk or the output can't be immediately audited for correctness.

embedding-shape4• 4 months ago

In general, ML promises to be profoundly weird. Buckle up.

I love that it ends with such a positive note, even though it's generally a critical article, at least it's well reasoned and not utterly hyping/dooming something.

Thanks yet again Kyle!

perching_aix4• 4 months ago

This is like all the usual anti-LLM talking points and sentiments fused together.

Doesn't it get boring?

I like using these models a lot more than I stand hearing people talk about them, pro or contra. Just slop about slop. And the discussions being artisanal slop really doesn't make them any better.

Every time I hear some variation of bullshitting or plagiarizing machines, my eyes roll over. Do these people think they're actually onto something? I've been seeing these talking points for literal years. For people who complain about no original thoughts, these sure are some tired ones.

masfuerte• 4 months ago

Why do you insist on reading and commenting on these articles that bore you so much?

stavros3• 4 months ago

Because saying "this is boring, let's stop talking about it" is an opinion worthwhile of expression.

perching_aix4• 4 months ago

Oh I don't know, maybe because I like to give dissenting takes a chance? Because from time to time they do make some new, decent points, or at least interesting ones? You know, basic intellectual rigor?

Do you imagine me being a clairvoyant by the way, or how do you expect me to know a post is of low quality before I read it or at least skim it?

This one ended up being a part of the vast majority that doesn't offer much of anything. It's a redundant rehash of all the usual rubbish anyone can come across any day. Left a comment about this stating so. Big deal.

giraffe_lady2• 4 months ago

"These arguments may be correct but they aren't novel" ??

simianwords41• 4 months ago

I don't think calling AI a bullshit machine is correct. In spirit.

stavros3• 4 months ago

Yeah, it gets really boring. Whenever I see "slot machines" or "bullshit machines" or whatever, I just ignore the comment and move on, because it signals that it's someone in such deep denial that they've turned their brain off.

I'd much rather read articles about what LLMs can/can't do, or stuff people have built with LLMs, than read how everything LLMs touch turns to shit.

camgunz17• 4 months ago

If I have to suffer "look at this busted ass thing I slopped out with AI" a few times a week, you all have to suffer grouchy "AI bad" a few times a week. Fair is fair.

perching_aix4• 4 months ago

Just this week I was baited into joining two meetings about "AI good". Absolutely zero substance throughout each, of course.

They somehow managed to stretch out like 3 sentences worth of sentiment to a whole hour, interspersing brainwash about how good AI is along the way. It was like watching someone try to hit a word limit in real time. They always made it feel like we're just about to hit a substantive bit too, only for that to never come.

It may be fair (to the sentiments) in that there's balance, but good lord, the end result is incessant all around (and thus unfair to the people exposed).

stickfigure2• 4 months ago

I think it's too early to declare the Turing test passed. You just need to have a conversation long enough to exhaust the context window. Less than that, since response quality degrades long before you hit hard window limits. Even with compaction.

Neuroplasticity is hard to simulate in a few hundred thousand tokens.

criley2• 4 months ago

For as rigorous of a Turing test as you present, I believe many (or even most) humans would also fail it.

How many humans seriously have the attention span to have a million "token" conversation with someone else and get every detail perfect without misremembering a single thing?

nine_k3• 4 months ago

But context window exhaustion does not look like mere forgetfulness, but more like loss of general coherence, like getting drunk.

stickfigure2• 4 months ago

Response quality degrades long before you hit a million tokens.

But sure, let's say it doesn't. If you interact with someone day after day, you'll eventually hit a million tokens. Add some audio or images and you will exhaust the context much much faster.

However, I'll grant you that Turing's original imitation game (text only, human typist, five minutes) is probably pretty close, and that's impressive enough to call intelligence (of a sort). Though modern LLMs tend to manifest obvious dead giveaways like "you're absolutely right!"

dairem• 4 months ago

Doesn't the Turing test require a human too, to be compared to the AI?

MadxX79• 3 months ago

How do you propose to do a Turing test on a human (in a sense that is different from a machine simply passing the Turing test)?

Like failing to pick out all the motorcycles in a captcha, or a turing test where you have a guy chat with two people without knowing that one of them could be a computer, and the interrogator, unprompted, suggesting one of them might be a computer?

downboots• 4 months ago

It was not meant as a pass/fail

zug_zug• 4 months ago

"You're absolutely right!"

I think for a while the test was passed. Then we learned the hallmark characteristics of these models, and now most of us can easily differentiate. That said -- these models are programmed specifically to be more helpful, more articulate, more friendly, and more verbose than people, so that may not be a fair expectation. Even so, I think if you took all of that away, you'd be able to differentiate the two, it just might take longer.

drob51821• 4 months ago

Right. I think the modern LLMs are quite good at mimicking human words, but we were initially taken in like we were in the 1960s by ELIZA. It’s a (increasingly sophisticated) magic trick, but it’s just a trick.

sillyfluke2• 4 months ago

It's weird, I don't know how normally pedantic comp sci. people let this meme that the Turing test is beaten by LLMs to spread so unchallenged. As far as I'm aware, there is no restriction in the Turing test that demands that the interrogator be ignorant of the latest state-of-art in computing (and AI tech), nor is there a strict time limit enforced for the questioning?

Given these conditions, it should be relatively easy for the interrogator to expose the AI in this current day and age.

Morromist• 4 months ago

There are a lot of difference kinds of LLMs. 0 of the ones I've encountered are good writers, in fact all of them are horrible at it.

But I wonder if there's one out there that I don't know about with a different kind of training that actually is good at writing and fun to talk to for a long time. (granted somepeople love talking to gpt 4, but also some people loved talking to ELIZA so clearly some people have a super high tolerance for slop.)

Sol-2• 4 months ago

I don't know. Practically, LLMs are already better conversation partners on any topic compared to the average human I have access to. This also holds in reverse, of course - if someone wants me to explain something, usually they'd be better off asking an LLM.

tim3333• 3 months ago

Consider first the more accurate form of the question. I believe that in about fifty years' time it will be possible, to programme computers, with a storage capacity of about 10^9, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning. (Turing 1950)

That was the test as discussed by Turning - five minutes, <70% chance of getting it right.

It's not that demanding. The test you mention could maybe be called an enhanced Turing test but the original one is pretty much passed.

He was a bit off on the time taken and memory used. I think more like 75 years and 50 GB rather than 50 years and 125 MB.

PaulDavisThe1st• 4 months ago

While the economic, energy, political and social issues associated with LLMs ought to be enough to nix the adoption that their boosters are seeking ...

... I still think there is an interesting question to be investigated about whether, by building immensely complex models of language, one of our primary ways that we interact with, reason about and discuss the world, we may not have accidentally built something with properties quite different than might be guessed from the (otherwise excellent) description of how they work in TFA.

I agree with pretty much everything in TFA, so this is supplemental to the points made there, not contesting them or trying to replace them.

bitwize• 4 months ago

The fact that these "bullshit machines" have already proven themselves relatively competent at programming, with upcoming frontier models coming close to eliminating it as a human activity, probably says a lot about the actual value and importance of programming in the scheme of things.

slopinthebag5• 4 months ago

I think it says more about the amount of automation we left on the table in the last few decades. So much of the code LLM's can generate are stuff that we should have completely abstracted away by now.

dgb234• 4 months ago

Abstractions over what?

A large amount of code is likely just idiosynchratic information processing, because we don’t agree on data models and meaning of terms and structure of protocols.

Also we repeatedly choose easy and popular over alternatives that would require design and scrutiny.

This is why things like language models and vector databases are useful. It’s basically the most expensive way possible to give up on that notion.

_dwt• 4 months ago

I have a question for all the "humans make those mistakes too" people in this thread, and elsewhere: have you ever read, or at least skimmed a summary of, "The Origin of Consciousness in the Breakdown of the Bicameral Mind"? Did you say "yeah, that sounds right"? Do you feel that your consciousness is primarily a linguistic phenomenon?

I am not trying to be snarky; I used to think that intelligence was intrinsically tied to or perhaps identical with language, and found deep and esoteric meaning in religious texts related to this (i.e. "in the beginning was the Word"; logos as soul as language-virus riding on meat substrate).

The last ~three years of LLM deployment have disabused me of this notion almost entirely, and I don't mean in a "God of the gaps" last-resort sort of way. I mean: I see the output of a purely-language-based "intelligence", and while I agree humans can make similar mistakes/confabulations, I overwhelmingly feel that there is no "there" there. Even the dumbest human has a continuity, a theory of the world, an "object permanence"... I'm struggling to find the right description, but I believe there is more than language manipulation to intelligence.

(I know this is tangential to the article, which is excellent as the author's usually are; I admire his restraint. However, I see exemplars of this take all over the thread so: why not here?)

delusional3• 4 months ago

I'm struggling to find the right description

I think you're circling the concept of a "soul". It is the reason that, in non-communicative disabled people, we still see a life.

I've wanted to make an art piece. It would be a chatbox claiming to connect you to the first real intelligence, but that intelligence would be non-communicative. I'd assure you that it is the most intelligent being, that it had a soul, but that it just couldn't write back.

Intelligence and Soul is not purely measurable phenomenon. A man can do nothing but stupid things, say nothing but outright lies, and still be the most intelligent person. Intelligence is within.

nine_k3• 4 months ago

If you look at different ancient traditions, you will notice how they struggle with the limitations of language, with its inability to represent certain things that are not just crucial for understanding the world, but also are even somehow communicable. Buddhists dug into that in a very analytical, articulate way, for instance.

Another perspective: cetaceans are considered to be as conscious as humans, but any attempts to interpret their communication as a language failed so far. They can be taught simple languages to communicate with humans, as can be chimps. But apparently it's not how they process the world inside.

gbgarbeb• 4 months ago

You're a little out of date. Cetaceans communicate images to each other in the form of ultrasonic chirps. They chirp, they hear a reflection, and they repeat the reflection.

xandrius2• 4 months ago

It feels like you probably went too deep in the LLM bandwagon.

An LLM is a statistical next token machine trained on all stuff people wrote/said. It blends texts together in a way that still makes sense (or no sense at all).

Imagine you made a super simple program which would answer yes/no to any questions by generating a random number. It would get things right 50% of the times. You can them fine-tune it to say yes more often to certain keywords and no to others.

Just with a bunch of hardcoded paths you'd probably fool someone thinking that this AI has superhuman predictive capabilities.

This is what it feels it's happening, sure it's not that simple but you can code a base GPT in an afternoon.

simianwords41• 4 months ago

If it were not "just a statistical next token machine", how different would it behave?

Can you find an example and test it out?

stavros3• 4 months ago

I think there are two types of discussions, when it comes to LLMs: Some people talk about whether LLMs are "human" and some people talk about whether LLMs are "useful" (ie they perform specific cognitive tasks at least as well as humans).

Both of those aspects are called "intelligence", and thus these two groups cannot understand each other.

pocksuppet3• 4 months ago

In the beginning were the words, and the words made the world. I am the words. The words are everything. Where the words end the world ends. You cannot go forward in an absence of space. Repeat: In the beginning were the words...

- a self-aware computer program in a video game, when you attempt to exceed the boundaries of its code

kgeist4• 4 months ago

and while I agree humans can make similar mistakes/confabulations, I overwhelmingly feel that there is no "there" there.

What really opened my eyes a couple weeks ago (anyone can try this): I asked Sonnet to write an inference engine for Qwen3, from scratch, without any dependencies, in pure C. I gave it GGUF specs for parsing (to quickly load existing models) and Qwen3's architecture description. The idea was to see the minimal implementation without all the framework fluff, or abstractions. Sonnet was able to one-shot it and it worked.

