I’ve read about CYC a while back—from what I recall/gather it is a massive handbuilt database of little natural language ‘facts’.
Some of the new stuff they are working on with search looks kinda interesting, but in general I don’t see this as a viable approach to AGI. A big syntactic database isn’t really knowledge—it needs to be grounded to a massive sub-symbolic learning system to get the semantics part.
On the other hand, specialized languages for AGI’s? Sure. But they will need to learn human languages first to be of practical value.
You look at CYC and see a massive hand-built database of facts.
I look and see a smaller (but still large) hand-built ontology of concepts
You, probably because you have worked in computer vision or pattern recognition, notice that the database needs to be grounded in some kind of perception machinery to get semantics.
I, probably because I have worked in logic and theorem proving, wonder what axioms and rules of inference exist to efficiently provide inference and planning based upon this ontology.
One of my favorite analogies and I’m fond of the Jainist? multi-viewpoint approach.
As for the logic/inference angle, I suspect that this type of database underestimates the complexity of actual neural concepts—as most of the associations are subconscious and deeply embedded in the network.
We use ‘connotation’ to describe part of this embedding concept, but I see it as even deeper than that. A full description of even a simple concept may be on the order of billions of such associations. If this is true, then a CYC like approach is far from appropriately scalable.
It appears that you doubt that an AI whose ontology is simpler and cleaner than that of a human can possibly be intellectually more powerful than a human.
All else being equal, I would doubt that with respect to a simpler ontology, while the ‘cleaner’ adjective is less well defined.
Look at it in terms of the number of possible circuit/program configurations that are “intellectually more powerful than a human” as a function of the circuit/program’s total bit size.
At around the human level of roughly 10^15 I’m almost positive there are intellectually more powerful designs—so P_SH(10^15) = 1.0.
I’m also positive that beyond some threshold there are absolutely zero possible configurations of superhuman intellect—say P_SH(10^10) ~ 0.0.
Of course “intellectually more powerful” is open to interpretation. I’m thinking of it here in terms of the range of general intelligence tasks human brains are specially optimized for.
IBM’s Watson is superhuman in a certain novel narrow range of abilities, and it’s of complexity around 10^12 to 10^13.
I’ve read about CYC a while back—from what I recall/gather it is a massive handbuilt database of little natural language ‘facts’.
Some of the new stuff they are working on with search looks kinda interesting, but in general I don’t see this as a viable approach to AGI. A big syntactic database isn’t really knowledge—it needs to be grounded to a massive sub-symbolic learning system to get the semantics part.
On the other hand, specialized languages for AGI’s? Sure. But they will need to learn human languages first to be of practical value.
Blind men looking at elephants.
You look at CYC and see a massive hand-built database of facts.
I look and see a smaller (but still large) hand-built ontology of concepts
You, probably because you have worked in computer vision or pattern recognition, notice that the database needs to be grounded in some kind of perception machinery to get semantics.
I, probably because I have worked in logic and theorem proving, wonder what axioms and rules of inference exist to efficiently provide inference and planning based upon this ontology.
One of my favorite analogies and I’m fond of the Jainist? multi-viewpoint approach.
As for the logic/inference angle, I suspect that this type of database underestimates the complexity of actual neural concepts—as most of the associations are subconscious and deeply embedded in the network.
We use ‘connotation’ to describe part of this embedding concept, but I see it as even deeper than that. A full description of even a simple concept may be on the order of billions of such associations. If this is true, then a CYC like approach is far from appropriately scalable.
It appears that you doubt that an AI whose ontology is simpler and cleaner than that of a human can possibly be intellectually more powerful than a human.
All else being equal, I would doubt that with respect to a simpler ontology, while the ‘cleaner’ adjective is less well defined.
Look at it in terms of the number of possible circuit/program configurations that are “intellectually more powerful than a human” as a function of the circuit/program’s total bit size.
At around the human level of roughly 10^15 I’m almost positive there are intellectually more powerful designs—so P_SH(10^15) = 1.0.
I’m also positive that beyond some threshold there are absolutely zero possible configurations of superhuman intellect—say P_SH(10^10) ~ 0.0.
Of course “intellectually more powerful” is open to interpretation. I’m thinking of it here in terms of the range of general intelligence tasks human brains are specially optimized for.
IBM’s Watson is superhuman in a certain novel narrow range of abilities, and it’s of complexity around 10^12 to 10^13.