If you have a sufficiently powerful/implementable automation method, any theoretical job they'd create by not being able to have it automated is just there until the time it takes for them (or for the job provider) to learn to have it automated, reaches.
McKinsey predicted that there'd be 900,000 cell phones by 2000.
If their automation prediction's error will be even a quarter as off as their cell phones', then we are not going to have enough people to have their jobs automated.
I know inference here is inductively fallacious and here we're talking about job categories not job numbers, but it just makes 49% feel like 98%.
1. How much power would we need? I'd assume a Thinkpad X230 won't do. If we need a moderately powerful system, do you provide AWS (or equivalent)?
2. I watch Siraj's videos. Although they are fun and useful, they are very short and resemble a recipe in a cookbook (no offense intended). Is the course going to be the same as videos? Or is it going to be discussing all the necessary mathematics/statistics?
3. I couldn't find detailed information about the instructors' background. Are they able answer questions from the deep learning book or Sutton&Barto, if one were to read them alongside the course?
What you're saying implies that a situation is -even- theoretically possible such that a radically new mathematical theory with near-completely novel underlying frameworks that's essentially developed by a single guy over the span of almost a decade that necessarily resulted in a deviation from the route focused by the majority of researchers, could be `quickly` understood/vetted by other mathematicians via traditional methods of un-automated peer-review process.
I guess if that was true, we would have been a significantly smarter species, or at a the very least, had god-like context-switching or learning abilities.