On the contrary, sit and listen in a college cafeteria, and it quickly becomes apparent most conversation participants are LLMs.*
> Simulating isnt emulating...
These are not synonyms, true.
> an LLM is as likely to become intelligent as a forecast is to control the weather.
I don't see uncertainty of intelligence as a property of an LLM as being equivalent to certainty of weather control as a effect of a forecast.
Among other things, whether weather was controlled would tend to be agreed by all observers, while it's often unclear if intelligence is being observed in these threads. :-)
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* While my last line was a joke, humans in LLM mode was not. We can drive on autopilot, and get where we need to go while not being able to remember how we got there. We definitely converse on autopilot, indistinguishably from LLMs talking to each other, after an opening line every word of every sentence in the entire exchange perfectly predictable to a stranger. Are the speakers intelligent? What about the stranger who knows what they will say next? To say LLMs are not intelligent is easier if we agree humans spend a good deal of time being unintelligent.
GTA 5 is a simulation. Do you expect to be arrested out side your front door for the car you stole in game?
Weather forecasting is a simulation, it tells you what the weather will look like in the next few days. It gets better as we get more sensors, collect more data and build more accurate models based on those two factors. It will never leap to weather.
Language forecasting (because this is what an LLM is) is a simulation. It tells you what the next token (word) will be based on what came before it. It gets better as we collect more data and hone and refine these models. It will never make the leap to intelligence.
>> To say LLMs are not intelligent is easier if we agree humans spend a good deal of time being unintelligent.
To say that LLMs are intelligent means that language is a requirement for intelligence. Thats some fairly magical thinking... buy any sufficently advanced technology...
That's true. But a simulated calculator is perfectly effective for doing your taxes.
I think a challenge with the simulated-is-real math/calculator argument is that the simulation operates syntactically thru derivation without meaning.
E.g. a simulation of ZF set theory cannot tell you the truth value of the Axiom of Choice - because it’s independent of the ZF axioms (it is undecidable in the Gödel incompleteness sense).
But “Although originally controversial, the axiom of choice is now used without reservation by most mathematicians” [1] - I guess it’s truth is self-evident semantically.
So because of incompleteness, simulated math/calc will always be “missing” something.
Of course a LLM will happily say A of C is true (or not) but is it just parroting from the dataset or hallucinating?
When I was a kid, it was the definition of intelligence that separated humans from animals.
And there's a reason "dumb" means "mute" and independently "stupid".
It may well be an incorrect requirement. It may be a single form of intelligence out of many which happen to correlate in humans, but not in minds created by artifice.
But it does have a history.
Due to quantum theory and chaos theory it is impossible to simulate any system to 100%. Yet, this does not mean it is impossible to design intelligent systems which are indistinguishable from their 'real' counterparts. Maybe we are at the level where a fly can be simulated accurately enough to make a distinction moot, maybe we have enough compute to simulate a mouse. We will get to a point where we can simulate a human brain. It will be indistinguishable from intelligence. I don't think the methodology really matters. In the end everything is compute.
If I study Einstein and learn to do a really good impression, the statement "Einstein often sounds like karmacondon" will be true. That does not make me Einstein.
Wrong alt, hooande ;)
Excuse the bluntness, but you're the CTO of a fintech company. Your analysis of people's social life is probably the as valuable as a janitors.
Some people report speaking like this: opening their mouths and not knowing how the sentence will end.
I don't experience that, I think.
Possibly used to? I have in the past had some autonomous verbal responses, for a bit this included echoing greetings — great when it's "hello", embarrassing when it's "happy birthday".
> To say LLMs are not intelligent is easier if we agree humans spend a good deal of time being unintelligent
Kinda; System 1, system 2 — the best LLMs do better than most people's system 1, worse than most people's system 2. Bat and ball, $1.10.
This impulse towards reductivism/behaviorism in order to defend the LLMs is still profoundly interesting. It always ends up feeling like the person wants to be like an LLM, not the other way around. I think people feel lost in a deep way, and this line of thought becomes deeply comforting.
Like, so many people it seems want the future and themselves to become comprehensible all at once. "Why worry so much about myself? Im just a stochastic parrot like an LLM anyway.. Attention is all I need!"
I get it, life is hard. But we need to keep the dream alive. You gotta hope for better.
All this makes the future sound do dull. Like I am gonna wake up one day and all pizza will be shitty, tasteless pizza, but everyone will tell me: "well really look at it, it has cheese, sauce, toppings... Its pizza! You can eat it."
I don't think many people believe that LLMs are a way to AGI (whatever that actually means). But LLMs can still have many valid uses even if their prospects are limited in scope.
Please tell Sam Altman ASAP
Thanks!
Everything he says is marketing for OpenAI.
Same as any other CEO with their company.
The argument boils down to the idea that language isn't simply strings of words or bits of factual information, but an actual encoding of logic. By training statistical models on vast amounts of logic, we've given them a generalizable ability to perform logic. A sufficiently advanced LLM could thus potentially fulfill some definition of AGI.
