How do I get onboard the ML/AI bus? Or do I wait for these smart people to figure it out and then I'll just call their API?
The problems to solve are many, and relatively few people are actually looking at the enterprise market because the challenges associated with big consumer data are obvious, profitable, and data is widely available for research.
The high dollar hires right now are primarily people with masters and phds that are highly relevant, but that will change in short order IMO like the whole market did in the mid-90s as markets grew out of nowhere. I think in three years - after we have a bit of a slump and nobody is investing in mobile apps or IOT anymore - we'll see a real rise in AI workflows that apply to mid-size enterprises.
To get ahead of the game, start paying attention. Take the suite of AI courses from coursera, fast.ai, etc. Participate in Kaggle. Then find a job that is loosely related and allows you to keep pace. Three to five years down the road you'll be leading the way in a gigantic shift in the enterprise market.
It's worth paying attention to distributed computing as well to understand how the whole data pipeline comes together. Not everybody will be structured the same way, but constantly changing large datasets are valuable and there are only so many ways to handle them.
I'm sure a lot of ML specialists are good coders, but they don't have a lifetime of experience in building production level software. Left to their own devices, they are going to be forced to re-invent the wheel over and over again, wasting time on over-engineered and under-utilized tools. There's value in being the guy who understands ML, and understands development but isn't an expert in linear algebra. There isn't a giant market for people like that at this point, but there is a market.
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.
[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.
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.
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.
See all of his answers here: https://www.quora.com/profile/Andrew-Ng
I like this one of his answers: for people with intermediate skills, read an ML paper and try to reproduce it, or use it with different/your own datasets. This is completely underrated.
I for one want to do this more.
Spend a very small amount of money and start experimenting with what AWS/Azure/Google/etc have made available, following any number of the solid tutorials available. You could run your experiments locally, however assuming eg $5 or $10 per month isn't a big deal to you, playing in their clouds will provide a more realistic use context whether you're building something for yourself in the future or if you're employed in that field.
Github implementation of current hot papers seems like a good approach. A lot of papers don't come with source code and creating source is useful thing to community.
Blogs explaining the mechanics of a paper.
A graduate degree in CS or Math might help.
If you could do a series of blogs that together are enough to introduce a new person, that could get interest.
Sometimes imitation is the best way to learn. So if there are some great resources for source codes being derived from papers, it will help people how to read papers and create code out of it.
There's also been a revival of reinforcement learning, especially when used together with neural nets ("deep reinforcement learning"), and again there have been many small advances that collectively make this work very well. This is the technology that powered Google's AlphaGo to beat the world champion at the board game of "Go", not to mention learning how to play many arcade games at beyond human level based only on the raw pixels and current score as input.
There have also been tremendous strides in AI hype leading folk to fear the robot uprising based on these more mundane machine-learning/neural-net breakthroughs!
1.Dropout and its variations. Widely used in both vision and NLP
2.BatchNormalization and its variations.
3.Inception Style Cell.
4.Residual/Skip connections.
5.Better optimizers RMSProp/Adam.
The bigger news is actually the paradigm shift. Representation learning with gradient descent swarms the whole ML field, and becomes the new norm. End-to-end learning is vastly accepted and preferred.
As to GAN, it is very exciting in research, and has the potential to make itself a bigger deal than the previous listed advancements combined, under the condition we can make it works on sequence as well as on images, for now, it doesn't make a practical impact in applications.
So that's an advancement... I guess.