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To me it sounded more like "the problems they do at Mathematical Olympiads are not the real mathematics done at academia/the enterprise," which turns out to be

To me it sounded more like "the problems they do at Mathematical Olympiads are not the real mathematics done at academia/the enterprise," which turns out to be quite true in many ways.

chubot 12y ago
Not just math -- even the problems done at programming contests have fairly little to do with programming! I've worked at Google for nearly 10 years -- not sure I can solve the Google code Jams :) Or at least I haven't been motivated to do so. I know all my basic data structures and algorithms, but the questions don't seem that motivating.
- radicality 12y ago
  Terminology would probably be important – programming contests have a lot to do with programming, you literally sit and write programs that pass the test cases. I'm sure you meant it has fairly little to do with day-to-day programming for the majority of software engineers, which is correct. Regarding the article, I also disagree with it. I feel like the author is saying "algorithms != programming contests" which is false. Algorithms are the essence of programming contests, just like 'machine learning' is the essence of Kaggle competitions. Open up Bishop's "Pattern Recognition and Machine learning" and you won't find anything about "deploying your model to production", but you will find lots of math on how to build models because that is what ML is. What the author seems to be describing is data-science.
krick 12y ago
I'd rather say that mathematics at academia aren't real mathematics. It pretty much depends on academia, of course, but what is generally taught to students under the name of "mathematics" (especially on technical programs, like engineering, CS, etc.) has less mathematical value than typical problem at Olympiads. But that wasn't my point, anyway. Yes, that is true that "pure something" is almost always significantly different from "something in the real life". That's pretty obvious, actually. I guess that there's virtually nothing in the "real life" that would consist from only one "core" activity. In fact, it's the opposite: you make some activity "pure" in order to master it, because in the real life there's so much that distracts you from mastering that one essential skill, whatever would it be. Plubmers and painters (and almost everyone else) must to be able to talk to others and to solve totally human-related problems in order to do their job. Person you would call sniper actually does much more running, crawling, waiting and hiding than shooting. Or does it really surprise anyone that policemen do tons of paperwork every day and not so much of that stuff they show in the movies? And somewhat opposite example: it's obvious that cook's work isn't only slicing vegetables, but have you ever seen how any real cook handles his knife? Please do, it's sight to behold. There's nothing "pure" in this world, no surprise here. So if it would be Yan LeCun writing something like this it would be justifiable (although I would be rather surprised to hear that), but in this particular case it's more like saying "Kaggle isn't important, data science is me!", in which case I find that NBA example to be OK. To summarise: "data science is more than building a model" — yes, "Machine learning isn't Kaggle competitions" — no.

To me it sounded more like "the problems they do at Mathematical Olympiads are not the real mathematics done at academia/the enterprise," which turns out to be quite true in many ways.

chubot 12y ago

Not just math -- even the problems done at programming contests have fairly little to do with programming! I've worked at Google for nearly 10 years -- not sure I can solve the Google code Jams :) Or at least I haven't been motivated to do so. I know all my basic data structures and algorithms, but the questions don't seem that motivating.

radicality 12y ago
Terminology would probably be important – programming contests have a lot to do with programming, you literally sit and write programs that pass the test cases. I'm sure you meant it has fairly little to do with day-to-day programming for the majority of software engineers, which is correct. Regarding the article, I also disagree with it. I feel like the author is saying "algorithms != programming contests" which is false. Algorithms are the essence of programming contests, just like 'machine learning' is the essence of Kaggle competitions. Open up Bishop's "Pattern Recognition and Machine learning" and you won't find anything about "deploying your model to production", but you will find lots of math on how to build models because that is what ML is. What the author seems to be describing is data-science.

krick 12y ago

I'd rather say that mathematics at academia aren't real mathematics. It pretty much depends on academia, of course, but what is generally taught to students under the name of "mathematics" (especially on technical programs, like engineering, CS, etc.) has less mathematical value than typical problem at Olympiads. But that wasn't my point, anyway. Yes, that is true that "pure something" is almost always significantly different from "something in the real life". That's pretty obvious, actually. I guess that there's virtually nothing in the "real life" that would consist from only one "core" activity. In fact, it's the opposite: you make some activity "pure" in order to master it, because in the real life there's so much that distracts you from mastering that one essential skill, whatever would it be. Plubmers and painters (and almost everyone else) must to be able to talk to others and to solve totally human-related problems in order to do their job. Person you would call sniper actually does much more running, crawling, waiting and hiding than shooting. Or does it really surprise anyone that policemen do tons of paperwork every day and not so much of that stuff they show in the movies? And somewhat opposite example: it's obvious that cook's work isn't only slicing vegetables, but have you ever seen how any real cook handles his knife? Please do, it's sight to behold. There's nothing "pure" in this world, no surprise here. So if it would be Yan LeCun writing something like this it would be justifiable (although I would be rather surprised to hear that), but in this particular case it's more like saying "Kaggle isn't important, data science is me!", in which case I find that NBA example to be OK. To summarise: "data science is more than building a model" — yes, "Machine learning isn't Kaggle competitions" — no.