Ask HN: Is my career doomed if I don’t become an AI/ML Developer?

1) ML is still going to be a tiny niche in numbers of all the software things that need to be done, it's just a comparably new and trendy niche, so it gets talked about.

2) In most ML projects, the majority of the necessary code written has nothing to do with ML, it's all the data transformation, transformation pipeline, service backend, data management tools, etc etc; so in a multi-person ML project usually most developers don't touch the ML part.

I've been a professional web developer for 8 years. I don't have a C.S. degree either, though I did go through calculus (not really relevant). Honestly, I think it's far more important to be a solid programmer than learning the hype of the month. That's not to say you shouldn't keep up with developments in technology though. I think it's just a fad that will fade out. Sure, it sounds like you can do some cool stuff such as image generation or natural language processing. However, I think those applications are not as numerous as the posts on HN would lead one to believe. Not everything needs some magical AI to synergize the widgets.

No.

1997: Is my career doomed if I don't become a web developer? No.

2007: Is my career doomed if I don't become a mobile developer? No.

2017: Is my career doomed if I don't become an AI/ML developer? No.

2027: Is my career doomed if I don't become a/an [...]? No.

You know, being able to make a microprocessor from scratch and being able to supply the right instructions to get what you want -- these are different domains. As long as you can understanding what Machine Learning is _doing_ I think getting practice with some libraries and watching a lecture or two will give you the ability to play with and train them.

Basically, you have a training set of data that has "this is the data in" and "these are the outputs I expect." so if you are training something to learn the different animals of the animal kingdom, your data set might be portraits of giraffes and gorillas, and your outputs might be simple labels like "gorilla, snake, giraffe, tucan" etc. The neural net part of it is like the parameters you can hone. You can hone how many layers deep it is (try a layer for each dimension, like if you are tracking 5 facial features, you can try a network of roughly 5 layers). You have an "activation function" [usually sigmoid] because the _neurons_ sum their inputs and only fire on activation threshold (like your brain neurons).

Anyway, don't let the maths hold you back. When you can find a Python library on neural networks and play with it well enough to get results, you have pretty much figured out the puzzle. Not many people have deep intuition on how it works exactly, because the parameters (the "brain" of "neurons") is really an abstract mathematical object that is adjusted regressively over the course of learning [the training data].

Don't be discouraged, there is plenty of growth in software, and unless you are trying to help make a breakthrough in ML or "artifical intelligence" which, in my opinion, is something we have not even come close to touching, you don't really need to know the nitty gritty. The promising modern day results in ML came about because someone wanted to model a brain with a computer and ended up learning how to pattern match.

      (Results match Data?  Accept : Adjust)

Are my finances doomed if i didnt buy bitcoin at USD100 some years ago ?