[tanilama]

Just trying to get something started first. Fast.ai or deeplearning.ai is not a bad start.

However, I would want to put into notice that, AI/ML field is very competitive, and there is tendency to hire people with PhDs, and for now it is big companies' game only. It won't create that big of an appetite to accumulate so many people, like what web development did.

[cm2187]

I would have assumed that AI/ML would be increasingly used in mundane tasks. Like quality control for small factories. You don't need PhDs, you just need people who are familiar with some large commonly used APIs. Very much today like a web developer doesn't really need to know what a stack & heap is (and more often than not doesn't) to earn a living.

[edit] Or kind of like cryptography. Thanks god we don't need to understand the underlying algorithms to apply them to real world applications. Just having a high level understanding of what's going on inside is enough.

[tanilama]

Your vision might come true. However there is catch. ML/DL right now, doesn't have good abstractions, the existing ones are to some extent all leaky.

That is why ML as an hands-free service, just like what a database is, doesn't work. To my surprise, I would say, currently ML/AI is a quite manual thing to get right, and it requires constant attention, not just one time effort, since the data is ever changing.

AutoML might be a solution to this, with the help of a working HPO solution, but both are not really public accessible at this point, requires long time and big computation resource.

[_pmf_]

> I would have assumed that AI/ML would be increasingly used in mundane tasks. Like quality control for small factories. You don't need PhDs, you just need people who are familiar with some large commonly used APIs.

CV has been common in SMD pick-and-place machines for 15 (probably 20, 25) years. For industrial applications, hardware size and price essentially do not matter, so the new AI approaches do not bring anything fundamentally new to the table (industrial solution vendors will eventually integrate smaller and cheaper solutions, but price is just not a huge discriminator here). What's interesting is the scaling down that is happening and making AI viable for consumer applications where budgets and device size are restricted.

[cm2187]

But also smaller companies, who cannot afford the sort of machinery a General Electric can.

[_pmf_]

That's a good point. With the kind of equipment I was talking about, small shops won't even be able to talk to a sales rep.