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.

> 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.

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

> 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.

> 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.

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?

> 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?

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): that's just how intelligence works.

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.

> 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.

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

> 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).

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

Is it something we want to emulate?

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.

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

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

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...

> 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.

Yes see Karl Frisstons Free energy principle

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

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.

> 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.

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.

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.

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.

> 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.

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.

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

Its usual gibberish that tries to throw many darts and see what sticks. Oh LLM's steal other people's work? Check. Oh LLM's cause ecological damage? Check. Oh LLM's hallucinate? Check.

When you see a pattern like this, you know that its not coming from any place of truth but rather ideology

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

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.

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?

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

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."

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.

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.

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.

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

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.

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

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.

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

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...

> 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!

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

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?

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.

> 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)