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It seems to be a joke of sorts: It's the original "They Write the Right Stuff"[1] with a find-and-replace job where "software" has been replaced with "AI" - alm

It seems to be a joke of sorts: It's the original "They Write the Right Stuff"[1] with a find-and-replace job where "software" has been replaced with "AI" - almost everywhere. [1] https://web.archive.org/web/20050830190246/www.fastcompany.com/magazine/06/writestuff.html https://web.archive.org/web/20050830190246/www.fastcompany.c...

yetihehe 6y ago
Yes, it's stated in last paragraphs of article. I've commented before I've read until end too, but removed my comment. And it's not joke, it's about where AI is now and where it should be. But you have to read until end.
- dh00608000 6y ago
  Thanks for reading until the end :-)
- masklinn 6y ago
  > And it's not joke Then it seems like the author completely missed that « they write the right stuff » remains a complete pipe dream throughout software development. > Looking at the result, it indeed seems like AI is going through what software went through 2-3 decades ago. That would be because AI is a coat of paint on software, and software has not significantly moved from where it was back then. It anything, it’s gotten worse on everything the essay covered.
  - cratermoon 6y ago
    AI - really Machine Learning - is "just" massively parallel software for using linear algebra on high-dimensional matrices representing huge data sets. Any programmer today can feed anything into the machine, but as the old saying goes, GIGO.
    - mklond 6y ago
      That every programmer today can build and train an ML model is one of the biggest advancements of ML engineering in the past 10 years. But as you say it's GIGO, the difficulty today is to know what to feed it and to know what that means for the real life performance. There are no great tools for that yet.
      - ska 6y ago
        > the difficulty today is to know what to feed it and to know what that means for the real life performance. This has always been the difficulty. Generalization is the fundamental problem in machine learning. Making easily available tools has led to an exponential growth in applications as more people play with it (many without understanding what they are doing or why), but predictably hasn't lead to an exponential growth in successful applications.
    - deleted 6y ago
      [deleted]

yetihehe 6y ago

Yes, it's stated in last paragraphs of article. I've commented before I've read until end too, but removed my comment. And it's not joke, it's about where AI is now and where it should be. But you have to read until end.

dh00608000 6y ago
Thanks for reading until the end :-)
masklinn 6y ago
> And it's not joke Then it seems like the author completely missed that « they write the right stuff » remains a complete pipe dream throughout software development. > Looking at the result, it indeed seems like AI is going through what software went through 2-3 decades ago. That would be because AI is a coat of paint on software, and software has not significantly moved from where it was back then. It anything, it’s gotten worse on everything the essay covered.
- cratermoon 6y ago
  AI - really Machine Learning - is "just" massively parallel software for using linear algebra on high-dimensional matrices representing huge data sets. Any programmer today can feed anything into the machine, but as the old saying goes, GIGO.
  - mklond 6y ago
    That every programmer today can build and train an ML model is one of the biggest advancements of ML engineering in the past 10 years. But as you say it's GIGO, the difficulty today is to know what to feed it and to know what that means for the real life performance. There are no great tools for that yet.
    - ska 6y ago
      > the difficulty today is to know what to feed it and to know what that means for the real life performance. This has always been the difficulty. Generalization is the fundamental problem in machine learning. Making easily available tools has led to an exponential growth in applications as more people play with it (many without understanding what they are doing or why), but predictably hasn't lead to an exponential growth in successful applications.
  - deleted 6y ago
    [deleted]