On Thu, Mar 29, 2012 at 12:29 PM, Max Orhai <max.or...@gmail.com> wrote:
> Probability is highly applicable to (bounded) nondeterminism, but I get > the impression that most CS theorists don't tend to learn much about it, > and I know for sure that it gets extremely short shrift in the applied CS > curriculum at my school. > How would you suggest applying probability to non-determinism? What benefit would it provide? I've seen some cases where probability is applied to non-determinism, e.g. concurrent-constraint probability models. But the benefits it offers seem marginal. > > Dave Ungar loves being deliberately provocative, but I really don't > understand why he's so attached to the (obviously unscalable) shared memory > imperative programming model... except, perhaps, he thinks that's the only > model the great unwashed masses of industry coders can handle. If so, I > sure hope he's wrong. > I also hope he's wrong. After all, the unwashed masses barely handle even that. Even if we exclude concurrency, most bugs and complexity involve state. > > But, lets face it, after decades of real-world deployment, Erlang is still > considered an exotic language, and hardly anybody outside the ivory towers > has even heard of Kahn nets, FRP, CALM, etc. These don't get taught in the > undergrad CS curriculum either. > Most professors don't know them either. Doctorates in CS are generally for a specialized contribution, not broad knowledge. It's unfortunate. > > Programmers, like everybody else, only get to choose their problems > inasmuch as they are aware of the choices. > And the only way to make them aware is to provide a killer app to extend... Regards, Dave -- bringing s-words to a pen fight
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