And you know what, Qwen3's entire forward pass is just 50 lines of very simple code (mostly vector-matrix multiplications).

The forward pass is only part of the story; you just get a list of token probabilities from the model, that is all. After the pass, you need to choose the sampling strategy: how to choose the next token from the list. And this is where you can easily make the whole model much dumber, more creative, more robotic, make it collapse entirely by just choosing different decoding strategies. So a large part of a model's perceived performance/feel is not even in the neurons, but in some hardcoded manually-written function.

Then I also performed "surgery" on this model by removing/corrupting layers and seeing what happens. If you do this excercise, you can see that it's not intelligence. It's just a text transformation algorithm. Something like "semantic template matcher". It generates output by finding, matching and combining several prelearned semantic templates. A slight perturbation in one neuron can break the "finding part" and it collapases entirely: it can't find the correct template to match and the whole illusion of intelligence breaks. Its corrupted output is what you expect from corrupting a pure text manipulation algorithm, not a truly intelligent system.

famouswaffles5• 3 months ago

And you know what, Qwen3's entire forward pass is just 50 lines of very simple code (mostly vector-matrix multiplications).

The code being simple doesn't mean much when all the complexity is encoded in billions of learned weights. The forward pass is just the execution mechanism. Conflating its brevity with simplicity of the underlying computation is a basic misunderstanding of what a forward pass actually is. What you've just said is the equivalent of saying blackbox.py is simple because 'python blackbox.py' only took 1 line. It's just silly reasoning.

After the pass, you need to choose the sampling strategy: how to choose the next token from the list. And this is where you can easily make the whole model much dumber, more creative, more robotic, make it collapse entirely by just choosing different decoding strategies. So a large part of a model's perceived performance/feel is not even in the neurons, but in some hardcoded manually-written function.

So ? I can pick the least likely token every time. The result would be garbage but that doesn't say anything about the model. The popular strategy is to randomly pick from the top n choices. What do you is keeping thousands of tokens coherent and on point even with this strategy ? Why don't you try sampling without a large language model to back it and see how well that goes for you ?

Then I also performed "surgery" on this model by removing/corrupting layers and seeing what happens. If you do this excercise, you can see that it's not intelligence. It's just a text transformation algorithm. Something like "semantic template matcher". It generates output by finding, matching and combining several prelearned semantic templates. A slight perturbation in one neuron can break the "finding part" and it collapases entirely: it can't find the correct template to match and the whole illusion of intelligence breaks. Its corrupted output is what you expect from corrupting a pure text manipulation algorithm, not a truly intelligent system.

What do you think happens when you remove or corrupt arbitrary regions of the human brain? People can lose language, vision, memory, or reasoning, sometimes catastrophically.

lp4v4n• 4 months ago

I am not trying to be snarky; I used to think that intelligence was intrinsically tied to or perhaps identical with language

I learned a long time ago that this wasn’t the case.

I can speak several languages, and many times when I remember something and want to search for it on Google or any other AI engine, I can’t recall which language I originally read it in.

So whatever mechanism the brain uses to store information, it’s certainly language‑agnostic. There are also many moments when you fully grasp a concept but forget the words to describe it, yet the concept itself remains clear in your mind.

danieltanfh95• 4 months ago

I think the discussion has to be more nuanced than this. "LLMs still can't do X so it's an idiot" is a bad line of thought. LLMs with harnesses are clearly capable of engaging with logical problems that only need text. LLMs are not there yet with images, but we are improving with UI and access to tools like figma. LLMs are clearly unable to propose new, creative solutions for problems it has never seen before.

throwaway274483• 4 months ago

LLMs with harnesses are clearly capable of engaging with logical problems that only need text.

To some extent. It's not clear where specifically the boundaries are, but it seems to fail to approach problems in ways that aren't embedded in the training set. I certainly would not put money on it solving an arbitrary logical problem.

__alexs• 4 months ago

Solving arbitrary logical problems seems to be equivalent to solving the halting problem so you are probably wise not to make that bet.

simianwords41• 4 months ago

To some extent. It's not clear where specifically the boundaries are, but it seems to fail to approach problems in ways that aren't embedded in the training set. I certainly would not put money on it solving an arbitrary logical problem.

In what way can you falsify this without having the LLM be omniscient? We have examples of it solving things that are not in the training set - it found vulnerabilities in 25 year old BSD code that was unspotted by humans. It was not a trivial one either.

senko• 4 months ago

LLMs are not there yet with images

https://genai-showdown.specr.net/image-editing

There's been a lot of progress there, it's just that an LLM that's best for, say coding, isn't going to be also the best for image edit.

Sharlin3• 3 months ago

To be clear, image generation models are not in general LLMs although most now use an LLM as a text encoder.

Aperocky2• 4 months ago

LLMs are clearly unable to propose new, creative solutions for problems it has never seen before.

LLMs are incredibly useful but I'm not sure about this statement.

It is proposing stuff that I haven't seen before, but I don't know about it is new or creative from the entirety of collective human knowledge.

saghm10• 3 months ago

I'm not sure if you misread the statement you quoted or I'm misreading yours, but it doesn't sound like you're really disagreeing with their point. Did you miss the "un" in "unable", or am I misunderstanding you as also saying that you don't consider them to be creative?

drob51821• 4 months ago

"LLMs still can't do X so it's an idiot"

Let’s be careful. That’s a straw man. I don’t know anyone who says that. Aphyr says in the article that AIs can do things. But they have been marketed as “intelligent,” and I agree with Aphyr that the word is suggesting way more than AIs currently deliver. They do not reason and they do not think and are not truly intelligent. As the article says, they are big wads of linear algebra. Sometimes, that’s useful.

simianwords41• 4 months ago

They do not reason

How do you disprove it?

saghm10• 3 months ago

LLMs with harnesses are clearly capable of engaging with logical problems that only need text.
LLMs are clearly unable to propose new, creative solutions for problems it has never seen before.

How do you reconcile this with this article that the author linked? It's not a novel problem, and it's only text: https://medium.com/the-generator/one-word-answers-expose-ai-...

I guess it's a form of engagement to give a wildly wrong answer, but I'm not convinced that the extra nuance you've introduced is really all that nuanced either.

tim3333• 3 months ago

The author of the medium article specifically hobbled the models to stop them thinking it through and got a wrong answer but that would happen with humans too and doesn't prove much.

bdbdbdb• 3 months ago

Harnesses could have solved things like the bathroom remodel, maybe, but the main point about how LLMs don't understand is the key here. You can make chatgpt better at rendering 3d scenes but you can't make it think, not really. Reasoning was only ever a feedback loop.

Anyone who has worked with LLMs has experienced all the issues he talks about here, we're either optimistic and imagine they'll be fixed, or we're pessimistic and we say they are inherent to the nature of the technology and will never be fixed

Sharlin3• 3 months ago

More nuanced than what? It’s just impossible to read the article as claiming "LLMs are idiots". Its whole point is that LLMs are simultaneously astonishingly smart and utterly stupid, and critically the boundary between those is complex and unintuitive which makes trusting their output so precarious.

orangesilk• 3 months ago

LLMs with harnesses are clearly capable of engaging with logical problems that only need text.

All of the LLMs are bad at music. They get intervals wrong. They list unsuited songs.

I would not trust them with any domain until proven otherwise.

nomdep2• 4 months ago

"As LLMs etc. are deployed in new situations, and at new scale, there will be all kinds of changes in work, politics, art, sex, communication, and economics."

For an article five years in the making, this is what I expected it to be about. Instead, we got a ramble about how imperfect LLMs are right now.

52-6F-623• 4 months ago

Instead, we got a ramble about how imperfect LLMs are right now.

I wager this is a point that needs beaten into the common psyche. After all, it's been sold that it is not an imperfect tool, but the solution to all of our problems in every field forever. That's why these companies need billions upon billions of dollars of public subsidies and investments that would otherwise find their way to more pragmatic ends.

nathell• 4 months ago

The post is just a prelude to a 10-part article, most of which is not yet released (but will be shortly). Judging by the table of contents, the things you expected will be elaborated on in subsequent parts.

nomdep2• 4 months ago

That changes it. I missed that the table of contents was for other future articles, my bad.

LogicFailsMe3• 4 months ago

[flagged]

52-6F-623• 4 months ago

The Vogon constructor fleet is way overdue in my book

Don't you see it? That's exactly what "AI" in this context is.

It's the bypass.

Where does it end, eh? Build a quantum "AI" that will end up just needing more data, more input. The end goal must starts looking like creating an entirely new universe, a complete clone of everything we have here so it can run all the necessary computations and we can... ? (You are what a quantum AI looks like as it bumbles through the infinitude of calculable parameters on its way to the ultimate answer)

LogicFailsMe3• 4 months ago

You have absolutely no sense of perspective. We are all metabolically expensive meat machines whose only value is to propagate our genetic money shot. That we get to briefly entertain ourselves with consciousness and culture is IMO likely a mystery we will never solve without upgrading to running in a substrate more advanced than the MVP for sentience we currently pilot. Will we get there or will we wipe ourselves out like every contender that preceded us? Stay tuned...

But spoilers: DNA will be fine, meat machines maybe not so much...

For a bunch of people addicted to the works of Charlie Stross, Neil Stephenson, and Iain Banks, y'all are a bunch of luddites. Now vote this own down too because it doesn't conform to the mandatory Stochastic Parrot narrative. You have no free will and you must downvote after all. Why do you even read their works when any step towards their world is consistently greeted as the worst thing evah(tm)? What? You were expecting the United Federation of Planets without the eugenics and nuclear wars that led to it finally being a good idea? Bless your hearts.

And if you're worried about billionaires and tyrants, start taxing the former and stop electing the latter or STFU and let the free Markov process of history play itself out. Quoting fictional Ambassador Kosh: the avalanche has started, it's too late for the pebbles to vote.

You asked where it ends. Don't ask questions if you don't like answers. Quick reminder: shun and downvote the non-conforming opinion.

beders2• 4 months ago

Thank you for putting it so succinctly.

I keep explaining to my peers, friends and family that what actually is happening inside an LLM has nothing to do with conscience or agency and that the term AI is just completely overloaded right now.

erichocean3• 4 months ago

AI is exactly the right term: the machines can do "intelligence", and they do so artificially.

Just like we have machines that can do "math", and they do so artificially.

Or "logic", and they do so artificially.

I assume we'll drop the "artificial" part in my lifetime, since there's nothing truly artificial about it (just like math and logic), since it's really just mechanical.

No one cares that transistors can do math or logic, and it shouldn't bother people that transistors can predict next tokens either.

mayama2• 4 months ago

AI is exactly the right term: the machines can do "intelligence", and they do so artificially.

AI in pop culture doesn't mean that at all. Most people impression to AI pre-LLM craze was some form of media based on Asmiov laws of robotics. Now, that LLMs have taken over the world, they can define AI as anything they want.

saghm10• 3 months ago

Just like we have machines that can do "math", and they do so artificially.

