Nils Durner's Blog Ahas, Breadcrumbs, Coding Epiphanies

Exploring AI Learning Paths

I got this question:

I’m also interested in learning the framework of AI, do you think a dedicated Learning path should be valuable for all of us in Udemy on this topic? Covering examples: BASICS: Mathematics (Linear algebra, Calculus, Probability), Statistics, Programming, DEEP LEARNING: Neural Networks/DL (Neural networks, Computer Vision, NLP), MACHINE LEARNING: Supervised ML, Unsupervised & Reinforcement Learning”.

In the context of Generative AI, I absolutely agree with Ethan Mollick:

The lesson is that just using AI will teach you how to use AI. You can become a world expert in the application of AI to your domain by just using AI a lot until you figure out what it is good and bad at. This is one of two reasons that I dislike the emphasis on prompting that pervades much of the discussions of AI: it makes using AI systems seem much harder and more mysterious than it is. Just use it and see where that takes you.

(Long Blogpost)

(Also, I agree with our in-house Computer-Vision guys in that “AI” is a vast topic)

The topics you, dear submitter, mention are like learning how to program a historical computer like the PDP-11 when you actually are amazed by, say, the latest Microsoft .NET product announcements. In this case, PDP-11 programming would hold no deeper truth, no better understanding and no value beyond entertainment.