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I see now that "conceptually solved" is just too ambiguous. Translation is conceptually solved, in the sense that I meant to evoke, to the extent that it's solv
by adrusi 10y ago
I see now that "conceptually solved" is just too ambiguous. Translation is conceptually solved, in the sense that I meant to evoke, to the extent that it's solved. We have all the conceptual understanding necessary to translate between human languages. We don't have a full conceptual model of language, but we discovered that that's not necessary for translation.
Suppose a team of reseachers were given a computing platform a trillion times more powerful than the best systems available today, and a trillion dollars to build a set of training data. Would they be able to create a general AI in a predictable timeframe? If not then there's at least some area where we need to develop conceptual understanding. My intuition says that this is the case.
If you think that general AI can, in fact, be developed the same way as google translate, then what do you think the training data would look like? I'd imagine that the translation training set looks something like a massive Rosetta stone, though I haven't looked into it. Inputs associated with a valid output. What would a set of input/output pairs look like for a general AI? Any valid English query/statement/remark/essay with an "intelligent" response? Like a chatterbot? Doesn't seem like that would work. Or maybe I'm wrong. Maybe a chatterbot with ungodly amounts of computational resources and training data would develop the ability to reason. Maybe I'm just not appreciating how much that could change the game.
But it seems more likely that the first general AI will look more like Alphago, where we started with a reasonably solid understanding of how to approach the game, and then introduced deep learning where we discovered it was the best approach. We don't have a reasonably solid understanding of how to organize a general intelligence, and we don't know what it would take to get there.
- kobeya 10y ago> We have all the conceptual understanding necessary to translate between human languages. Except that we don't. The original Google Translate used the conceptual modeling approach to translation, and it was absolute shit for any non-closely related language pairs. Google Translate (and Baidu, etc.) now uses deep networks whose operations are rather opaque. We don't understand in detail how they work. But that didn't stop us from building them.
- adrusi 10y agoIn context, that claim was an explanation of what I was trying to get at with the phrase "conceptual understanding". We don't have a conceptual understanding of language. We do have a conceptual understanding of how to translate between human languages — Proof: we can translate between human languages. If you think that's not adequate proof then you're disagreeing with me on the semantics of "conceptual understanding". If you have an alternative name for that then I'd appreciate it because I think my usage is confusing. It's not a useless notion of "conceptual understanding" though, because while being able to have a program complete a task implies having the conceptual understanding necessary for that task, it's possible to have the conceptual understanding but not be able to make a program, if, for instance, it would require greater (within reason) computing resources.
- kobeya 10y agoIt does sound pretty useless because the same logic can be used to prove that we have a conceptual understanding of general intelligence, because after all we exist and we think.
- lern_too_spel 10y agoGoogle Translate never used conceptual modeling. The original Google Translate used statistical machine translation.
- btilly 10y agoGoogle Translate itself may have been done through statistical machine translation, but that wasn't Google's first attempt at the subject. In particular Google spent around $100 million in 2003 for Applied Semantics, in part hoping that their natural language processing expertise could be used for translation. That technology was superseded by statistical techniques.