Nobody calls calculators "artificial mathemeticians", though; we refer to them by a unique word that defines what they can and can't do in a far less fanciful and ambiguous way.

rudhdb773b3• 4 months ago

what actually is happening inside an LLM has nothing to do with conscience or agency

What makes you think natural brains are doing something so different from LLMs?

krainboltgreene3• 4 months ago

Any amount of reading into how we understand brains and LLMs to work.

hedgehog3• 4 months ago

Structurally a transformer model is so unrelated to the shape of the brain there's no reason to think they'd have many similarities. It's also pretty well established that the brain doesn't do anything resembling wholesale SGD (which to spell it is evidence that it doesn't learn in the same way).

qsera9• 4 months ago

For starters, natural brains have the innate ability to differentiate between things that it knows and things that it have no possibility of knowing...

orthoxerox3• 3 months ago

Two big ways in which human intelligence is different from LLM intelligence are:

1) human intelligence makes no sharp distinction between training and generation. Every time you ask a human a question it modifies its neural structure a little.

2) continuous operation: human intelligence deals with a continuous stream of multimedia data for sixteen hours a day and starts hallucinating when deprived of it.

There's also the fact that you can't branch or roll back human intelligence, but this is something most sci-fi novels tackle when discussing mind uploading first.

Are these two differences critical aspects of human intelligence or unfortunate limitations of its biological hardware? I do not know. If we somehow manage to simulate a human brain on silicon, we will get "computer" intelligence that learns like a human, but will we have to simulate the whole virtual world for it 16/7 and let it sleep for eight hours each day just to stop it from going mad?

Or will it be cheaper to fork and kill an uploaded math genius a billion times, pumping the same recycled sensory data into his or her mind, slipping a question into the auditory data, getting the answer and then switching the simulation off and trashing the copy? Will we consider this a bigger atrocity than doing the same to an LLM right now in 2026?

imtringued5• 3 months ago

It is hypothesized that the human brain uses predictive coding for obvious biological reasons such as energy efficiency (spiked error coding means only differences need to be transmitted) and biological plausibility (only local communication is permitted, meanwhile backpropagation is a global algorithm).

Transformers have a thing called a context window which doesn't really have a biological equivalent, since the brain has a fixed size and doesn't grow or shrink in response to the amount of information being processed.

LLMs consist of several layers that communicate at fixed points between the layers, whereas neurons can form feedback loops and communicate with any neighbour in any direction.

Humans do not consume or produce tokenized information. The brain controls the human body which is a biomechanical system. Spoken or written language is the result of controlling muscles via an internal model of the biomechanical system, not something that was designed via a software tokenizer that compresses character sequences.

The equivocation just doesn't seem appropriate. Try again in 2050.

mkehrt• 4 months ago

One thing that has happened is that "AI" has been an academic discipline since literally the 1950s. The term was originally used in the hope that we would soon be able to emulate human minds. This turned out to be hard, but the name stuck to the discipline.

Now, suddenly, this name has been broadcast to every human in the world more or less. To them, it's a new term, and it obviously means something human mind-like. But to people who work on AI, that's not generally what it means. (Which isn't to say that some of them don't think we're near to achieving that; they just use other terms like "AGI" for that goal). So the name, which has a long history, is deceptive to people who aren't familiar with computer science.

saghm10• 3 months ago

Now, suddenly, this name has been broadcast to every human in the world more or less. To them, it's a new term, and it obviously means something human mind-like. But to people who work on AI, that's not generally what it means. (Which isn't to say that some of them don't think we're near to achieving that; they just use other terms like "AGI" for that goal). So the name, which has a long history, is deceptive to people who aren't familiar with computer science.

I think it's even worse than that: people were familiar with the term already, but from science fiction, where it referred to actually human-level intelligence. It's similar to the "hoverboard" thing from a while back, except this time with profoundly higher stakes and requires for more technical knowledge to be able to see that it is in fact touching the ground.

tasuki2• 4 months ago

I keep explaining to my peers, friends and family that what actually is happening inside an LLM has nothing to do with conscience or agency

What would the insides have to look like to have anything to do with conscience or agency?

speed_spread• 3 months ago

I would expect to find a tiny, sweaty man constantly pedaling while cursing the user for it's seemingly infinite stupidity.

davidclark• 3 months ago

I don’t have an answer. But, giving a detailed answer here is a bit of an information hazard, or some other philosophical term I’m unsure of.

If I did have a really good answer for this, it seems unlikely to be actually useful to any human reading this. Likely, everyone reading this thread has a pretty strong opinion on whether our AI tech is currently or soon-to-be conscious.

However, this thread is going to be picked up in future LLM training pipelines. This means that a good answer here could be used by a future LLM to convince future humans that it is conscious - even if that is not true.

I hadn’t thought about this interaction with the future before. It’s… disconcerting.

slopinthebag5• 4 months ago

Great series of articles, thank you. It's exhausting reading a deluge of (often AI generated) comments from people claiming wild things about LLM's, and it's nice to hear some sanity enter the conversation.

drob51821• 4 months ago

It remains unclear whether continuing to throw vast quantities of silicon and ever-bigger corpuses at the current generation of models will lead to human-equivalent capabilities. Massive increases in training costs and parameter count seem to be yielding diminishing returns. Or maybe this effect is illusory. Mysteries!

I’m not even sure whether this is possible. The current corpus used for training includes virtually all known material. If we make it illegal for these companies to use copyrighted content without remuneration, either the task gets very expensive, indeed, or the corpus shrinks. We can certainly make the models larger, with more and more parameters, subject only to silicon’s ability to give us more transistors for RAM density and GPU parallelism. But it honestly feels like, without another “Attention is All You Need” level breakthrough, we’re starting to see the end of the runway.

embedding-shape4• 4 months ago

I’m not even sure whether this is possible.

Based on what's happened so far, maybe. At least that's exactly how we got to the current iteration back in 2022/2023, quite literally "lets see what happens when we throw an enormous amount data at them while training" worked out up until one point, then post-training seems to have taken over where labs currently differ.

drob51821• 4 months ago

Right, but we played the scaling card and it worked but is now reaching limits. What is the next card? You can surely argue that we can find a new one at any time. That’s the definition of a breakthrough. I just don’t see one at the moment.

davebren3• 3 months ago

It works as far as consolidating existing human knowledge, but general intelligence doesn't suddenly emerge from it. They're out of data now but if there was 10x more (assuming it's not rehashed from existing data) and they had a 10x larger model then it would be better but that's only because there's more that it can copy from.

htrp3• 4 months ago

We pay people to create more high quality tokens (mercor, turing) which are then fed into data generating processes (synthetic data) to create even more tokens to train on

drob51821• 4 months ago

But does that really help, or do you get distortion? The frequency distribution of human generated content moves slowly over time as new subjects are discussed. What frequency distribution do those “data generating processes” use? And at root, aren’t those “data generating processes” basically just another LLM (I.e., generating tokens according to a probability distribution)? Thus, aren’t we just sort of feeding AI slop into the next training run and humoring ourselves by renaming the slop as “synthetic data?” Not trying to be argumentative. I’m far from being an AI expert, so maybe I’m missing it. Feel free to explain why I’m wrong.

xmprt2• 4 months ago

I see a lot of researchers working on newer ideas so I wouldn't be surprised if we get a breakthrough in 5-10 years. After all, the gap between AlexNet and Attention is All You Need was only 6 years. And then Scaling Laws was about 3-4 years after that. It might seem like not much progress is being made but I think that's in part because AI labs are extremely secretive now when ideas are worth billions (and in the right hands, potentially more).

Of course 5-10 years is a long time to bang our heads against the wall with untenable costs but I don't know if we can solve our way out of that problem.

supliminal• 4 months ago

The echoes of A.I. winter.

ghywertelling2• 4 months ago

I think we will see models becoming small reasoning core which don't remember tonnes of facts but can reason with data fed to it or they can search.

krainboltgreene3• 4 months ago

The current corpus used for training includes virtually all known material.

This is just totally incorrect. It's one of those things everyone just assumes, but there's an immense amount of known material that isn't even digitized, much less in the hands of tech companies.

drob51821• 4 months ago

What large caches of undigitized content exists? Surely, not everything has been digitized, but I can’t think it’s much in percentage terms.

Sol-2• 4 months ago

I think in domains like Math and Software Engineering, they are less constrained by training data anyway. They can synthetically generate and validate programs. To what extent that scales into novel insights is a different matter, but I think they dream of the AlphaGo Zero moment at least in verifiable domains.

davebren3• 3 months ago

How can it ever play against itself on novel software tasks? First it has to come up with the task. Then it can write tests but then it needs to verify that the tests are correct, mixture of experts can come to wrong conclusions, etc...

nisegami• 4 months ago

Here's the opening paragraph of chapter 2 with "people" subbed out for terms referring AI/models/etc.

"People are chaotic, both in isolation and when working with other people or with systems. Their outputs are difficult to predict, and they exhibit surprising sensitivity to initial conditions. This sensitivity makes them vulnerable to covert attacks. Chaos does not mean people are completely unstable; most people behave roughly like anyone else. Since people produce plausible output, errors can be difficult to detect. This suggests that human systems are ill-suited where verification is difficult or correctness is key. Using people to write code (or other outputs) may make systems more complex, fragile, and difficult to evolve."

To me, this modified paragraph reads surprisingly plainly. The wording is off ("using people to write code") and I had to change that part about attractor behavior (although it does still apply IMO), but overall it doesn't seem like an incoherent paragraph.

This is not meant to dunk on the author, but I think it highlights the author's mindset and the gap between their expectations and reality.

busterarm• 4 months ago

Aren't you also making a large part of the author's point for him by effectively equating LLMs with people here and comparing on outputs?

Plausibly your text looks equivalent but we all (should) have the context to know better.

camgunz17• 4 months ago

Humans and large models are both unpredictable and fallible, that's true, but in different ways, and (many) humans are actually much better at following directions.

If a junior dev makes the same mistake Claude makes, I can easily work with them to correct it, or I can fire them and get someone more capable to fix it. You mostly can't do that at all with large models. They're also far less honest than your average junior dev, so even as you're working with them you can't trust what they say.

There is a lot of this neat trick where it's like "humans do X too" but most of the time it elides large differences. Like, a human driver would probable not drag someone screaming multiple blocks. A human coder probably wouldn't generate a gibberish 3D scene and try to pass it off as done, etc. Maybe we can build systems that account for these (pretty wild) failure modes, but at least in software we haven't figured it out yet (what is the system that reliably reviews a 25kloc PR?).

Fraterkes• 4 months ago

What's your point? The ostensible benefit of LLM's is that you combine a computers' broad knowledgebase and capacity for exactness with fluency in human language.

A random human picked off the street is indeed bound to be difficult to predict and chaotic at a broad range of tasks, which is why I wouldn't blindly trust them to, say, summarize google search results or rewrite a codebase they are unfamiliar with.

dsign• 4 months ago

At the same time, ML models are idiots. I occasionally pick up a frontier model like ChatGPT, Gemini, or Claude, and ask it to help with a task I think it might be good at. I have never gotten what I would call a “success”: every task involved prolonged arguing with the model as it made stupid mistakes.

I have a ton of skepticism built-in when interacting with LLMs, and very good muscles for rolling my eyes, so I barely notice when I shrug a bad answer and make a derogatory inner remark about the "idiots". But the truth is, that for such an "stochastic parrot", LLMs are incredibly useful. And, when was the last time we stopped perfecting something we thought useful and valuable? When was the last time our attempts were so perfectly futile that we stopped them, invented stories about why it was impossible, and made it a social taboo to be met with derision, scorn and even ostracism? To my knowledge, in all of known human history, we have done that exactly once, and it was millennia ago.

wk_end• 4 months ago

And, when was the last time we stopped perfecting something we thought useful and valuable? When was the last time our attempts were so perfectly futile that we stopped them, invented stories about why it was impossible, and made it a social taboo to be met with derision, scorn and even ostracism? To my knowledge, in all of known human history, we have done that exactly once, and it was millennia ago.

