4 ms·

It's worth noting that Djikstra (to my understanding) thought almost none of that was even contained in the field of CS. His perspective was that CS was about p

by tel 13y ago

It's worth noting that Djikstra (to my understanding) thought almost none of that was even contained in the field of CS. His perspective was that CS was about process and verification and proof and thus while the current implementation of computers is interesting, it didn't deserve any privilege. So being able to prove certain nice properties about algorithms without worrying about the underlying implementation is exactly what Djikstra believed important and something that Haskell does allow you to do... even if the result is abysmal performance. It's worth noting as well that there's a whole wonderful book [1] that provides a great glimpse of good Haskell style with the conceit that one should program the most abysmally slow correct thing first and then use algebraic theories to transform it along obviously correct paths to something highly performant. [1] http://www.amazon.com/Pearls-Functional-Algorithm-Design-Richard/dp/0521513383 http://www.amazon.com/Pearls-Functional-Algorithm-Design-Ric...

SilasX 13y ago
>His perspective was that CS was about process and verification and proof and thus while the current implementation of computers is interesting, it didn't deserve any privilege. Is that the basis for the quote about "CS isn't about computers any more than astronomy is about telescopes"? http://en.wikiquote.org/wiki/Edsger_W._Dijkstra#Disputed http://en.wikiquote.org/wiki/Edsger_W._Dijkstra#Disputed
knz42 13y ago
"one should program the most abysmally slow correct thing first and then use algebraic theories to transform it along obviously correct paths to something highly performant." Except that the syntactic variance problem [1] makes this impossible to do in theory. Unfortunately so long we use Turing-equivalent computers there will be a need for "performance programmers" and low-level programming because we cannot automate their job by formal transformations from high-level, "obviously correct" programs (theory proves so). [1] http://dx.doi.org/10.1145/502175.502181 http://dx.doi.org/10.1145/502175.502181
abc_lisper 13y ago
Well said! Understanding the machine, while useful, does not usually help you solve the problem at hand. A better approach is to teach how to create models, mental and the ones on the computer(datastructures, types etc), and show how that maps to problems at hand. Teaching how to verify models for rigor, efficiency(algorithmic), taste for elegance , and help seeing that computer science is not all that different from math, is what helps you build structures that stand the test of time.

by tel 13y ago

SilasX 13y ago

>His perspective was that CS was about process and verification and proof and thus while the current implementation of computers is interesting, it didn't deserve any privilege. Is that the basis for the quote about "CS isn't about computers any more than astronomy is about telescopes"? http://en.wikiquote.org/wiki/Edsger_W._Dijkstra#Disputed http://en.wikiquote.org/wiki/Edsger_W._Dijkstra#Disputed

knz42 13y ago

"one should program the most abysmally slow correct thing first and then use algebraic theories to transform it along obviously correct paths to something highly performant." Except that the syntactic variance problem [1] makes this impossible to do in theory. Unfortunately so long we use Turing-equivalent computers there will be a need for "performance programmers" and low-level programming because we cannot automate their job by formal transformations from high-level, "obviously correct" programs (theory proves so). [1] http://dx.doi.org/10.1145/502175.502181 http://dx.doi.org/10.1145/502175.502181

abc_lisper 13y ago

Well said! Understanding the machine, while useful, does not usually help you solve the problem at hand. A better approach is to teach how to create models, mental and the ones on the computer(datastructures, types etc), and show how that maps to problems at hand. Teaching how to verify models for rigor, efficiency(algorithmic), taste for elegance , and help seeing that computer science is not all that different from math, is what helps you build structures that stand the test of time.