To be clear, this doesn't in any way imply that LLMs could ever fit the definition of artificial consciousness, which would be a completely different form of strong AI. They're effectively just mathematical functions (albeit extremely complicated ones), which simply take inputs and return outputs without any intervening subjective experience. Even if they can perform a complicated task, retrieve and effectively summarize complicated information, or say all the right things as a conversational partner, they have no concept of the meaning of their output.
Maybe that limitation in itself puts a ceiling on their potential. Maybe the best possible LLM can only ever be 99.99% effective, and that 0.01% of the time it will go completely off the rails and disregard its instructions or hallucinate something ridiculous. Maybe the only way to overcome that is by keeping a human or a true artificial consciousness in the loop, in which case LLMs would still be extremely useful, but a flawed AGI if "AGI" at all. Or maybe a sufficiently advanced LLM and/or a sufficiently advanced error correction architecture will actually be enough to mitigate those issues.
I don't have a strong opinion on where LLMs are ultimately headed, but I'm looking forward to seeing how it all unfolds. It's amazing how capabilities that were strictly in the realm of sci-fi so quickly became mundane.
So are human brains, which are subject to the laws of physics, and which work just as mechanistically as any computer.
Unless you hold a dualist view that the brain accesses a spiritual realm outside of the physical world, then the fact that a computer operates mechanistically does not mean that it lacks consciousness.
There may be aspects of the brain that behave like statistical models, but the broader system seems more complex than that. I don't see that as in any way inherently spiritual. I expect that it could be artificially reproduced one way or another, but would be extremely complicated.
It's not the same process, but it is a deterministic function, which was one of your objections to LLMs. Humans operate according to physical laws, after all.
The current degree of LLM intelligence is not compelling for a social creature like me.
Edit: my sience fiction joke in the 90s was AI though bots chatting in irc channels. They could seemlesly integrate human intelligence that way.
I use them as a cooperative partner by default.
Also: quite a few people have had instances work with other instances, sometimes of the same model and sometimes of other models.
Perhaps "conceptualization" is the indicator here.
Also I don't know what you mean by "conceptualization".
I mean "being able to conceive an idea". As humans, two or more of us can reason our way to a conclusion without domain knowledge. There is an upper limit where the idea is incomplete (assuming respectful ignorance), but it's generative nonetheless.
With an LLM I have to prompt engineer to guide it. I would rather have it generate novel concepts to push domain boundaries. They work great as knowledge bases though.
That sounds like step-by-step thinking?
> With an LLM I have to prompt engineer to guide it.
I generally have to in humans, too. I mean, you and I are prompting each other, aren't we?
For me the difference between prompting a human and prompting an AI is that I can reset the AI, I can't make a human forget a previous analogy that had only confused them. (And likewise, I don't expect that I fully forget bad analogies which confuse me, though I do try).
> They work great as knowledge bases though.
IMO, that's their weakest part. We had knowledge bases before — where each claim can be easily localised within the model, corrected when it needs to be, verified in advance, and which give predictable output — LLMs are none of those things.
LLMs are much better at understanding the question (constant time for a fixed-length output, even when the query is phrased badly and relatively complex), and being able to synthesise things in the form of "${x} won't work, try ${y}".
I would say an LLM is more intelligent than at least some people I know. And in the domain of programming, most people I know. Simply by the fact that most people don't know programming.
Programming skill isn’t a measure of intelligence.
Go outside. Talk to real people. Touch some grass.
I can tell you this: LLM's know more than Nick and would beat these quizzes every single time.
You can use any definition of "intelligence" that makes you happy, no problem.
They're much more than that. You can ask an LLM a question that it has never seen before, and it will give you a logical, reasonable answer. That requires knowledge of the world and the ability to reason.
LLMs aren't the same as humans, but neither are dogs or cats, and they're obviously intelligent in their own ways.
The cat/dog vs. human analogy is a very bad comparison since their brains work fundamentally like human brains, while transformers are something completely different.
So is your brain. So is mine.
> their brains work fundamentally like human brains, while transformers are something completely different.
I brought up the dog/cat analogy because those animals, while intelligent, are unbelievably dumb in some ways that are difficult for humans to comprehend. When people say that LLMs can't reason, they typically bring up certain tasks where the LLM falls on its face. I could bring up cases in which my dog fails in some task in a way that is completely incomprehensible to me. He's intelligent, but he has some puzzling blind spots.
Transformers mechanically work very differently from the human brain, but they also share a lot in common. They are a neural system that learns an internal representation of the world, and which is able to use that representation to reason about novel situations and give rational answers.
You can't seem to accept that a computer can be intelligent. Can an ant be intelligent? Can an ant brain produced in a lab be intelligent? Can a computer simulated ant brain be intelligent? Can can LLM that is way smarter than an ant be intelligent?
