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True that. But you could replace "Machine Learning System" with "code base" and "linear algebra" with "C++" and it would be an accurate description of most corp

True that. But you could replace "Machine Learning System" with "code base" and "linear algebra" with "C++" and it would be an accurate description of most corporate software development I've seen.

H4CK3RM4N 9y ago
I think this is one of Munroe's general digs at corporate culture, and not specifically aimed at machine learning, that's just the current buzzword.
- TeMPOraL 9y ago
  It feels like a dig at how people do machine learning these days though. My impression is that people just take TensorFlow and a random neural net, and keep throwing gigabytes of data at it until they feel the results look like they should...
  - sverhagen 9y ago
    Courtesy of "Machine Learning in $(some single digit number) Minutes".
  - TheOtherHobbes 9y ago
    Random Neural Nets should totally be a real thing. I mean more than they already are.
    - mpweiher 9y ago
      At University, we did neural nets via genetic algorithms. Does that count?
      - majewsky 9y ago
        Bingo. :)
    - tincholio 9y ago
      They are, actually: https://en.wikipedia.org/wiki/Random_neural_network https://en.wikipedia.org/wiki/Random_neural_network
    - visarga 9y ago
      > Random Neural Nets should totally be a real thing. Reservoir computing. Some are critical of this method.
    - tgb 9y ago
      There's a long history of that: http://catb.org/jargon/html/koans.html#id3141241 http://catb.org/jargon/html/koans.html#id3141241
s_kilk 9y ago
Eh, we (collectively) have a pretty good grip on how code-bases, linear algebra and C++ work, but we still have no idea how minds and intelligence work. And so the best we can do with ML right now is stumble blindly in the dark, poking and prodding things until it 'seems' to give the right answers, some of the time.
bjoernbu 9y ago
Imho, it's more typical of machine learning. Sure, both can be very similar and are often signs of a lack of deeper understanding. I'm no ML expert by any means, but I've seen several bachelor/master thesis and even ML competitions where ensembles performed best. Sure, this isn't necessarily aimless stirring and could combine models that really capture different aspects of the data. But often enough it's just several algorithms that do the same general thing, combined to achieve a slightly higher score. Imho this is most relevant when competitions provide data that is not readable by humans (e.g. simplified: "classify these documents where all words are given as word IDs and never as actual strings"). To me this has a touch of pouring in data, stirring (build many classifiers and plug them together in an ensemble), and getting answers on the right side. Optimizing hyper parameters goes in a similar direction, imho. I can really see an analogy to stirring
marcosdumay 9y ago
You don't throw random C++ code at the compiler to see if you get the correct result. At least, most of the time you don't.

True that. But you could replace "Machine Learning System" with "code base" and "linear algebra" with "C++" and it would be an accurate description of most corporate software development I've seen.

H4CK3RM4N 9y ago

I think this is one of Munroe's general digs at corporate culture, and not specifically aimed at machine learning, that's just the current buzzword.

TeMPOraL 9y ago
It feels like a dig at how people do machine learning these days though. My impression is that people just take TensorFlow and a random neural net, and keep throwing gigabytes of data at it until they feel the results look like they should...
- sverhagen 9y ago
  Courtesy of "Machine Learning in $(some single digit number) Minutes".
- TheOtherHobbes 9y ago
  Random Neural Nets should totally be a real thing. I mean more than they already are.
  - mpweiher 9y ago
    At University, we did neural nets via genetic algorithms. Does that count?
    - majewsky 9y ago
      Bingo. :)
  - tincholio 9y ago
    They are, actually: https://en.wikipedia.org/wiki/Random_neural_network https://en.wikipedia.org/wiki/Random_neural_network
  - visarga 9y ago
    > Random Neural Nets should totally be a real thing. Reservoir computing. Some are critical of this method.
  - tgb 9y ago
    There's a long history of that: http://catb.org/jargon/html/koans.html#id3141241 http://catb.org/jargon/html/koans.html#id3141241

s_kilk 9y ago

Eh, we (collectively) have a pretty good grip on how code-bases, linear algebra and C++ work, but we still have no idea how minds and intelligence work. And so the best we can do with ML right now is stumble blindly in the dark, poking and prodding things until it 'seems' to give the right answers, some of the time.

bjoernbu 9y ago

Imho, it's more typical of machine learning. Sure, both can be very similar and are often signs of a lack of deeper understanding. I'm no ML expert by any means, but I've seen several bachelor/master thesis and even ML competitions where ensembles performed best. Sure, this isn't necessarily aimless stirring and could combine models that really capture different aspects of the data. But often enough it's just several algorithms that do the same general thing, combined to achieve a slightly higher score. Imho this is most relevant when competitions provide data that is not readable by humans (e.g. simplified: "classify these documents where all words are given as word IDs and never as actual strings"). To me this has a touch of pouring in data, stirring (build many classifiers and plug them together in an ensemble), and getting answers on the right side. Optimizing hyper parameters goes in a similar direction, imho. I can really see an analogy to stirring

marcosdumay 9y ago

You don't throw random C++ code at the compiler to see if you get the correct result. At least, most of the time you don't.