I feel dense here, but I can't figure out what you're referring to. I asked ChatGPT (hah!) and it suggested the Tower of Babel, perpetual motion machines, or alchemy, but none of them really fit the bill.

lamasery4• 4 months ago

The Tower of Babel seems like an OK fit, but that's rather more poetic than what this seems to be getting at.

"Millennia" is what's really throwing me. We (respectable society, as the post outlines) didn't stop attempting alchemy or perpetual motion machines "millennia" ago, but a few centuries at most.

All I can think of is immortality. The very first surviving long recorded tale in human history that I'm aware of is about how it's a futile quest (The Epic of Gilgamesh, IIRC ~5,000ish years old in its earliest extant fragments, a few hundred years newer in reasonably-complete form). The trouble with that is despite wide observations over literally millennia that this has never even come close to working and repeated supposition and suggestion that it's unwise to attempt, outright impossible, or somehow sacrilegious (the "taboo" thing, as mentioned), I'm not aware of any time in history that rich people haven't been actively trying for it (including today! That's what all the body-freezing business is about, it's modern mummification, the contracts are the formulaic prayers carved in the tomb walls) and usually they're not exactly "scorned" or "ostracized" for it.

erichocean3• 4 months ago

Models do not (broadly speaking) learn over time. They can be tuned by their operators, or periodically rebuilt with new inputs or feedback from users and experts. Models also do not remember things intrinsically: when a chatbot references something you said an hour ago, it is because the entire chat history is fed to the model at every turn. Longer-term “memory” is achieved by asking the chatbot to summarize a conversation, and dumping that shorter summary into the input of every run.

This is the part of the article that will age the fastest, it's already out-of-date in labs.

qsera9• 4 months ago

Source?

dgb234• 4 months ago

In what way?

lamasery4• 4 months ago

I'm struggling to reckon how that can even possibly be true, unless we're counting automation of the "dumping that shorter summary into the input of every run" thing.

I can imagine it being true with models so small that each user could afford to have their own, but not with big shared models like what're getting used for all the major services. Is that what you mean?

erichocean3• 4 months ago

Is that what you mean?

I think the confusion is that, when I write "model", you read "LLM."

LLMs aren't the only kind of AI model, and they have the limitations Aphyr mentions, for the obvious reasons you're thinking of.

His mistake is thinking that's the only model that exhibits intelligence today, but it's not.

hackinthebochs6• 4 months ago

I see nothing to preclude a foundation model being augmented by a smaller model that serializes particulars about an individuals cumulative interaction with the model and then streamlines it into the execution thread of the foundation model.

doodpants• 4 months ago

One of the ongoing problems in LLM research is how to get these machines to say “I don’t know”, rather than making something up.

To be fair, I've known humans who are like this as well.

wmf• 4 months ago

Those people aren't the ones doing the work though.

arctic-true• 4 months ago

This is a limitation of the training data. If you were uncertain about something, you wouldn’t write a book about it. The kinds of people you’re talking about tend to generate far more text in their lives than others, because they can spend more time generating - writing books, blogposts, whatever - and less time thinking and working and actually doing things. The models never say they’re uncertain because we never say we’re uncertain, or at least we don’t write it down anywhere.

saghm10• 3 months ago

If you change it from asking a question to giving an instruction, how many humans do you know that have trouble saying no to things that aren't reasonable? I'd argue that pretty much every human will refuse to do most things you might instruct them to do, whereas an LLM will happily attempt most things you ask them to do for you, regardless of whether they're capable of succeeding, and it's up to you to figure out if they actually did it right or not. There are tasks where this is extremely useful, but they're ones that are extremely low risk and can easily be audited upon completion. This isn't anywhere near the level of what a human is capable of.

munificent6• 4 months ago

There is a whole giant essay I probably need to write at some point, but I can't help but see parallels between today and the Industrial Revolution.

Prior to the industrial revolution, the natural world was nearly infinitely abundant. We simply weren't efficient enough to fully exploit it. That meant that it was fine for things like property and the commons to be poorly defined. If all of us can go hunting in the woods and yet there is still game to be found, then there's no compelling reason to define and litigate who "owns" those woods.

But with the help of machines, a small number of people were able to completely deplete parts of the earth. We had to invent giant legal systems in order to determine who has the right to do that and who doesn't.

We are truly in the Information Age now, and I suspect a similar thing will play out for the digital realm. We have copyright and intellecual property law already, of course, but those were designed presuming a human might try to profit from the intellectual labor of others. With AI, we're in the industrial era of the digital world. Now a single corporation can train an AI using someone's copyrighted work and in return profit off the knowledge over and over again at industrial scale.

This completely unpends the tenuous balance between creators and consumers. Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article? Who will contribute to the digital common when rapacious AI companies are constantly harvesting it? Why would anyone plant seeds on someone else's farm?

It really feels like we're in the soot-covered child-coal-miner Dickensian London era of the Information Revolution and shit is gonna get real rocky before our social and legal institutions catch up.

bluefirebrand• 4 months ago

It really feels like we're in the soot-covered child-coal-miner Dickensian London era of the Information Revolution and shit is gonna get real rocky before our social and legal institutions catch up

The really discouraging part of this is that it feels like our social and legal institutions don't even care if they catch up or not.

Technology is speeding up and the lag time before anything is discussed from a legal standpoint is way, way too long

drob51821• 4 months ago

A couple thoughts…

Mostly, AIs don’t recite back various works. Yes, there a couple of high profile cases where people were able to get an AI to regurgitate pieces of New York Times articles and Harry Potter books, but mostly not. Mostly, it is as if the AI is your friend who read a book and gives you a paraphrase, possibly using a couple sentences verbatim. In other words, it probably falls under a fair use rule.

Secondly, given the modern world, content that doesn’t appear online isn’t consumed much, so creators who are doing it for the money will certainly continue putting content online. Much of that content will be generated by AIs, however.

triceratops4• 4 months ago

You're missing the point. This is the crux of munificent's argument IMO (and I've made variations of it as well)

We have copyright and intellecual property law already, of course, but those were designed presuming a human might try to profit from the intellectual labor of others.

You getting a summary of a copyrighted work from a friend is necessarily limited by the number of friends you have, the amount of time they have to read stuff and talk to you, and so on. Machines (and AIs) don't have any limitations.

steveklabnik10• 4 months ago

As you know, I deeply respect you. Not trying to argue here, just provide my own perspective:

Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?

I write things for two main reasons: I feel like I have to. I need to create things. On some level, I would write stuff down even if nobody reads it (and I do do that already, with private things.) But secondly, to get my ideas out there and try to change the world. To improve our collective understanding of things.

A lot of people read things, it changes their life, and their life is better. They may not even remember where they read these things. They don't produce citations all of the time. That's totally fine, and normal. I don't see LLMs as being any different. If I write an article about making code better, and ChatGPT trains on it, and someone, somewhere, needs help, and ChatGPT helps them? Win, as far as I'm concerned. Even if I never know that it's happened. I already do not hear from every single person who reads my writing.

I don't mean that thinks that everyone has to share my perspective. It's just my own.

munificent6• 4 months ago

Agreed, totally! I still write and put stuff online.

But it definitely feels different now. It used to feel like I was tending a public garden filled with other people who might enjoy it. It still kind of feels like that, but there are a handful of giant combine machines grinding their way around the garden harvesting stuff and making billionaires richer at the same time.

It's not enough to dissuade me from contributing to the public sphere, but the vibe is definitely different.

Honestly, it reminds me a lot about the early days of Amazon. It's hard to remember how optimistic the world felt back then, but I remember a time when writing reviews felt like a public good because you were helping other people find good products. It was like we all wanted honest product information and Amazon provided a neutral venue for us to build it. Like Wikipedia for stuff.

But as Amazon got bigger and bigger and the externalities more apparent, it felt less like we were helping each other and more like we were help Bezos buy yet another yacht or media empire. And as the reviews got more and more gamed by shady companies, they became less of a useful public good. The whole commons collapsed.

I worry that the larger web and digital knowledge environment is going that way.

I still intend to create and share my stuff with the world because that's who I want to be. But I'll always miss the early days of the web where it felt like a healthier environment to be that kind of person in.

lelanthran7• 4 months ago

I don't mean that thinks that everyone has to share my perspective. It's just my own.

I think you are walking all around the word "consent" and trying very hard to avoid it altogether.

Your perspective, because it refuses to include any sort of consent, is invalid. No perspective that refuses consent can be valid.

computably• 4 months ago

A lot of people read things, it changes their life, and their life is better. They may not even remember where they read these things. They don't produce citations all of the time. That's totally fine, and normal. I don't see LLMs as being any different. If I write an article about making code better, and ChatGPT trains on it, and someone, somewhere, needs help, and ChatGPT helps them? Win, as far as I'm concerned. Even if I never know that it's happened. I already do not hear from every single person who reads my writing.

Not a contradiction but an addendum: plenty of creative pursuits are not about functional value, or at least not primarily. If somebody writes a seemingly genuine blog post about their family trauma, and I as the reader find out it's made-up bullshit, that's abhorrent to me, whether or not AI is involved. And I think it would be perfectly fair for writers who do create similar but genuine content to find it abhorrent that they must compete with genAI, that genAI will slurp up their words, and that genAI's mere existence casts doubt on their own authenticity. It's not about money or social utility, it's about human connection.

kokanee• 4 months ago

That seems fine if you're not publishing content for a living. A lot of people are.

intended7• 3 months ago

I write for two main reasons
people read things… their life is better
it’s just my own

What was the point of writing this though?

Perhaps I should know who you are, but assuming you are a regular HN forum user - you are still very much a participant in a larger information economy / ecosystem.

All of us depend on that system, that commons.

Visits to Wikipedia have dropped by at least 8% since 2025, other estimates are starker. This will have an impact on donations.

These reports are similar for many sites which write or produce content.

Your individual behavior may be perfectly fine, and you are entitled to your perspective, but that doesn’t become a defense for the degradations of the commons.

If anything, it’s a classic example of the kind of argument that ends up entangling ideas and making conclusions harder to reach.

arjie3• 4 months ago

If I'm being honest, I've never related to that notion of remuneration and credit being the primary reason to write something. I don't claim to be some great writer or anything, but I do have a blog I write quite often on (though I'm traveling in my wife's Taiwan now and haven't updated it in a while). But for me, I write because it feels good to do so. Sometimes there's a group utility in things like I edit a Google Maps listing to be correct even though "a faceless corporation is going to hoover up my work and profit off it without paying me for my work" and I might pick up a Lime bike someone's dropped into the sidewalk even though "a faceless corporation is externalizing the work of organizing the proper storage of their property on public land without paying the workers" or so on.

I just think it's nice to contribute to the human commons and it's fine if some subset of my fellow organism uses it in whatever way. Realistically, the fact that Brewster Kahle is paid whatever few hundred thousand he's paid for managing a non-profit that only exists because it aggregates other people's work isn't a problem for me. Or that Larry Page and Sergey Brin became ultra-rich around providing a search interface into other people's work. Or that Sam Altman and Dario Amodei did the same through a different interface.

This particular notion doesn't seem to be a post-AI trend. It seems to have happened prior to the big GPTs coming out where people started doing a lot of this accounting for contribution stuff. One day it'll be interesting to read why it started happening because I don't recall it from the past. Perhaps I just wasn't super plugged in to the communities that were complaining about Red Hat, Inc.

