[gchadwick]

> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem.

How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though? Certainly the person who does have the masters/PhD and a few years of applying that to real-world ML problems will have the edge but an experienced developer who's got a knack for maths (though no direct ML experience) may be able to get up to speed quicker than you think. Part of that will be experience with knowing how and when to ask the right questions when you get stuck.

[achompas]

> How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though?

It's both, right? You pick up problem-solving techniques as a researcher or engineer; as the former, those techniques lean towards scientific problems. Your average engineer doesn't need to know about contrasting.

Again: it's possible to learn the necessary math in your spare time! I agree!! However, it's far easier to do it in a graduate program as a full-time job for 2-5+ years.

[infinite8s]

The knack for maths is the important bit.

[sannee]

The math necessary for ML/AI (statistics/vector calculus) is mostly taught at undergrad level though isn't it? So most engineers should already have it covered.