[rabidrat]

> learning ML does not make you forget what you already know

I often feel that learning something new takes such energy and mental rearrangement that it does crowd out what I already "know". The brain is a neural network whose weights are constantly being adjusted, just because you learned something at one point does not mean it is permanently there.

For example, I spent many years working in networking, but now that I've been out of that field for several years and working in embedded firmware and data, I would have to relearn much of what I "knew" in my old field in order to be professionally productive. And that's apart from the field itself advancing in ways I haven't kept up with.

It's like riding a reverse-steering bicycle. By learning something that's in direct competition with your existing neural structures, your neural structures change and you no longer "know" what you used to. Jump to 5:10 to see this person, who took 8 months to learn to ride a reverse bicycle, try to ride a straight bicycle: https://ed.ted.com/featured/bf2mRAfC

[sailingparrot]

> but now that I've been out of that field for several years and working in embedded firmware and data, I would have to relearn much of what I "knew" in my old

This seems to me this is mostly caused by being out of the field for so long, not because you did something else while being out.

[perl4ever]

That seems like a distinction without a difference. The reason time erases skills is because you're always doing stuff, and over a longer period of time, you've done more stuff, which has reprogrammed your brain.