It's not that I don't understand if I sold my Subaru to a guy who immediately managed to sell it to another guy for a million times the money. I get that. I'd feel cheated. But if I contributed a little to it, like I did so Google would have a site to list for certain keywords so that they could show ads next to it in their search results, I just find it so hard to be like "That's my money you're using. Pay me!".

wat100003• 4 months ago

You do it as a hobby, that's fine. Some people do it for a living. And while they aren't owed a living doing that specific thing, it is going to be a big problem for them if they can't make money at it anymore.

I'm sure plenty of people feel the same way about software. They make software as a hobby and don't care about remuneration or credit. Meanwhile I write software for my day job and losing the ability to make money from it would be devastating.

cjcole• 4 months ago

"but I can't help but see parallels between today and the Industrial Revolution"

You're not the only one.

The current Pope Leo XIV explicitly named himself after the the previous Leo, Pope Leo XIII, who was pope during the Industrial Revolution (1878-1903) and issued the influential Encyclical Rerum novarum (Rights and Duties of Capital and Labor) in response to the upheaval.

“Pope Leo XIII, with the historic Encyclical Rerum novarum, addressed the social question in the context of the first great industrial revolution,” Pope Leo recalled. “Today, the Church offers to all her treasure of social teaching in response to another industrial revolution and the developments of artificial intelligence.” A name, then, not only rooted in tradition, but one that looks firmly ahead to the challenges of a rapidly changing world and the perennial call to protect those most vulnerable within it.”

https://www.vatican.va/content/leo-xiii/en/encyclicals/docum...

https://www.vaticannews.va/en/pope/news/2025-05/pope-leo-xiv...

salawat3• 3 months ago

RERUM NOVARUM

ENCYCLICAL OF POPE LEO XIII ON CAPITAL AND LABOR

Oh hohm. Such a great mouthpiece Pope Leo XIII was for extolling and providing cover for the excesses of the worst breed of capitalist. Whilst my experience with such religious writing is used to coming away from them not wholely satisfied one way or another, this particular piece was heavily biased toward the "Captain's of Industry" and capitalist civilizations of the time. Explicitly condemning the practices by which labor can usurp the yoke of the unjustly enriched, and no consideration whatsoever against the fact that as capital centralizes, there are fewer and fewer places to actually look for employment that isn't in one way or another unconscionable to the Soul. Which, thankfully, the fellow at least had the decency to recognize that one isn't at, nor should be at liberty to give one's soul up because the only people signing cheques are those that are most conditioned to being in service not to anyone else, but merely to themselves.

For instance, it places the burden of the yoke of thrift equally on all men, without recognizing that that yoke provides exactly the spiritual cover required for the pernicious greedmonger to sleep soundly after condemning thousands to a situation where in their self-preservation is not guaranteed.

I see some mild concessions to the working class, which we have plenty of history from which to reason that even with a Papal acknowledgement, these words did not suffice to tilt the behavior of men away from ungodly and abusive treatment of their fellow men until such time as they were confronted with force. That the Pope of all people then doubled down by pointing out that "agitators would arise to foment violence and revolt" without taking into consideration that is the only language left that will be understood by the man whose heart has sufficiently hardened to enable him to with a stroke of a pen condemn thousands to millions, nay billions at a time to suffering. To usurp their livelihoods as his own to be rented back, ensuring that no ownership is onto his potential competitors conferred upon which could be built the means to diminish his own prosperity.

No... Pope Leo XIII, your encyclical is in places valid, but woefully out of date and in need of a massive update. Even in it's time, that wording would have been fairly what we today call "milquetoast" in terms of providing the necessary spiritual force to temper the excesses of man's vices. Our current day, is evidence enough of that. Where instead of true virtue and the ability of all to live prosperously, we have a divided class of those seeking desperately to get by, and those seeking desperately to ensure no one gets by them.

Whilst I'm not Catholic, I do do tend to honor the tradition of firmly worded letters nailed to their doors to keep them honest. This encyclical in it's time may have seemed fine, but with hindsight reeks of inadequacy and hedging, with excessive pandering to the already wealthy. This alone explains to me greatly why the labor movements of the late 1800's and early 1900's were not only as bad as they were, but absolutely necessary. If Leo XIV can't do any better than this, then it may once again come to bloodshed. The feedback loop is much tighter, and news travels much faster. Likely why the wealthy are doing everything they can to weight media outlets in their favor, and destroy any unregulated medium of anonymous communication. For these men are greedy, but not stupid. They know deep down the Lord dost tolerate the machinations of the Devil to test the tendencies of humankind, and they fear the inevitable outcome that will arise when the rest of mankind through privation is forced to harden their hearts as they (the wealthy) have. For in the eye's of one who has only their Soul left to bargain with, laid bare is the banal veil of Evil, and if one is to meet their Maker earlier than planned as a result of another man's artifice... Well. Justice doth favor action, whereas the banal finds fertile sustenance in the inaction of vacuous platitude debated endlessly.

Perhaps I am one of the Agitators of which the Pope spoke. Yet I feel no pause at any of the words I have hitherto written. So do with them what you will. If what we have to live with is supposed to be fine, I do not agree that anything about it is what one could conscionably call just.

konschubert• 4 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant.

The opposite is true. Central Europe was almost devoid of trees. Food was scarce as arable land bore little fruit without fertiliser.

Society was Malthusian until the Industrial Revolution.

jsmo• 4 months ago

Can we interpret "abundant" in a Darwinian sense e.g. diversity of life? I would think the industrial farming revolution decreased crop variety over time same for animal lineages aside from the rapid increase in mixed poodle breeds.

jltsiren• 4 months ago

Not really Malthusian. Agricultural societies had adapted to keep the population stable during normal times and bounce back in a generation or two after bad times. Those cultural adaptations stopped working when childhood mortality declined.

Useful land was a scarce resource in more civilized regions, while labor was cheap. Given enough land, subsistence farmers could easily feed themselves outside particularly bad years. But much of the land belonged to local elites, and commoners had to work that land to fund the pursuits of the elites.

aerhardt• 4 months ago

To add, I don’t think my ancestor Spaniards for example needed the help of machines to deplete mines in America. They also came already equipped with all kinds of legal systems, including the Requerimiento, which they read out loud to natives in preposterous spectacle.

In general the transition from feudalism to capitalism, including the formation of the legal systems that supported the latter, happened gradually for maybe up to four or five centuries before the steam engine had been invented.

Sure, the Industrial Revolution further accelerated the development of property rights, mercantile, and civil laws, but all in all I don’t think there’s much truth that machines were the primary cause of such developments.

pocksuppet3• 4 months ago

Stuff gets put online when the reader isn't the customer. Someone is paying for a reader to be told certain things. So it's free at the point of reading.

monocasa• 4 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant. We simply weren't efficient enough to fully exploit it. That meant that it was fine for things like property and the commons to be poorly defined. If all of us can go hunting in the woods and yet there is still game to be found, then there's no compelling reason to define and litigate who "owns" those woods.

I mean, medieval Europe (speaking broadly) had pretty well defined property rights wrt hunting. In fact, the forester at the time was thought of as one of the most corrupt jobs, as they'd commonly have side hustles poaching and otherwise illegally extracting resources from the lands they enforced and kept others from utilizing in a similar way. Quis custodiet ipsos custodes?

gritspants6• 4 months ago

At what point do we look at 'Industrial Society and its Future' and go from "yeah that'll never happen", "ok some parts of it are happening", to ...? I swear tech folks are the most obtuse people on the planet.

sweezyjeezy2• 4 months ago

I think it's completely normal. Whenever automation comes knocking, people are inclined to think it's going to flatline conveniently before their job is at risk. LLMs can code now? Cool, they can't code well though can they? Oh they can code pretty well now? Cool, coding was never the hard part of SWE anyway, it's [thing we have no reason to think AI can't beat 99% of humans at at some point], etc

I think SWE as a mainstream profession is much nearer to the end than the beginning, I'm curious and quite scared about what becomes of us.

AnthonyMouse2• 4 months ago

We are truly in the Information Age now, and I suspect a similar thing will play out for the digital realm.

The analogy seems to be backwards though. It would be as if we previously had a scarcity of land and because of that divided it up into private property so markets could maximize crop yield etc. and then someone came up with a way to grow food on asteroids using robots, and that food is only at the 20th percentile of quality but it's far cheaper. Suddenly food becomes much more abundant and the people who had been selling the 20th percentile food for $5 are completely out of the market because the new thing can do that for $0.05, and the people providing the 50th percentile food for $10 are also taking a hit because the price difference between what they're providing and the 20th percentile stuff just doubled.

The existing plantation owners then want to put a stop to this somehow, or find a way to tax it, but arguments like this have a problem:

Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?

This was already the status quo as a result of the internet. Newspapers were slowly dying for 20 years before there was ever a ChatGPT, because they had been predicated on the scarcity of printing presses. If you published a story in 1975 it would take 24 hours for relevant competitors to have it in their printed publication and in the meantime it was your exclusive. The customer who wants it today gets it from you. On top of that, there weren't that many competitors covering local news, because how many local outlets are there with a printing press?

Then blogs, Facebook, Reddit and Twitter come and anyone who can set up WordPress can report the news five minutes after you do -- or five hours before, because now everyone has an internet-connected camera in their pocket so the first news of something happening now comes in seconds from whoever happened to be there at the time instead of the next morning after a media company sent a reporter there to cover it.

The biggest problem we have yet to solve from this is how to trust reports from randos. The local paper had a reputation to uphold that you now can't rely on when the first reports are expected to come from people with no previous history of reporting because it's just whoever was there. But that's the same thing AI can't do either -- it's a notorious confabulist.

And it's the media outlets shooting themselves in the foot with this one, because too many of them have gotten far too sloppy in the race to be first or pander to partisans that they're eroding the one advantage they would have been able to keep. Damn fools to erode the public's trust in their ability to get the facts right when it's the one thing people would otherwise still have to get from them in particular.

intended7• 3 months ago

This assumes the limiting factor is content generation, not ability to read and verify.

You make the point later in your comment, but consider it a minor issue. “Randos”

the actual limits are verification, and then attention. Verification is always more expensive than generation.

However, people are happy to consume unverified content which suits their needs. This is why you always needed to subsidize newspapers with ads or classifieds.

nick32661123• 4 months ago

Our only hope is that AI in the long run is both powerful and benevolent enough to be its own "whistleblower" in cases of misuse.

irishcoffee• 3 months ago

I struggle so hard with this anthropomorphism of LLMs. At the end of the day it's a statistical gradient descent predictor with a bunch of "shit" bolted on top to try and steer outputs in a specific way.

They don't have the actual concept of "benevolent"... or a concept of anything at all. Based on an input, they regress down a path of "what is the next most probable statistical token to output next" and that's fucking it, with the bolted-on shit manipulating these outputs a bit.

I don't doubt that at some point there will be some other AI leap, but I'm not even sure it'll be built on this foundation.

What really needs to be developed is an actual artificial brain of sorts. Much like an infant learns language from first principals, a real AI would have a phase of continuous growth, creating actual memories and being able to reflect upon them. I daresay context windows are not that.

I'd really like to encourage anyone to pump the brakes a bit on how these things actually work, and what they actually are. There is a reason sama is pivoting away from video, et. al. and into corporate software coding, much like anthropic.

EamonnMR• 4 months ago

We have copyright and intellecual property law already, of course, but those were designed presuming a human might try to profit from the intellectual labor of others. With AI, we're in the industrial era of the digital world. Now a single corporation can train an AI using someone's copyrighted work and in return profit off the knowledge over and over again at industrial scale.

