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Hypothesis: Because the solution to "hard AI" isn't actually "general". Ie, what humans view as "intelligence" isn't actually an emergent property of the right

Hypothesis: Because the solution to "hard AI" isn't actually "general". Ie, what humans view as "intelligence" isn't actually an emergent property of the right set of rules. Rather, it's a massive and hopelessly self-intersecting set of ad-hoc solutions and special casing all jumbled together over millions of years of evolution, producing something that looks general, but only because we're looking at it from within itself. An easy example: Having a computer identify all images of a thing called a "cup". That's an arbitrary category that has no definition reducible to actual physical properties. What makes something a "cup"? A human saying it is one. For something to look like "full, general AI" to humans, we'd need to either: Build something that replicates all that specialized hyper-meta-spaghetti-code mental processing of ours to a level of fidelity that's beyond both our current understanding of our brain's structure and the level of complexity tenable by human computer science. Or leave out those evolutionarily-discovered "optimization circuits" and require far, far more processing power than we'll have access to for a good, long time.

jimmaswell 12y ago
"What makes something a "cup"? A human saying it is one" And the human got their definition of cup by seeing a lot of things others called a cup and extrapolating rules, something that's already obtainable for some problems with solutions like neural networks.
- Epenthesis 12y ago
  True, but that doesn't really negate the hypothesis. What are those rules? Are they the same as the ones generated by a neural network? Or are they qualitatively different? (Diagnostic experiment: Is there a significant subset of "cups" that a naive neural network would always fail to include or only include when also including a significant subset of non-"cups"?)
- fit2rule 12y ago
  So you're saying we can't have proper AI until we've had some semi-decent AI? That explains everything: what constitutes decent AI, versus good AI, versus superlative AI? The problem is: nobody knows. Its a bit like two cavemen banging rocks together until one says "hey, lets use these rocks to kill something" and the other one goes "eh? How?" and the first one says "I don't know, keep bashing until something dies.."
- Retra 12y ago
  I think, if you take Epenthesis' statement and shift up a meta-level, you're going to lose a lot of the clarity in what you're saying. A cup happens to have a fairly stable, consistent representation in a variety of relevant timescales. But if you want a computer to identify something like "an isomorphism," or "a useless activity," or "something to help a thirsty person not dehydrate," it is not so clear what the rules are anymore.
eli_gottlieb 12y ago
This contradicts our present-day knowledge of cognitive science: probmods.org
- kanzure 12y ago
  > This contradicts our present-day knowledge of cognitive science: probmods.org But nobody was arguing about the infeasibility of approximations of specific human cognitive abilities. Wasn't the conversation about general human cognitive ability?
orbifold 12y ago
I believe one problem is, that computers right now are terrible at considering things up to some equivalence relation. Probably mainly because most equivalence relations are hard to express as computations: For example it is "easy" for a human to "see" that a coffee cup and a donut are topologically the same, whereas the same task for a computer, i.e. provide it with enough data to recognize something to have exactly one "hole" seems extremely hard. Another example would be to recognize that up to rotation two things are roughly the same. Clearly humans do not solve such problems case by case, they have some build in classifier system that solves those problems by a general principle.

jimmaswell 12y ago

"What makes something a "cup"? A human saying it is one" And the human got their definition of cup by seeing a lot of things others called a cup and extrapolating rules, something that's already obtainable for some problems with solutions like neural networks.

Epenthesis 12y ago
True, but that doesn't really negate the hypothesis. What are those rules? Are they the same as the ones generated by a neural network? Or are they qualitatively different? (Diagnostic experiment: Is there a significant subset of "cups" that a naive neural network would always fail to include or only include when also including a significant subset of non-"cups"?)
fit2rule 12y ago
So you're saying we can't have proper AI until we've had some semi-decent AI? That explains everything: what constitutes decent AI, versus good AI, versus superlative AI? The problem is: nobody knows. Its a bit like two cavemen banging rocks together until one says "hey, lets use these rocks to kill something" and the other one goes "eh? How?" and the first one says "I don't know, keep bashing until something dies.."
Retra 12y ago
I think, if you take Epenthesis' statement and shift up a meta-level, you're going to lose a lot of the clarity in what you're saying. A cup happens to have a fairly stable, consistent representation in a variety of relevant timescales. But if you want a computer to identify something like "an isomorphism," or "a useless activity," or "something to help a thirsty person not dehydrate," it is not so clear what the rules are anymore.

eli_gottlieb 12y ago

This contradicts our present-day knowledge of cognitive science: probmods.org

kanzure 12y ago
> This contradicts our present-day knowledge of cognitive science: probmods.org But nobody was arguing about the infeasibility of approximations of specific human cognitive abilities. Wasn't the conversation about general human cognitive ability?

orbifold 12y ago

I believe one problem is, that computers right now are terrible at considering things up to some equivalence relation. Probably mainly because most equivalence relations are hard to express as computations: For example it is "easy" for a human to "see" that a coffee cup and a donut are topologically the same, whereas the same task for a computer, i.e. provide it with enough data to recognize something to have exactly one "hole" seems extremely hard. Another example would be to recognize that up to rotation two things are roughly the same. Clearly humans do not solve such problems case by case, they have some build in classifier system that solves those problems by a general principle.