It’s ok, as long as the talking is done in sufficiently rigorous manner. By an analogy, a lot of discoveries in theoretical physics have been made long before they could be experimentally supported. Theoretical CS also has good track record here, for example, the first notable quantum algorithms were discovered long before the first notable quantum computers were built. Furthermore, the theory of computability mostly talks about the uncomputable (computations that cannot be realized and devices that cannot be built in this universe), so has next to no practical applications. It just so happened that many of the ideas and methods developed for CT also turned out to be useful for its younger sister—the theory of algorithmic complexity, which has enormous practical importance.
In short, I feel that the quality of an academic inquiry is more dependent on its methods and results than on its topic.
To have rigorous discussion, one thing we need is clear models of the thing that we are talking about (ie, for computability, we can talk about Turing machines, or specific models of quantum computers). The level of discussion in Superintelligence still isn’t at the level where the mental models are fully specified, which might be where disagreement in this discussion is coming from. I think for my mental model I’m using something like the classic tree search based chess playing AI, but with a bunch of unspecified optimizations that let it do useful search in large space of possible actions (and the ability to reason about and modify it’s own source code). But it’s hard to be sure that I’m not sneaking in some anthropomorphism into my model, which in this case is likely to lead one quickly astray.
It’s ok, as long as the talking is done in sufficiently rigorous manner. By an analogy, a lot of discoveries in theoretical physics have been made long before they could be experimentally supported. Theoretical CS also has good track record here, for example, the first notable quantum algorithms were discovered long before the first notable quantum computers were built. Furthermore, the theory of computability mostly talks about the uncomputable (computations that cannot be realized and devices that cannot be built in this universe), so has next to no practical applications. It just so happened that many of the ideas and methods developed for CT also turned out to be useful for its younger sister—the theory of algorithmic complexity, which has enormous practical importance.
In short, I feel that the quality of an academic inquiry is more dependent on its methods and results than on its topic.
To have rigorous discussion, one thing we need is clear models of the thing that we are talking about (ie, for computability, we can talk about Turing machines, or specific models of quantum computers). The level of discussion in Superintelligence still isn’t at the level where the mental models are fully specified, which might be where disagreement in this discussion is coming from. I think for my mental model I’m using something like the classic tree search based chess playing AI, but with a bunch of unspecified optimizations that let it do useful search in large space of possible actions (and the ability to reason about and modify it’s own source code). But it’s hard to be sure that I’m not sneaking in some anthropomorphism into my model, which in this case is likely to lead one quickly astray.