Designing a practical chess program before understanding backtracking algorithms and search trees (or analogous conceptual tools) seems difficult. The same concept applies to your other examples (bridge building, aviation) where it is important to have a theoretical understanding of relevant physics before trying to build the Golden Gate bridge or a 747.
I’m not sure what the problem domain of chess has to do with AGI
It’s an analogy: chess is a domain where smart people were confused about it (Poe), and then Shannon developed conceptual tools to resolve many of those confusions (trees, backtracking), and this enabled the creation of practical algorithms (Deep Blue). The claim is that AGI is similar: we are currently confused about it, and conceptual tools / theoretical understanding seem quite important.
This theoretical understanding alone is not enough, of course. As with flight, practical experimentation is necessary both before (e.g., to figure out which physics questions to ask) and after (e.g., to deal with higher-order complications, as you said). The point we’re trying to make with the technical agenda is that the current understanding of AGI is still wanting for conceptual tools. (The rest of the document attempts to back this claim up, by providing examples of confusing questions that we lack the conceptual tools to answer.)
(I definitely agree that significant practical work is necessary, but I don’t think that modern practical systems are close enough to AGI for practical work available today to have a high chance of being relevant in the future. I’ll expand on this when I reply to your other comments :-)
Designing a practical chess program before understanding backtracking algorithms and search trees (or analogous conceptual tools) seems difficult. The same concept applies to your other examples (bridge building, aviation) where it is important to have a theoretical understanding of relevant physics before trying to build the Golden Gate bridge or a 747.
It’s an analogy: chess is a domain where smart people were confused about it (Poe), and then Shannon developed conceptual tools to resolve many of those confusions (trees, backtracking), and this enabled the creation of practical algorithms (Deep Blue). The claim is that AGI is similar: we are currently confused about it, and conceptual tools / theoretical understanding seem quite important.
This theoretical understanding alone is not enough, of course. As with flight, practical experimentation is necessary both before (e.g., to figure out which physics questions to ask) and after (e.g., to deal with higher-order complications, as you said). The point we’re trying to make with the technical agenda is that the current understanding of AGI is still wanting for conceptual tools. (The rest of the document attempts to back this claim up, by providing examples of confusing questions that we lack the conceptual tools to answer.)
(I definitely agree that significant practical work is necessary, but I don’t think that modern practical systems are close enough to AGI for practical work available today to have a high chance of being relevant in the future. I’ll expand on this when I reply to your other comments :-)