[Bjorkbat]

It's a smart purchase, it's just that I don't see how these datasets factor into super-intelligence. I don't think you can create a super-intelligent AI with more human data, even if it's high-quality data from paid human contributors.

Unless we watered-down the definition of super-intelligent AI. To me, super-intelligence means an AI that has an intelligence that dwarfs anything theoretically possible from a human mind. Borderline God-like. I've noticed that some people have referred to super-intelligent AI as simply AI that's about as intelligent as Albert Einstein in effectively all domains. In the latter case, maybe you could get there with a lot of very, very good data, but it's also still a leap of imagination for me.

[Fraterkes]

I think this is kind of a philosphical distinction to a lot of people: the assumption is that a computer that can reason like a smart person but still runs at the speed of a computer would appear superintelligent to us. Speed is already the way we distinguish supercomputers from normal ones.

[psb217]

I'd say superintelligence is more about producing deeper insight, making more abstract links across domains, and advancing the frontiers of knowledge than about doing stuff faster. Thinking speed correlates with intelligence to some extent, but at the higher end the distinction between speed and quality becomes clear.

[zozbot234]

If anything, "abstract links across domains" is the one area where even very low intelligence AI's will still have an edge, simply because any AI trained on general text has "learned" a whole lot of random knowledge about lots of different domains; more than any human could easily acquire. But again, this is true of AI's no matter how "smart" they are. Not related to any "super intelligence" specifically.

Similarly, "deeper insight" may be surfaced occasionally simply by making a low-intelligence AI 'think' for longer, but this is not something you can count on under any circumstances, which is what you may well expect from something that's claimed to be "super intelligent".

[psb217]

I don't think current models are capable of making abstract links across domains. They can latch onto superficial similarities, but I have yet to see an instance of a model making an unexpected and useful analogy. It's a high bar, but I think that's fair for declaring superintelligence.

In general, I agree that these models are in some sense extremely knowledgeable, which suggests they are ripe for producing productive analogies if only we can figure out what they're missing compared to human-style thinking. Part of what makes it difficult to evaluate the abilities of these models is that they are wildly superhuman in some ways and quite dumb in others.

[rxtexit]

I think they can make abstract links across domains.

Like the prompt "How can a simplicial complex be used in the creation of black metal guitar music?" https://chatgpt.com/share/684d52c0-bffc-8004-84ac-95d55f7bdc...

It is really more of a value judgement of the utility of the answer to a human.

Some kind of automated discovery across all domain pairs for something that a human finds utility in the answer seems almost like the definition of an intractable problem.

Superintelligence just seems like marketing to me in this context. As if AGI is so 2024.

[zozbot234]

> It's a high bar, but I think that's fair for declaring superintelligence.

I have to disagree because the distinction between "superficial similarities" and genuinely "useful" analogies is pretty clearly one of degree. Spend enough time and effort asking even a low-intelligence AI about "dumb" similarities, and it'll eventually hit a new and perhaps "useful" analogy simply as a matter of luck. This becomes even easier if you can provide the AI with a lot of "context" input, which is something that models have been improving at. But either way it's not superintelligent or superhuman, just part of the general 'wild' weirdness of AI's as a whole.

[psb217]

I think you misunderstood what I meant about setting a high bar. First, passing the bar is a necessary but not sufficient condition for superintelligence. Secondly, by "fair for" I meant it's fair to set a high bar, not that this particular bar is the one fair bar for measuring intelligence. It's obvious that usefulness of an analogy generator is a matter of degree. Eg, a uniform random string generator is guaranteed to produce all possible insightful analogies, but would not be considered useful or intelligent.

I think you're basically agreeing with me. Ie, current models are not superintelligent. Even though they can "think" super fast, they don't pass a minimum bar of producing novel and useful connections between domains without significant human intervention. And, our evaluation of their abilities is clouded by the way in which their intelligence differs from our own.

[CamperBob2]

I don't know about "useful" but this answer from o3-pro was nicely-inspired, I thought: https://chatgpt.com/share/684c805d-ef08-800b-b725-970561aaf5...

I wonder if the comparison is actually original.

[psb217]

Comparing the process of research to tending a garden or raising children is fairly common. This is an iteration on that theme. One thing I find interesting about this analogy is that there's a strong sense of the model's autoregressiveness here in that the model commits early to the gardening analogy and then finds a way to make it work (more or less).

