[W]hile dynamical systems often cannot be effectively computed on a finite state machine (such as a von Neumann computer), they can often be efficien… - Joscha Bach

For Turing it wasn't quite so bad. ...[T]uring could see that the solution is to understand that mathematics was computational all along. ...For instance pi in classical mathematics is a value. It's also a function, but it's the same thing. In computation, a function is only a value when you can compute it, and if you cannot compute the last digit of pi, you only have a function. You can plug this function into your local sun, let it run until the sun burns out... This is it. This is the last digit of pi you will know. But it also means that there can be no process in the physical universe, or in any physically realized computer that depends on having known the last digit of pi. ...Which means that there are parts of physics that are defined in such a way that cannot strictly be true, because, assuming that this could be true leads into contradictions.

With this vector space you can do amazing things, [e.g.,] if you take the vector from king to queen, it's pretty much the same vector as between man and woman. ...[B]ecause [these concept spaces are] really a high dimension manifold, we can do interesting things like machine translation without understanding what it means, that is, without doing any proper mental representation that predicts the world. ...[T]his is [a] type of mental representation that is somewhat incomplete, but it captures the landscape that we share in a culture.

[T]aking [the design of a cognitive architecture] to the AI laboratory... requires the theory not merely to be plausible, but... requires it to be fit for implementation, and delivers it to the... merciless battle of testing.