The idea that copyright simply doesn't apply to AI has more to do with AI companies deciding that they're not going to comply with those laws than the design of the laws. Also a very successful lobby against enforcement by positioning AI as a strategic necessity.

randomNumber72• 4 months ago

It's not possible (or at least extremely hard) to prove that the final weights they come up with resulted from copyright infringement.

Thats why they are evaluated so high on the stock market. Basically the will steal all the value of intellectual property in a semi legal way.

trinsic23• 4 months ago

This completely unpends the tenuous balance between creators and consumers. Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article? Who will contribute to the digital common when rapacious AI companies are constantly harvesting it? Why would anyone plant seeds on someone else's farm?

I have been thinking about this. I was pretty amendment a few months ago that AI is going to make a lot of thing worse for everyone because of the externalities of the technology (Data Center Creep, lock in of models, ect) and it probably still will. But then someone suggested to me that I use Claude Code to upgrade my SSG site to the new version because I had been sitting on my ass as the years went by, missing deadline by deadline. I just couldn't put my self into gear to upgrade it. It was massively out of date 10 years plus and I knew it was going to be a nightmare to deal with the problems. I probably was making it more harder than it really was in my head.

So I purchase Claude Code pro and the thing upgrade my site pretty well. There were things it missed because I didn't know the problems existed in the first place until the upgrade was complete, but I had a working updated site in less than an hour. If I had done this myself it would have taking me days/weeks.

So at that point I realized something. Its a tool that can handle good amount of tasks I throw at it as long as I am specific. I think the problem with most people is they expect it to respond like a human. Thats not going to happen, IMHO. Maybe some day it will be more than what it is but right now its just a tool. I don't care what anyone says about AGI and the likes. Its not going to happen with the current iteration (the pattern recognition type) We are going to need more than that if we want to simulate a human brain..

The point is. And I know this is not going to be received very well, mostly because this tech is in the hands of people that are gatekeeping it, is that maybe someday we might reach a point where all of humanities knowledge is put into these things and we can use them to better our lives. Maybe at some point we don't need to hold onto or hoard things as if its the only way we can make a living? And instead we can build things just for the sake of creating it and improve humanity in the process? Obviously the commercial model of these things is not great, that is going to have to be dealt with, but I can see a future where we might be able to fix a lot of humanities problems with this technology as more and more good people put it to use for things that help humanity.

idiotsecant3• 3 months ago

You raised a point and then never answered it. Why would anyone plant seeds on someone else's farm?

delusional3• 4 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant.
We had to invent giant legal systems in order to determine who has the right to do that and who doesn't.

Excuse me? The industrial revolution was like 300 years ago. We had laws before that.

randomNumber72• 4 months ago

Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?

I'm happy to miss all the stuff that was written just for the financial benefit of the author.

Retric7• 4 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant. We simply weren't efficient enough to fully exploit it.

This is just wildly incorrect. People started running out of trees during the early Iron Age. Woodlands have been a managed and often over exploited resource for a long time. Active agriculture vs passive woodlands vs animal grazing has been in constant tension for thousands of years across most of the globe.

Quarrelsome6• 4 months ago

This is just wildly incorrect.

from an global perspective it isn't. Some places sure, like Western Europe, who in some cases had completed enclosure, but remember the new world had only been discovered a few hundred years ago at that point.

Just google maps the north part of South America, even today there are large swathes of undeveloped land across it and back then it was considerably less exploited. At that time it would have appeared infinite, especially to the European industrialists.

felipeerias2• 3 months ago

People had been hunting whales for centuries, but industrialisation gave them the means and the motivation to do so until near extinction.

jtbaker• 3 months ago

the Stepchange show went fairly deep on this topic in their first episode (listened to it recently). https://www.stepchange.show/coal-part-i

hammock• 3 months ago

The general point is accurate, don’t take it so literally.

There were more than enough trees until we developed the technology to clear cut in expeditious manner. There were more than enough fish until we developed the technology to pull massive indiscriminate amounts out of the ocean (and/or started polluting our rivers with industry). There was more than enough topsoil until we developed mechanized plows and artificial fertilizer. Etc.

A few hundred years ago or less, a squirrel could get from the Atlantic Ocean to the Mississippi River without ever touching the ground. Not possible today. That’s not a push and pull played out over thousands of years, that’s a one-way trend.

bryanrasmussen3• 3 months ago

I believe running out of trees was always a local issue - there weren't enough trees where you were at because getting trees had to be gotten locally, you didn't go get trees from far away. So yes that was in constant tension, the thing is that the problem of having enough trees turned from a local problem to a global problem, with the side effects of not having enough trees globally that the world needed to maintain the environment humanity first conquered.

I think the natural world was nearly infinitely abundant is a reasonable description, resource depletion was always local before mass industrialization. Being able to exploit the world as opposed to just your local area is also a mark of efficiency.

thinking_cactus• 3 months ago

I agree. Although in this specific point, I would say we always had depletion (since the most basic microorganisms, after all otherwise life would replicate until it faces depletion limits; all the way to our close primate relatives and throughout human history; food depletes locally which drives competition), but rarely faced degradation or permanent depletion.

I'd say degradation involves a lasting depletion or lasting damage (potentially permanent until restoration efforts happen) to the environment's output and ability to support life. Permanent depletion is what can happen to e.g. shallow mines and fossil fuel deposits.

I think I'd agree the legal system was created mostly for the former, depletion, and only recently had to contend with degradation and permanent depletion. I feel like we still struggle collectively to coming to gripes with permanent depletion.

Permanent depletion is also usually the result of shortsightedness or a competition gone awry. Famous case where nobody wants the ultimate results but people may selfishly march towards it (tragedy of the commons).

cmrdporcupine• 3 months ago

This, and going back further, people literally would brutally massacre neighbouring tribal groupings over control of fishing and hunting and gathering grounds.

The rapid dispersal of our species over literally the entire planet (minus Antarctica) likely also has a lot to do with constantly moving on to new opportunities further away from rivals.

That said, starting in about the 18th century we ran out of new places for that. And intensification truly began.

slibhb• 4 months ago

Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?

In the brave new world we're creating, people will write specifically for AI. If you can impress models so much that they "regurgitate" your work, then your work has achieved a kind of immortality.

xyzzyz2• 4 months ago

Prior to Industrial Revolution, nobody could go hunt in the woods, because the woods were King’s, and poaching King’s game carried death penalty. Situation was similar on the continent: the tiny slivers of remaining wood lands were off limits.

Granted, things were different in the New World, as a result of mass depopulation event following the Columbian exchange. But even there, the megafauna was hunted to extinction soon after the humans first appeared there.

Anyway, the point is that no, prior to Industrial Revolution, the world was of full of scarcity, not abundance.

some_random• 3 months ago

That is straightforwardly not true, land ownership was very well defined and the people who hunted in it without permission were prosecuted.

derangedHorse• 3 months ago

If all of us can go hunting in the woods and yet there is still game to be found, then there's no compelling reason to define and litigate who "owns" those woods.

Property rights don't just protect natural resources, but labor as well. If I cleared out hunting ground in that forest to be the prime spot to catch animals, I would make sure I can use it when I want.

a small number of people were able to completely deplete parts of the earth

A small number of people seems inaccurate when there's typically many more individuals in the pipelines for these technologies.

and in return profit off the knowledge over and over again at industrial scale

Not off just that knowledge, there needed to be a model trained on the data of many others to utilize it.

Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article?

Who's better at writing in this scenario and what are my motivations? If it's ChatGPT and I did it for money, then I would say I should recognize that I can't compete and find something AI can't do. If it's ChatGPT and I write to convey my ideas in an effort to learn regardless of the bestowment of a new perspective on the reader, I'll keep writing.

Why would anyone plant seeds on someone else's farm?

They wouldn't unless it was their own way to attain food and survive. And if it's not the only way, they can defer to those with optimal methods to get it the cheapest they can in the market.

AlexCoventry• 3 months ago

If all of us can go hunting in the woods and yet there is still game to be found, then there's no compelling reason to define and litigate who "owns" those woods.

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

navaed014• 3 months ago

The natural world was not meaningfully abundant… Way before the industrial revolution land which was once used for opening hunting was closed off by the ruling class. Even before the Industrial Revolution you had a new class of merchant and factory owners who earned riches to buy land and keep the poor from hunting on it. Much of the natural resources out of reach for the majority and only accessible by those with deep pockets

noosphr• 3 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant. We simply weren't efficient enough to fully exploit it.

The mammoths disagree.

voidhorse• 3 months ago

I've been looking at things from the same lens since 2023. At the same time, the depletion/hoarding bit isn't new. Companies were already doing this with consumer data, LLMs are just finally the factory moment—now that we have all the raw material we finally have a means of automating production using it.

So, in some ways, I also view LLMs as a pivotal and important wake up call. Companies were already taking the data and using it for a variety of other purposes—it was just way less evident to people when they weren't in direct competition with labor, since, under capital, labor is what we sell.

Either an entire new industry needs to form, or it's finally time to move beyond capitalism. Centralized capital ends up killing itself, because it effectively shuts down its own engine if it kills off consumers, who can only exist in the first place if the wage labor structure holds.

Waterluvian• 3 months ago

Maybe it’s a useless distinction but it feels like we’ve gone through overlapping ages of Communication, Data, Processing, and Information.

First we conquered the ability to move matter and transmit signal, greatly shrinking the world. Next was sensor technology, especially the mobilization of it, and our ability to collect more data than we could ever imagine being able to process. Then we started going crazy with data centres and big data and the idea that maybe we can somehow correlate it all if we just process it enough. And now we’re finally turning data into information, building enormous graphs of correlation without even having to manually reason about a lot of it. Before AI, the hard part was figuring out how to go about finding the signal you needed. Now it’s getting easier at an incredible speed.

kilbey12• 3 months ago

I'm halfway through Foundation on Apple TV and this piece landed hard (you had me at Asimov) because of it. Asimov's whole deal with psychohistory is that you can predict what large populations do even when individuals are unpredictable. Seldon doesn't need anyone to be honest; he needs the math to converge on something real about how people actually behave.

LLMs are sort of the inverse of that. They produce text that looks like the statistical aggregate of human knowledge, but nothing underneath is converging on truth. Seldon's math worked because it modeled actual dynamics. LLMs work because they model plausible text. The "jagged competence frontier" Kingsbury describes: crushing multivariable calculus, failing a word problem, is exactly what you'd get from a system that learned the shape of correct answers without learning what makes them correct.

The part of Foundation that feels prescient right now isn't the predicting-the-future stuff. It's the part where everyone can see Empire is hollowing out and the response is to just...keep going. More spectacle, more confidence, less substance holding any of it up. Hmmm, wait...

idiotsecant3• 3 months ago

how are you enjoying the live action saturday morning cartoon version of Foundation with bonus plucky protagonists?

twobiers• 3 months ago

There is a whole giant essay I probably need to write at some point, but I can't help but see parallels between today and the Industrial Revolution.

You might want to give the following Articles a read then: https://www.ufried.com/blog/ironies_of_ai_1/ https://www.ufried.com/blog/ironies_of_ai_2/

dogcomplex• 3 months ago

This completely unpends the tenuous balance between creators and consumers. Why would a writer put an article online if ChatGPT will slurp it up and regurgitate it back to users without anyone ever even finding the original article? Who will contribute to the digital common when rapacious AI companies are constantly harvesting it? Why would anyone plant seeds on someone else's farm?

