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.
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.
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.
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 That technology was superseded by statistical techniques.
To me google translate seems like the perfect illustration of the shortcomings of this purely data driven approach...
Alpha go, on the other hand is a much much more elaborated algorithm ( from my very limited knowledge) and it does produce amazing results.
Yes, I can list plenty of cases where translation fails. For example not understanding that in a conversation about specialist topic X (eg Ruby programming, or the game of G) that seemingly common terms have specific meanings. But I can also use it on a random newspaper article in a different language and expect to understand the result. This was something that I did not expect to happen in my lifetime.
The genius of the RL feedback loop makes me think we can get to AGI without understanding human intelligence holistically.
A good example is Google Translate. We stopped trying to "understand" language and just threw a large dataset at pattern matching. And it worked amazingly well.
For other things we throw ideas of "this might improve what we can already do" and it winds up effectively solved, whether or not we understand it. This happened, for example, with Alpha-Go.
So "conceptually solved" and "AI can do it" are pretty much disconnected.