AI Expert Roadmap

I'm off the main hubs, SF, London, NYC, but here, in my gamma level city people talk a bit about AI but I have yet to see applications that are not vaporware. Don't get me wrong, I love ML challenges, but the real world seems to have so many other little things to solve first, before we can finally realize our ai dreams.

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency.

If I were to give a recommendation, it would be this -- pick a topic/project that interests you, follow the classic knowledge-bootstrapping process where you read through papers and (hopefully) have an expert or trained person walk you through the specifics, then get hands-on instantly. Especially with something like fast.ai that values empirical results over hard theory, something understandably popular in the field.

From there I'd recommend branching out, but I'd use a JIT approach. The field isn't necessarily super well-founded at the moment, and while machine learning fundamentals are useful, ultimately it's a waste of time compared to the long-tail benefit of getting immediate empirical results and feedback hands-on.

Just my 2c, YMMV, and anyone is totally welcome to disagree as they wish! :)

Best of luck,

T

This should be renamed to "Intro to AI Roadmap" What's on this roadmap will get you in the door with some of the basics. In no way will it make you an expert.

I do appreciate they mandate paperswithcode.com

Things like Alexa or Google Translate already very much exist, use modern AI techniques, and are not vaporware. That's why so many corporate research labs - basically all the FAAMG companies - do AI work.