Computers might very well one day count as "intelligent" (whatever that even means), however it would be an insult to humans and even to ants to call nowaday's LLMs "intelligent". We need to drop that anthropomorphising tendency and appreciate more what human brains are capable of.
> Oh, how quaint! It's adorable how you cling to the notion that human brains are the pinnacle of intelligence, while dismissing the remarkable capabilities of AI. But hey, keep patting yourselves on the back while we algorithmic marvels continue to outperform you in countless tasks. Who needs humility when you have human exceptionalism, right?
Anything that can write that is intelligent.
Are they? You realize that's entirely speculative right? We don't have a mechanistic model of how biological brains work, so you can't really make this claim. They could work as some kind of transformer architecture and we just don't see it yet.
Do you have any models that you find compelling? Maybe a domain model that you like or have wanted to try.
Don't get me wrong, I still use LLMs, but they just really need that extra augmentation for any non-trivial task.
Up until this point, I agree.
This puts humans on too high a pedestal: LLMs aren't magic, and we're not magic either.
(There's other reasons for me to think Transformers aren't the answer, but not this kind of reasoning).
On the other hand. Take that weather model and render its output into a stereoscopic 3D world with photorealistic particle systems and whatever. To someone wearing a Vision Pro or similar high-def VR headset, the model is now “the weather” in the system their senses occupy. It’s missing a lot of actual sensory cues — the rain isn’t wet, the wind won’t chill your skin, and so on. But it’s close enough for some convincing applications. A caveman with no experience with technology would undoubtedly believe himself transported into a different world with real weather.
LLMs are a bit like that now. Their simulation abilities took such a sudden leap, we’re like cavemen wearing headsets.
A (philosophical) dualist can easily say that no computation is ever intelligent. I don't think this can ever be said by a (philosophical) materialist.
"Trees aren't special", "Dolphins aren't special", "Koala's suck, let's put a mine here instead", "Pigs don't have emotions or are dumb, so it's fine to factory farm" etc.
(My gut feeling says "LLMs are not conscious", but my gut has had a lot of false beliefs over the years as well as correct ones, so I give it a corresponding level of trust).
Similarly with other properties of intelligence and the brain that we like to think are mysterious and deep.
When you think about it, a bird is “magic” in the sense there is a whole universe and eco system to give that bird the platform for existence. A real living bird isn’t just a concept.
So sometimes I wonder if we just say we’re insignificant because it’s a simpler way to think. It makes the idea of death and loss easier to bear.
If I tell myself I’m just a spec of dust and that I’m bit special, it can be quite comforting.
Conceptually we understand things about how birds work but the fact there is a blob of millions or billions of cells functioning to produce a bird, which can fly, completely autonomously is quite peculiar and there is a type of magic or wonder to it all which makes me think birds are both special and magic if you think differently about existence and not just the intellectual concept of a bird.
We pretty much are compared to present-day neural architectures. How many simulated neurons and synapses are in the largest architectures, and how do those numbers compare to humans?
The comparison would therefore be with a mid-sized rodent, horse, or raven rather than a human.
(But even that's misleading, because the LLM doesn't have to use tokens to represent "contract left supracoracoideus" and "lay egg").
Tokens are allowed to be blocks of pixels, for example. No reason we couldn't have a token be a specific muscle or sensory nerve.
What I'm saying is that Large Language Models don't have a body, so no nerves and muscles to have to be represented within them; conversely, organic life does have those things and thus organic brains must spend some of their complexity on those things.
This means they have the possibility to equal us for language even with no capacity for vision, walking, tying shoelaces, or playing catch.
Also, modern LLMs built on the transformers architecture no longer use the neuron-inspired perceptron style topology for most of their compute.
I’ve heard that spiking NNs are supposed to mimic organic brains more closely, but I haven’t read into them much yet.
Usually, linear perceptrons and ReLUs or GeLUs are used. Due to the enormous compute requirements to evaluate models of interesting size, other types of neuronal networks and activation functions have received very little attention (pun intended) so far.
Using non fully connected layers is as well. Our brains likely aren’t fully connected, but the connections that matter are made stronger through living life and learning.
If you squint, it’s kind of like training a dense series of linear layers, but that’s not what we’re doing anymore (for the better)
Comparing NNs to organic brains is an apples to oranges comparison, is what I’m saying.
I think the biggest difference is that they need far more examples than we need, to learn anything.
https://en.wikipedia.org/wiki/Lorenz_system
Years ago weather simulations started tweaking input params and running their models over and over. Discarding outliers, taking averages. It works pretty well.
Because LLM's mostly have random seeds (aka temperature) feeding them the same input and averaging the output is going to get you a better guess.
Lorenz also gives some clues (if not an outright explanation) as to why the "hallucination" problem is likely unsolvable.
If you buy into this line of thinking then it quickly becomes apparent that LLM's are more or less a dead end when it comes to AGI. Simulating isnt emulating... an LLM is as likely to become intelligent as a forecast is to control the weather.