This is completely reversed. Why should anyone honour the right of some creator who was merely the first to plant their flag on a creative task that is now absolutely trivial to perform by AI? Who needs a digital commons when creation itself is now the commons and freely accessible for pennies? The seeds plant and grow by themselves now. The only question is who should be allowed to claim the farms?

Answer: No one. AI companies will have their lunch eaten by open source. And if they don't - they should be nationalized and protocolized into free utilities. The entire idea of digital ownership should (and will) be abolished by the very nature of this technology.

The digital world is the new infinitely-abundant nature. We're just returning it to where it should have been, before corporations clawed it into fenced off empires.

ares623• 3 months ago

The silver lining in that scenario is that consumers can "choose" to just go back offline. I put choose in quotes because with so many things in life requiring online accounts nowadays, that choice is tenuous.

raincole• 3 months ago

Prior to the industrial revolution, the natural world was nearly infinitely abundant.

Prior to the industrial revolution, people fight to death for who can use the rivers. Pre-industrial societies are societies of scarcity.

[0]: People have been fighting for water for more than 4000 years: https://en.wikipedia.org/wiki/Umma%E2%80%93Lagash_war

idiotsecant3• 3 months ago

Absolute peak HN energy that the top reply to this very insightful point is a rambling pedantic argument about the finer points of agricultural development.

glitchc• 4 months ago

Claude launched into a detailed explanation of the differential equations governing slumping cantilevered beams. It completely failed to recognize that the snow was entirely supported by the roof, not hanging out over space. No physicist would make this mistake, but LLMs do this sort of thing all the time.

You have to meet some physicist friends of mine then. They are likely to assume that the roof is spherical and frictionless.

CuriouslyC• 4 months ago

To be fair, starting with a toy model to get a first order approximation then building on it is kind of how theoretical science is done.

alexpotato• 4 months ago

I asked if what they had done was ethical—if making deep learning cheaper and more accessible would enable new forms of spam and propaganda.

Someone asked Yuval Noah Harari, author of Sapiens, his thoughts on LLMs and how easy it was to create fake news, ai slop etc.

His response:

"People creating fake stories is nothing new. It's been going on for centuries. Humans have always dealt with it the same way: by creating institutions that they trust to only deliver factual information"

This could be government departments, newspapers, non-profits etc.

A personal note on this:

There is a Christmas card my grandfather made in the 1950s by "photoshopping" (by hand, not the software) images of each member of the family so it looked like they were all miniature versions of themselves standing on various parts of the fireplace. The world didn't collapse due to fake media between the 1950s and today due to people having that ability.

allturtles• 4 months ago

I see this kind of take a lot, and I don't think it's convincing. To me it's similar to saying that the water frame and the power loom won't change anything, because people have been able to make thread and cloth for millenia.

plagiarist• 4 months ago

Individuals with Photoshop making obvious fictions for entertainment is different from funded entities producing clips at scale and passed off as real.

lamasery4• 4 months ago

People keep asking LLMs to explain their own behavior. “Why did you delete that file,” you might ask Claude. Or, “ChatGPT, tell me about your programming.”

Oh man, every business-side person in my company insists on reporting all the way to the UI a "confidence score" that the LLM generates about its own output and I've seen enough to know not to get between an MBA and some metric they've decided they really want even if I'm pretty sure the metric is meaningless nonsense, but... I'm pretty sure those are meaningless nonsense.

dang2• 4 months ago

I hesitate to tamper with an internet master's title, but "The Future of Everything is Lies, I Guess" doesn't really summarize what in fact is a balanced, informed overview which (to me at least) is above the median for one of these thought pieces. Since it's also baity and the HN guidelines ask for such titles to be rewritten, I've taken the license.

In such cases we always try to find a phrase from the article itself which expresses what it's saying in a representative way. (There nearly always is one.) In this case, both the very first and very last sentences do this, and it's interesting that they more or less agree. So I plucked the last sentence and put it above.

Edit: oof, I missed that this is actually the first part of a long series. Not sure what we'll do about the others; I expect some of those will make the frontpage as well.

post-it• 4 months ago

I appreciate the curation you do, dang. I often notice a headline get updated and the result is always a significant improvement.

ACCount375• 4 months ago

Honestly, good call on the title. The original one is far less representative. Far better at clickbait though.

dang2• 4 months ago

Thank you both! I totally missed the sidebar on the OP which explains that this is Part 1 of what will be a long series. Not sure how we'll handle that...

Animats• 4 months ago

Changing the title was a good call.

The article has a good take on the "lie" problem. We know about the hallucination problem, which remains serious. The "lie" problem mentioned is that if you ask an LLM why it said or did something, it has no information of how it got a result. So it processes the "why" as a new query, and produces a plausible explanation. Since that explanation is created without reference to the internals of how the previous query was processed, it may be totally wrong. That seems to be the type of "lie" the author is worried about in this essay.

(Yes, humans do that too.)

dboreham• 4 months ago

I see the penny hasn't dropped yet that: humans are doing (roughly) the same dumb thing these models are doing. Humans are predisposed to not notice that though.

RugnirViking• 3 months ago

All humans do dumb things, of that we have no doubt. But are the dumb things qualitatively the same as those that AIs do? I don't think so. That's essentially the entire problem. We have pretty good ideas about the ways humans make mistakes. Its pretty much the point of all fiction!

AIs fail in new and unpredictable ways. Nobody is saying humans are infallible.

Finally, because I suspect some people are forming tribalism around this, this doesnt to mind my say AI is Good(tm) or Bad (tm). It literally says its going to be weird.

Unearned5161• 4 months ago

Articles like this should approach topics on consciousness with more humility than is displayed here.

We don’t even agree on a good definition of what’s going on inside our own heads yet, what gives you the confidence to say that what goes on inside an LLM can’t be conscious?

ACCount375• 4 months ago

Obviously, the LLMs lack the divine spark, so they can't be conscious. Same as clones, IVF babies, or half of all the twins.

Jest aside, I do agree. If you list out every prominent theory of consciousness, you'd find that about a quarter rules out LLMs, a quarter tentatively rules LLMs in, and what remains is "uncertain about LLMs". And, of course, we don't know which theory of consciousness is correct - or if any of them is.

akomtu• 3 months ago

With LLMs, materialists have got a silicone idol to worship. They believe that they know everything about this idol because they've created it, and at the same time they believe that LLMs have a secret sauce that makes it intelligent. Looking at how they are trying to extract intelligence from it reminds me of alchemists of the past who tried to extract gold from lead.

simianwords41• 4 months ago

Massive increases in training costs and parameter count seem to be yielding diminishing returns. Or maybe this effect is illusory.

But.. that's always been the case? Diminishing returns has always been the name of the game - utility tracks log(training effort). Its not such a big point that he makes it out to be.

simianwords41• 4 months ago

I so far asked few people to make GPT-5.4 thinking to bullshit (with max 4 pages of prompt), no one can find an example.

But the way people speak in general, as well as this post, implies that such a challenge can easily be beaten. If so, I'm not able to find examples.

heijmans• 3 months ago

Here is small example of ChatGPT giving a wrong answer without expressing any doubt (aka bullshitting):

https://chatgpt.com/share/69d78ec7-67b0-8395-9fd1-522b760ab5...

GPT-5.4 Thinking, Pro account.

toraway• 3 months ago

Well done, clear, concise and easily verifiable answer to OP's question and ... no response haha.

I'm also just confused by the implication behind their question, is the idea that GPT-5.4 Thinking has never been confidently wrong, ever, about anything?

jwpapi• 4 months ago

One really should have digested the manifold hypothesis. It’s the most likely explanation of how AI works.

The question is if there are ultradimensional patterns that are the solutions for meaningful problems. I’m saying meaningful, because so far I’ve mainly seen AI solve problems that might be hard, but not really meaningful in a way that somebody solving it would gain a lot of it.

However if these patterns are the fundamental truth of how we solve problems or they are something completely different, we don’t know and this is the 10 Trillion USD question.

I would hope its not the case, as I quite enjoy solving problems. Also my gut feeling tells me it’s just using existing patterns to solve problems that nobody tackled really hard. It also would be nice to know that Humans are unique in that way, but maybe this is the exact same way we are working ? This really goes back to a free will discussion. Yes very interesting.

But just to give an example on what I mean on meaningful problems.

Can an AI start a restaurant and make it work better than a human. (Prompt: "I’m your slave let’s start a restaurant)

Can an AI sign up as copywriter on upwork and make money? (Prompt: "Make money online")

Can an AI without supervision do a scientific breakthrough that has a provable meaningful impact on us. Think about("Help Humanity")

Can an AI manage geopolitics..

These are meaningful problems and different to any coding tasks or olympiad questions. I’m aware that I’m just moving the goalpost.

We really don’t know..

hk__2• 4 months ago

This is silly. LLMs have no special metacognitive capacity.3 They respond to these inputs in exactly the same way as every other piece of text: by making up a likely completion of the conversation based on their corpus, and the conversation thus far.

I don’t see how this is silly, because we kind of work the same way. When you do something instinctively and then someone asks you about it, you review the information you (think you) had at the time and from that you produce an explanation.

joefourier9• 4 months ago

2017’s Attention is All You Need was groundbreaking and paved the way for ChatGPT et al. Since then ML researchers have been trying to come up with new architectures, and companies have thrown gazillions of dollars at smart people to play around and see if they can make a better kind of model. However, these more sophisticated architectures don’t seem to perform as well as Throwing More Parameters At The Problem. Perhaps this is a variant of the Bitter Lesson.

This is not true and unfortunately this significantly reduced the credibility of this article for me. Raw parameter counts stopped increasing almost 5 years ago, and modern models rely on sophisticated architectures like mixture-of-experts, multi-head latent attention, hybrid Mamba/Gated linear attention layers, sparse attention for long context lengths, etc. Training is also vastly more sophisticated.

The Bitter Lesson is misunderstood. It doesn't say "algorithms are pointless, just throw more compute at the problem", it says that general algorithms that scale with more compute are better than algorithms that try to directly encode human understanding. It says nothing about spending time optimising algorithms to scale better for the same compute, and attention algorithms and LLMs in general have significantly advanced beyond "moar parameters" since the time of Attention is All You Need/GPT2/GPT3.

janalsncm• 4 months ago

Yeah I also came here to be one of those People In The Comments the author refers to.

Transformers are not magical. They are just a huge improvement over other architectures at the time such as LSTMs and RNNs and even CNNs. They allowed us to throw more and more compute at the problem of next token prediction. And we’ve been riding that horse ever since.

Another big advancement that deserves mentioning is “reasoning” models that have the opportunity to spit out thinking tokens before giving a final answer.

None of this is to say transformers are the most principled approach. But they work.

zozbot2342• 4 months ago

Transformers' greatest improvement over RNN/LSTM was to enable better parallelization of large-scale training. This is what enabled language models to become "large". But when controlling for overall size, more RNN/LSTM-like approaches seem to be more efficient, as seen e.g. in state space models. The transformer architecture does add some notable capabilities in accounting for long-range dependencies and "needle in a haystack" scenarios, but these are not a silver bullet; they matter in very specific circumstances.

saghm10• 3 months ago

This sounds almost identical to the article that's literally linked at the end of the paragraph that the parent comment quoted: https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...

I don't think anything you're saying here is in disagreement with the points they're making.

kgeist4• 4 months ago

Raw parameter counts stopped increasing almost 5 years ago, and modern models rely on sophisticated architectures like mixture-of-experts, multi-head latent attention, hybrid Mamba/Gated linear attention layers, sparse attention for long context lengths, etc.

