First of all, let me point out that this: > Most of my work seems to be boring CRUD work where the challenge is gluing libraries together or figuring out business requirements. Actual interesting technical problems seems to be mostly wrapped in ready-made libraries / SaaS services. ...isn't a problem. It's a sign of maturity of the field. In 2018 most problems that you can solve with a computer should have an off-the…
>Second, you've got to take into account that NN/ML/AI et. al. is going to "eat" a portion of all programming work within the next fifteen years or so. Are there any repos I should look at to learn this? How is this possible when even humans can't read sarcasm online? I'm genuinely blown that this is even possible?!?
"Software 2.0", Andrej Karpathy https://medium.com/@karpathy/software-2-0-a64152b37c35
> It turns out that a large portion of real-world problems have the property that it is significantly easier to collect the data (or more generally, identify a desirable behavior) than to explicitly write the program.
"Deep Learning is Eating Software", Pete Warden https://petewarden.com/2017/11/13/deep-learning-is-eating-so...
> The pattern is that there’s an existing software project doing data processing using explicit programming logic, and the team charged with maintaining it find they can replace it with a deep-learning-based solution.