The sorts of useful analogies I was mostly talking about are those that appear in scientific research involving actionable technical details. Eg, diffusion models came about when folks with a background in statistical physics saw some connections between the math for variational autoencoders and the math for non-equilibrium thermodynamics. Guided by this connection, they decided to train models to generate data by learning to invert a diffusion process that gradually transforms complexly structured data into a much simpler distribution -- in this case, a basic multidimensional Gaussian.

I feel like these sorts of technical analogies are harder to stumble on than more common "linguistic" analogies. The latter can be useful tools for thinking, but tend to require some post-hoc interpretation and hand waving before they produce any actionable insight. The former are more direct bridges between domains that allow direct transfer of knowledge about one class of problems to another.

[edmundsauto]

My POV, speed + good evaluation are all you need. Infinite monkeys and Shakespeare.

[echelon]

> It's a smart purchase, it's just that I don't see how these datasets factor into super-intelligence.

It's a smart purchase for the data, and it's a roadblock for the other AI hyperscalers. Meta gets Scale's leading datasets and gets to lock out the other players from purchasing it. It slows down OpenAI, Anthropic, et al.

These are just good chess moves. The "super-intelligence" bit is just hype/spin for the journalists and layperson investors.

[Bjorkbat]

> These are just good chess moves. The "super-intelligence" bit is just hype/spin for the journalists and layperson investors.

Which is kind of what I figured, but I was curious if anyone disagreed.

[ryanblakeley]

Super-intelligent game-playing AIs, for decades, were trained on human data.

[zozbot234]

I'll believe that AI is anywhere near as smart as Albert Einstein in any domain whatsoever (let alone science-heavy ones, where the tiniest details can be critical to any assessment) when it stops making stuff up with the slightest provocation. Current 'AI' is nothing more than a toy, and treating it as super smart or "super intelligent" may even be outright dangerous. I'm way more comfortable with the "stochastic parrot" framing, since we all know that parrots shouldn't always be taken seriously.

[Applejinx]

Earlier today in a conversation about how AI ads all look the same, I described them as 'clouds of usually' and 'a stale aftertaste of many various things that weren't special'.

If you have a cloud of usually, there may be perfectly valid things to do with it: study it, use it for low-value normal tasks, make a web page or follow a recipe. Mundane ordinary things not worth fussing over.

This is not a path to Einstein. It's more relevant to ask whether it will have deleterious effects on users to have a compliant slave at their disposal, one that is not too bright but savvy about many menial tasks. This might be bad for people to get used to, and in that light the concerns about ethical treatment of AIs are salient.

[xpe]

> I'm way more comfortable with the "stochastic parrot" framing, since we all know that parrots shouldn't always be taken seriously.

First, comfort isn't a great gauge for truth.

Second, many of us have seen this metaphor and we're done with it, because it confuses more than it helps. For commentary, you could do worse than [1] and [2]. I think this comment from [2] by "dr_s" is spot on:

    > There is no actual definition of stochastic parrot, it's just a derogatory
    > definition to downplay "something that, given a distribution to sample
    > from and a prompt, performs a kind of Markov process to repeatedly predict
    > the most probable next token".
    >
    > The thing that people who love to sneer at AI like Gebru don't seem to
    > get (or willingly downplay in bad faith) is that such a class of functions
    > also include thing that if asked "write me down a proof of the Riemann
    > hypothesis" says "sure, here it is" and then goes on to win a Fields
    > medal. There are no particular fundamental proven limits on how powerful
    > such a function can be. I don't see why there should be.

I suggest this: instead of making the stochastic parrot argument, make a specific prediction: what level of capabilities are out of reach? Give your reasons, too. Make your writing public and see how you do. I agree with "dr_s" -- I'm not going to bet against the capabilities of transformer based technologies, especially not ones with tool-calling as part of their design.

To go a step further, some counter-arguments take the following shape: "If a transformer of size X doesn't have capability C, wait until they get bigger." I get it: this argument can feel unsatisfying to the extent it is open-ended with no resolution criteria. (Nevertheless, increasing scale has indeed shown to make many problems shallow!) So, if you want to play the game honestly, require specific, testable predictions. For example, ask a person to specify what size X' will yield capability C.

[1]: https://www.lesswrong.com/posts/HxRjHq3QG8vcYy4yy/the-stocha...

[2]: https://www.lesswrong.com/posts/7aHCZbofofA5JeKgb/memetic-ju...

[deah12]

btw, question

Isn't stochastic parrot just a modern reframing of Searle's Chinese room, or am I oversimplifying here?