computationally

By capture, I mean understand them well enough to describe them mathematically so that they can be simulated computationally.

In particular, it completely ignores computational complexity: it gives equal weight to all (computationally) simple explanations (eg.

That said, any sort of hyperparameter optimization is extremely computationally intensive so random search is far from a panacea.

Clearly it's different to photon mapping, but what ever happened to that: Was it too computationally expensive for the pay-off?

Was there any advantage of using a deep learning model instead of something more computationally simple?

And there's still the problem of whether it is too computationally expensive to do for every thread view for every person.

There is zero reason to believe that human level mathematical creativity is computationally significantly more expensive than normal day to day thinking.

But that sounds almost impossibly computationally expensive for 3D games and the like.

CPU usage shouldn't be high at all unless something really computationally expensive needs to go on like speech recognition or video encoding or something.

Assuming a competent implementation, it should be computationally infeasible to brute-force any digests in less than a few billion years using current models of computation.

In fact, the OP lucked out because this is a very special circumstance (this method is completely computationally infeasible for 99/100 applications of this problem).

It is his conjecture that there is no computationally simple mapping, but I see no reason to believe that this conjecture is correct.

"there is a star orbiting the Earth, it will keep orbiting" and "the Earth is spinning near to a star, and will keep spinning"), as well as to all (computationally) complicated explanations (eg.

One could conceivably target an individuals desired accent recognition so you could let the user say "Most of my callers speak in an X accent" and then get a little more accurate for that pool of callers, but this also means installing disparate systems for recognition, each of which are quite expensive computationally.