You're taking the machine "learning" metaphor literally. A human being learning something is not analogous to training an ML model. Training models is more analogous to compilation or lossy encoding or compression.
If an algorithm of some sort scans a bunch of repos regarding video encoding and decoding and sees a lot of ffmpeg use, it might associate ffmpeg with video encoding and decoding, and decide to present some info about ffmpeg and a generic snippet to include ffmpeg as a library and initialize it if it associates the current project with that.
If I have perused a few encoding or decoding repos at some point and I think of the current project as having to do with encoding or decoding of video, I might immediately think ffmpeg even if I've never used it in a project as a library because I remembered seeing it in projects that used it, and look for some initialization code.
In what ways are these materially different? What makes the random conceptual associations in my head from what I've seen previously different than an algorithm that collects the same?
> Training models is more analogous to compilation or lossy encoding or compression.
And learning in people isn't? Isn't all knowledge transference in people analogous to lossy encoding and compression?
I don't know about you, but in college I don't remember regurgitating sections of "Advanced Programming in the UNIX Environment" to complete assignments, I remember studying it, internalizing parts of it on a conceptual level (as well as remembering specific fairly small chunks almost exactly), and using that to solve problems or answer questions or make associations.
I'm not saying ML and and learning in humans is the same. I do think for the very specific case presented here in how it's used, there are some parallels. Feel free to disabuse me of that notion if you have evidence that contradicts it though. I'm not wedded to that position, but I would want to see arguments to the contrary before abandoning it.