I think what I’m trying to get at, here, is that the ability to use these better, self-derived abstractions for planning is nontrivial, and requires a specific universal-planning algorithm to work. Animals et al. learn new concepts and their applications simultaneously: they see e. g. a new fruit, try eating it, their taste receptors approve/disapprove of it, and they simultaneously learn a concept for this fruit and a heuristic “this fruit is good/bad”. They also only learn new concepts downstream of actual interactions with the thing; all learning is implemented by hard-coded reward circuitry.
Humans can do more than that. As in my example, you can just describe to them e. g. a new game, and they can spin up an abstract representation of it and derive heuristics for it autonomously, without engaging hard-coded reward circuitry at all, without doing trial-and-error even in simulations. They can also learn new concepts in an autonomous manner, by just thinking about some problem domain, finding a connection between some concepts in it, and creating a new abstraction/chunking them together.
Hmm I feel like you’re underestimating animal cognition / overestimating how much of what humans can do comes from unique algorithms vs. accumulated “mental content”. Non-human animals don’t have language, culture, and other forms of externalized representation, including the particular human representations behind “learning the rules of a game”. Without these in place, even if one was using the “universal planning algorithm”, they’d be precluded from learning through abstract description and from learning through manipulation of abstract game-structure concepts. All they’ve got is observation, experiment, and extrapolation from their existing concepts. But lacking the ability to receive abstract concepts via communication doesn’t mean that they cannot synthesize new abstractions as situations require. I think there’s good evidence that other animals can indeed do that.
General intelligence is an algorithm for systematic derivation of such “other changes”.
Does any of that make sense to you?
I get what you’re saying but disbelieve the broader theory. I think the “other changes” (innovations/useful context-specific improvements) we see in reality aren’t mostly attributable to the application of some simple algorithm, unless we abstract away all ofthe details that did the actual work. There are general purpose strategies (for ex. the “scientific method” strategy, which is an elaboration of the “model-based RL” strategy, which is an elaboration of the “trial and error” strategy) that are widely applicable for deriving useful improvements. But those strategies are at a very high level of abstraction, whereas the bulk of improvement comes from using strategies to accumulate lower-level concrete “content” over time, rather than merely from adopting a particular strategy.
Non-human animals don’t have language, culture, and other forms of externalized representation, including the particular human representations behind “learning the rules of a game”. Without these in place, even if one was using the “universal planning algorithm”, they’d be precluded from learning through abstract description and from learning through manipulation of abstract game-structure concepts
Agreed, I think. I’m claiming that those abilities are mutually dependent. Turing-completeness allows to construct novel abstractions like language/culture/etc., but it’s only useful if there’s a GI algorithm that can actually take these novelties in as inputs. Otherwise, there’s no reason to waste compute deriving ahead of time abstractions you haven’t encountered yet and won’t know how to use; may as well wait until you run into them “in the wild”.
In turn, the GI algorithm is (as you point out) only shines if there’s extant machinery that’s generating novel abstractions for it to plan over. Otherwise, it can do no better than trial-and-error learning.
I guess I don’t see much support for such mutual dependence. Other animals have working memory + finite state control, and learn from experience in flexible ways. It appears pretty useful to them despite the fact they don’t have language/culture. The vast majority of our useful computing is done by systems that have Turing-completeness but not language/cultural competence. Language models sure look like they have language ability without Turing-completeness and without having picked up some “universal planning algorithm” that would render our previous work w/ NNs ~useless.
Why choose a theory like “the capability gap between humans and other animals is because the latter is missing language/culture and also some binary GI property” over one like “the capability gap between humans and other animals is just because the latter is missing language/culture”? IMO the latter is simpler and better fits the evidence.
Hmm, we may have reached the point from which we’re not going to move on without building mathematical frameworks and empirically testing them, or something.
Other animals have working memory + finite state control, and learn from experience in flexible ways
“Learn from experience” is the key point. Abstract thinking allows to learn without experience — from others’ experience that they communicate to you, or from just figuring out how something works abstractly and anticipating the consequences in advance of them occurring. This sort of learning, I claim, is only possible when you have the machinery for generating entirely novel abstractions (language, math, etc.), which in turn is only useful if you have a planning algorithm capable of handling any arbitrary abstraction you may spin up.
“The capability gap between humans and other animals is because the latter is missing language/culture and also some binary GI property” and “the capability gap between humans and other animals is just because the latter is missing language/culture” are synonymous, in my view, because you can’t have language/culture without the binary GI property.
Language models sure look like they have language ability
As per the original post, I disagree that they have the language ability in the relevant sense. I think they’re situated firmly on the Simulacrum Level 4; they appear to communicate, but it’s all just reflexes.
I didn’t mean “learning from experience” to be restrictive in that way. Animals learn by observing others & from building abstract mental models too. But unless one acquires abstracted knowledge via communication, learning requires some form of experience: even abstracted knowledge is derived from experience, whether actual or imagined. Moreover, I don’t think that some extra/different planning machinery was required for language itself, beyond the existing abstraction and model-based RL capabilities that many other animals share. But ultimately that’s an empirical question.
Hmm, we may have reached the point from which we’re not going to move on without building mathematical frameworks and empirically testing them, or something.
Yeah I am probably going to end my part of the discussion tree here.
