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.
And it's not joke, it's about where AI is now and where it should be. But you have to read until end.
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.
Any programmer today can feed anything into the machine, but as the old saying goes, GIGO.
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.
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.