What sort of heuristics do you have when weighing up learning a new technology?

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#1

I have become more and more fixed in my tech preferences (static FP ftw).

But, try to keep a cynicism / naivety balance.

What sort of heuristics do you have when weighing up learning a new technology in your free time / for fun?

For example, I am loathe to look into deep learning - the tech doesn’t appeal (hacky Python) the sales pitch (better statistics) is questionable (too long to go into here).

And yet, of course, I should probably make time for it. Just too cynical…


#2

For me the heuristic for really learning something is “Will it be useful for the ideas I have?”. Otherwise I might be interested in how things work in general to understand it’s usefulness and/or backdraws, but I hardly go into more detail or implement stuff in that area.


#3

I see two distinct cases here:

Exploratory learning
Imagine an infant saying: Well, it might be cool to learn to walk now. First, the child could never know where the ability to walk will take it. Second, the choice to learn things that transcend one’s horizons/knowledge cannot be based on what’s in it form me. An example of things that fall into this category for me was discovering erts/Elixir and AI employing genetic algorithms. The amount of need for exploratory learning and the inner drive behind it is highly individual, of course.

In-depth learning
When I know the desired outcome, for me, it boils down to: what do I need to learn in order to achieve goal X? Once the knowledge seems unfit for fulfilling such goal: rinse and repeat. I believe it was in The Gay Science where Nietzsche talked about a savant who dedicated his life to the study of a part of a worm’s brain. When such knowledge is not practical nor expands one’s horizons and basically just uses up more memory, then, I would channel the energy elsewhere. It might be just another shape the procrastination took on.

Having such question, I am surprised you loathe neural networks-related knowledge (or is it just deep learning/Tensor Flow crap?). I would look into AI parts where fitness/reward function is mentioned. There you might get answers on a theoretical level.


#4

Yeh, shallow learning / dilettantism is probably underrated (and most of all, keeping an open mind)


#5

I really like the way you categorise.

Suspect, as adults we become too goal oriented… Need to avoid that.

I come from a stats background, and I am interested in FP.

The mix of crappy programming and dodgy ML results have put me off the field. But perhaps you just need some knowledge at first, to filter in the good stuff (as in filtering away OO things, in favour of FP etc…)