My overall take remains:
There may be general purpose problem-solving strategies that humans and non-human animals alike share, which explain our relative capability gains when combined with the unlocks that came from language/culture.
We don’t need any human-distinctive “general intelligence” property to explain the capability differences among human-, non-human animal-, and artificial systems, so we shouldn’t assume that there’s any major threshold ahead of us corresponding to it.
Moreover, I don’t think that some extra/different planning machinery was required for language itself, beyond the existing abstraction and model-based RL capabilities that many other animals share.
I would expect to see sophisticated ape/early-hominid-lvl culture in many more species if that was the case. For some reason humans went on the culture RSI trajectory whereas other animals didn’t. Plausibly there was some seed cognitive ability (plus some other contextual enablers) that allowed a gene-culture “coevolution” cycle to start.
Hmm I feel like you’re underestimating animal cognition / overestimating how much of what humans can do comes from unique algorithms vs. accumulated “mental content”. Non-human animals don’t have language, culture, and other forms of externalized representation, including the particular human representations behind “learning the rules of a game”. Without these in place, even if one was using the “universal planning algorithm”, they’d be precluded from learning through abstract description and from learning through manipulation of abstract game-structure concepts. All they’ve got is observation, experiment, and extrapolation from their existing concepts. But lacking the ability to receive abstract concepts via communication doesn’t mean that they cannot synthesize new abstractions as situations require. I think there’s good evidence that other animals can indeed do that.
I get what you’re saying but disbelieve the broader theory. I think the “other changes” (innovations/useful context-specific improvements) we see in reality aren’t mostly attributable to the application of some simple algorithm, unless we abstract away all of the details that did the actual work. There are general purpose strategies (for ex. the “scientific method” strategy, which is an elaboration of the “model-based RL” strategy, which is an elaboration of the “trial and error” strategy) that are widely applicable for deriving useful improvements. But those strategies are at a very high level of abstraction, whereas the bulk of improvement comes from using strategies to accumulate lower-level concrete “content” over time, rather than merely from adopting a particular strategy.
(Would again recommend Hanson’s blog on “The Betterness Explosion” as expressing my side of the discussion here.)
Agreed, I think. I’m claiming that those abilities are mutually dependent. Turing-completeness allows to construct novel abstractions like language/culture/etc., but it’s only useful if there’s a GI algorithm that can actually take these novelties in as inputs. Otherwise, there’s no reason to waste compute deriving ahead of time abstractions you haven’t encountered yet and won’t know how to use; may as well wait until you run into them “in the wild”.
In turn, the GI algorithm is (as you point out) only shines if there’s extant machinery that’s generating novel abstractions for it to plan over. Otherwise, it can do no better than trial-and-error learning.
I guess I don’t see much support for such mutual dependence. Other animals have working memory + finite state control, and learn from experience in flexible ways. It appears pretty useful to them despite the fact they don’t have language/culture. The vast majority of our useful computing is done by systems that have Turing-completeness but not language/cultural competence. Language models sure look like they have language ability without Turing-completeness and without having picked up some “universal planning algorithm” that would render our previous work w/ NNs ~useless.
Why choose a theory like “the capability gap between humans and other animals is because the latter is missing language/culture and also some binary GI property” over one like “the capability gap between humans and other animals is just because the latter is missing language/culture”? IMO the latter is simpler and better fits the evidence.
Hmm, we may have reached the point from which we’re not going to move on without building mathematical frameworks and empirically testing them, or something.
“Learn from experience” is the key point. Abstract thinking allows to learn without experience — from others’ experience that they communicate to you, or from just figuring out how something works abstractly and anticipating the consequences in advance of them occurring. This sort of learning, I claim, is only possible when you have the machinery for generating entirely novel abstractions (language, math, etc.), which in turn is only useful if you have a planning algorithm capable of handling any arbitrary abstraction you may spin up.
“The capability gap between humans and other animals is because the latter is missing language/culture and also some binary GI property” and “the capability gap between humans and other animals is just because the latter is missing language/culture” are synonymous, in my view, because you can’t have language/culture without the binary GI property.
As per the original post, I disagree that they have the language ability in the relevant sense. I think they’re situated firmly on the Simulacrum Level 4; they appear to communicate, but it’s all just reflexes.
I didn’t mean “learning from experience” to be restrictive in that way. Animals learn by observing others & from building abstract mental models too. But unless one acquires abstracted knowledge via communication, learning requires some form of experience: even abstracted knowledge is derived from experience, whether actual or imagined. Moreover, I don’t think that some extra/different planning machinery was required for language itself, beyond the existing abstraction and model-based RL capabilities that many other animals share. But ultimately that’s an empirical question.
Yeah I am probably going to end my part of the discussion tree here.
My overall take remains:
There may be general purpose problem-solving strategies that humans and non-human animals alike share, which explain our relative capability gains when combined with the unlocks that came from language/culture.
We don’t need any human-distinctive “general intelligence” property to explain the capability differences among human-, non-human animal-, and artificial systems, so we shouldn’t assume that there’s any major threshold ahead of us corresponding to it.
I would expect to see sophisticated ape/early-hominid-lvl culture in many more species if that was the case. For some reason humans went on the culture RSI trajectory whereas other animals didn’t. Plausibly there was some seed cognitive ability (plus some other contextual enablers) that allowed a gene-culture “coevolution” cycle to start.