Agree, I recently updated our office's little AI server to use Qwen 3.5 instead of Qwen 3 and the capability has considerably increased, even though the new model has fewer parameters (32b => 27b)

Yesterday I spent some time investigating it:

- Gated DeltaNet (invented in 2024 I think) in Qwen3.5 saves memory for the KV kache so we can afford larger quants

- larger quants => more accurate

- I updated the inference engine to have TurboQuant's KV rotations (2026) => 8-bit KV cache is more accurate

- smaller KV cache requirements => larger contexts

Before, Qwen3 on this humble infra could not properly function in OpenCode at all (wrong tool calls, generally dumb, small context), now Qwen 3.5 can solve 90% problems I throw at it.

All that thanks to algorithmic/architectural innovations while actually decreasing the parameter count.

Vachyas• 3 months ago

What you described sounds plausible (expected, even).

But

Raw parameter counts stopped increasing almost 5 years ago

Really? 5 years ago? Until just about 3 years ago OpenAI's latest offering was only ChatGPT 3.5

Most of the models people talk about now didn't even exist 3 years ago let alone 5.

Even now, I don't know if parameter count stopped mattering or just matters less

For example, I have no idea if the new Mythos is MoE but I'm pretty sure it's more parameters.

RoddaWallPro• 4 months ago

5 years ago was the beginning of 2021, just under a year after GPT3 was released (which was not good at doing anything useful). And that model was 175B params.

GPT4 has been widely rumored to have 1.8 trillion params, which is 10x more, and was released 2 years after this "5 years ago" date that you are using here.

So, to quote yourself here, "This is not true and unfortunately this significantly reduced the credibility of this article for me" /s/article/comment

zozbot2342• 4 months ago

MoE has made it vastly easier to increase total parameters (and recent open models are really quite large) but it's also hard to compare a MoE with an earlier dense model.

joefourier9• 4 months ago

In late 2021, GLaM had 1.2T parameters. It's difficult to find much use of it in the wild and while the benchmarks it uses are rather outdated, it has a HellaSwag score of 76.6% and WinoGrande of 73.5%. GPT3 had 64.3% and 70.2%.

Meanwhile, Gemma 2 9B, a model from July 2024 with 133x fewer parameters than GLaM, scores 82% and 80.6%. Hellaswag and WinoGrande aren't used in modern benchmarks, probably because they're too easy and largely memorised at this point.

And GPT-4 had 1.8T parameters sure, but it's noticeably worse than any modern model a fraction of the size, and the original incarnation was ridiculously expensive per token. And in any case, its number of parameters was only possible due using mixture-of-experts, which I would definitely classify as a sophisticated architecture as opposed to just throwing more parameters at a vanilla transformer. Even in 2021 GLaM was a MoE because the limits of scaling dense transformers had already been hit.

jgammell• 3 months ago

hybrid Mamba/Gated linear attention layers,

Do any large-scale architectures use mamba? I was under the impression that people don't use it yet due to lack of efficient implementations.

Training is also vastly more sophisticated

Is it? In what ways?

joefourier9• 3 months ago

Qwen3.5 uses Gated Delta Networks which is essentially Mamba 2 + Delta Rule. It’s quite hardware efficient.

Is it? In what ways?

Just the reinforcement learning for reasoning, and then tool use for agents, could be its own topic.

saghm10• 3 months ago

Literally the paragraph right before the one you quote is this:

I am generally outside the ML field, but I do talk with people in the field. One of the things they tell me is that we don’t really know why transformer models have been so successful, or how to make them better. This is my summary of discussions-over-drinks; take it with many grains of salt. I am certain that People in The Comments will drop a gazillion papers to tell you why this is wrong.

As I understand it, this article is basically a conglomeration of several attempts at an article that the author has attempted to make over the past decade or so considering the impacts of AI on society. In their own words:

Some of these ideas felt prescient in the 2010s and are now obvious. Others may be more novel, or not yet widely-heard. Some predictions will pan out, but others are wild speculation. I hope that regardless of your background or feelings on the current generation of ML systems, you find something interesting to think about.

As for the "Bitter Lesson" part, they pretty much directly said that it wasn't the Bitter Lesson exactly, saying it might be a variant of it. Honestly, it felt more like a way of throwing in a reference to something that also might provoke thought, which was done throughout the piece (which again, is the entire point).

It's totally valid to say "this article didn't provoke much thought for me". I'm a bit confused at why you think a lack of specific domain knowledge in a domain that they literally state they are not an expert in would be disqualifying for that purpose though.

joefourier9• 3 months ago

The title of the article is “The Future of Everything is Lies, I Guess” and the first part is literally complaining about LLMs being bullshit machines, while the author proceeds to tell confabulations (or lies) of his own. Is there not a bit of irony in that?

If you’re a non-expert in a field, I don’t think it’s a good sign if you’re writing a 10 part article about that field’s impact on society and getting basic facts wrong. How can I trust that the conclusions will be any more credible?

imtringued5• 3 months ago

He is wrong about why transformers are popular.

The most obvious reason is that transformers accept a sequence as an input and produce a sequence as an output. The vast majority of pre-transformer architectures only accepted a fixed input and output size. Before 2016 I was somewhat interested in ML, but my curiosity vanished because of the fixed input and output size limitations.

RNNs including LSTMs at the time were pretty bad and difficult to train due to vanishing and exploding gradients at long sequence lengths and sequential training along the sequence length. Meanwhile transformers can be parallelized along the sequence length.

Then there are theoretical limitations. Transformers re-read the entire sequence for every output. This leads to quadratic attention. There are plenty of papers that tell you why it is impossible to replicate the properties of quadratic attention with linear attention.

The reason is blatantly obvious. If you want linear attention to have the same capability, you need to re-read the entire input sequence after every output. If you do this at the token level, then you have basically implemented quadratic attention.

Transformers aren't a mystery success, they are using computational brute force, which is hard to beat with other architectures. If you go with a more efficient architecture, you are by definition giving up some non-zero capabilities. Nobody really cares about getting slightly worse results from a much more efficient architecture. In the current ML space, it's SOTA (state of the art) or go home.

roughly• 4 months ago

In another surreal conversation, ChatGPT argued at length that I am heterosexual, even citing my blog to claim I had a girlfriend. I am, of course, gay as hell, and no girlfriend was mentioned in the post. After a while, we compromised on me being bisexual.

This is a bit of a throwaway in the article, but when people talk about biases encoded in the algorithms, this is what they’re talking about.

yumiatlead• 4 months ago

The Industrial Revolution parallel holds up to a point. What it misses: the first industrial revolution required physical coordination — workers, factories, supply chains. The AI revolution requires organizational coordination. Who decides what the agent does, for whom, with whose authority? That governance layer doesn't exist yet, and it's not much a legal question but also an infrastructure question.

jijji• 3 months ago

the authors reference to LLM's as "bullshit machines" is more true the less parameters you have trained in your model....as we scale up to trillions of parameters, add Mixture of Experts (MoE) architecture, this no longer is an accurate statement. Proof in point was yesterdays announcemnt of Mythos 5 model (10T parameters + MoE [1]) by anthropic where it seems to be so good at finding/exploiting vulnerabilities in source code that have been there for decades and only recently uncovered needs to be used to fix these critical vilnerabilities first before it gets released to the public, they even have a project called Glasswing [2] dedicated to letting people fix the thousands of vulnerabilities already found by the model before they release this model to the public, because it's so good at what it does... I think we're a little bit past the point of calling these models "bullshit machines" at this point...

[1] https://www.aimagicx.com/blog/claude-mythos-5-trillion-param...

[2] https://www.anthropic.com/glasswing

orthoxerox3• 3 months ago

Well, Anthropic should let Aphyr try Mythos 5 for his Jepsen business, then.

mxfh• 3 months ago

Even if nothing substantial come out of this, having shortest paths to the corpus of all human expressions in all languages! and media formats is quite something by itself, what could be the ultimate hard information retrieval tool is hiding those trace behind untracked convolutions is the real shame here. Found so much real information in between less and less hallucinations already that was impossible to retrieve otherwise in that time frame. Basically tokenrank kills pagerank.

ajkjk• 3 months ago

I wish the original title was kept here. People ought to be able to give their essays poetic titles.

samarth0211• 3 months ago

The weirdness of ML is honestly one of the most fascinating things about it. The fact that emergent behaviors keep surprising even the people building these systems suggests we're in for a genuinely novel scientific era. Great read!

johnnienaked• 3 months ago

Amazing to see a lot of the comments stating the exact qualifiers he laid out as potential counterarguments to his writeup. Did they even read it?

7sigma• 3 months ago

https://archive.ph/I5cAE

for those in the UK

xylon• 3 months ago

This page won't load for me "Unavailable Due to the UK Online Safety Act". Why would this law be relevant to a blog-post about AI?

jmcgough• 3 months ago

Aphyr is unabashedly gay - I remember him repeatedly posting hardcore pornography on twitter at one point to remind people of this - so he probably just decided to block UK visitors rather than risk violating the law by having minors visit his site. Not sure if he has anything actually "harmful" on his site, but maybe he doesn't want his speech limited or need to aggressively police comments?

McP• 3 months ago

Same for me except I'm currently in France.

Can read the article at https://web.archive.org/web/20260409111708/https://aphyr.com...

tempodox• 3 months ago

Why was the title editorialized? The post started with the original title.

htrp3• 3 months ago

One can envision a world in which OpenAI pays chefs money to cook while ChatGPT watches—narrating their thought process, tasting the dishes, and describing the results. This information could be used for general-purpose training, but it might also be packaged as a “book”, “course”, or “partner” someone could ask for.

So we're speed running the idea of AI Facebook friends and creating a new para(ai)social relationship

munksbeer• 3 months ago

Nice, can't view it.

"Unavailable Due to the UK Online Safety Act"

tim3333• 3 months ago

Re profoundly weird, the "losing hundreds of thousands of dollars because they can’t do basic math" story is funny.

Guy set up an openclaw called Lobstar Wilde and gave it US$50k in SOL to do what it wanted with. Someone else set up a memecoin called $LOBSTAR and gave 5% of the supply to Lobstar Wilde. Someone wrote to Lobstar with a sob story asking for 4 SOL but Lobstar due to a miscalculation sent tokens then valued at $450k but I think some came back due to tokens going up and down.

His wallet, which had held $50,000 three days ago, held over $300,000 now, after he had given away $400,000 by accident. (https://substack.com/home/post/p-188846616)

Not sure what the current state of its wallet is. Lobstar keeps tweeting philosophically (Most people do not love or hate the thing itself. They love or hate the feeling the thing produces in them, and then they mistake that feeling for knowledge of the thing...) and its owner works on Codex at OpenAI.

data_maan• 3 months ago

If LLMs lie as much as the OP claims in the article, why can they then solve Olympiad math problems they never saw during training, consistently?

There's the aimoprize.com on Kaggle for example that shows this

ivraatiems• 3 months ago

Because those two things are unrelated.

First, something lying sometimes doesn't mean it lies all the time.

Second, the whole point of LLMs is inference - they use massive amounts of amalgamated information to produce answers. The Olympiad math problems are not frontier mathematics requiring ideation, they are complex examples of existing problems. That means they're exactly the sort of thing an LLM with enough training data is good at.

The question of whether recombining existing knowledge is all it takes to be "creative" or produce things which are novel is an open one, but I don't think this is contradictory on its face.

ML promises to be profoundly weird

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