[GeoffWozniak]

Taking into account the snark, it doesn't change the fact that you're presenting a false dichotomy.

Ungar isn't saying that we have to give up determinism. What he's saying is that to take advantage of massively parallel systems, determinism comes at a high cost.

Clearly, we can write programs on current architectures that are deterministic (well, perceived as such, at least) and we don't have to forgo that. But there is no reason to believe it has to be the only architecture. Ungar is looking at what happens when you try to do computation in a highly networked environment with low latency (like say, a brain).

Also, if you don't see it helping for the practitioner, perhaps practitioners aren't asking good (or enough?) questions.

[hessenwolf]

Determinism comes at a high cost? So the more processors we add, the more our computers become like us, very smart but not entirely accurate?

[brazzy]

More like: the more processors we add, the more performance can be gained by switching to nondeterministic algorithms for problems where getting a solution that is within 99% of the perfectly correct one and arrives in 1 second is better than getting a perfect solution in 2 hours.

[azim]

I like the way you've phrased this, so let me give a contrived but quasi-realistic example: I have a network of nodes which calculates and returns some floating point value. I then accumulate those in the arbitrary order in which they come back from in the network. (According to Google) 1111.0000000000001 * 2 * 1111.00000000000001 = 2468642 BUT 1111.0000000000001 * 1111.00000000000001 * .2 = 246864.2. By using floating point we've already decided it's OK to give up some precision, however by applying the commutative property of multiplication now we've also given up determinism.

[JoeAltmaier]

Ungar could have said what you said, but didn't. Clearly some gee-whiz marketing going on.

And no, practitioners shouldn't have to ask the question "what good it is?" Every research result begs that question.