Check out epoch.ai. You won't get a direct answer but it'll give you directional, and useful data points.

I think the frameworks became complex over time but were initially simpler because of the more managable set of AI workloads.

There might be but instead of thinking about it as a binary, think about it as a continuous conviction curve. Ask yourself, if this idea is true, what's the smallest testable prediction you can make? It can mean running a small experiment that doesn't give you statistical significance or would pass scientific scrutiny, but can be a gating function for a slightly more sophisticated experiment. And then you rinse and repeat until you prove/disprove the idea.

Not the first product category you think of when it comes to AI consumer hardware but how about smart glasses (eg. Meta Raybans). Extrapolating to the future, AR glasses are probably going to be heavily dependent on AI.

Great recommendation. Based on the ToC, its similar to AIMA.

Andrew Ng's course is great for the learning NN's from scratch, but not understanding how NN's fit in the broader discipline of AI.

I've been your position before and although I can't say I'm a successful entrepreneur, I can share lessons that helped me escape "analysis paralysis":

1. Fear. This is was a huge inhibitor of action. I was afraid of picking the wrong problem and then spending months-years having nothing to show for it.

2. To overcome the fear, I decided that instead of anchoring on the painpoint, I'll anchor on something else: The user. I chose ML engineers as the market I want to serve (its a terrible market, I advise you pick something else). It's hard to fathom a niche of users out there that don't have some pain they're willing to relieve by paying somebody else. You don't have to anchor on a user. You can anchor on something else, like a mission (eg. democratizing access to startup investing), or an industry (eg. semiconductor manufacturing). When you commit to a center point, now you have the freedom to iterate on ideas freely, knowing that even if an idea doesn't work out, you'll learn useful information you can use in the next iteration.

Does this guarantee that you'll company eventually grow into a unicorn? No, not really. You can end up picking a tiny niche, but in practice, most founders are able to expand the niche or find ways of expanding their market by combining niches.

This is more relevant to software businesses but hopefully some of it is still useful for other types of businesses.

> *but in practice, most founders are able to expand the niche or find ways of expanding their market by combining niches.+

“Most” founders fail miserably or toil in obscurity making peanuts until they give up.

True true. I should qualify my statement: Most founders who are able to find micro-PMF in a niche generally can expand that niche or grow threw composition. But this is based on my own anecdotal evidence, with a slant towards optimism.

No, but there is a PDF version, ahem, floating around on Reddit.

Was not aware of these resources. Thanks for sharing!

Will check out Discrete Mathematics, thanks!