[cowmoo728]

We have to separate AI researcher and implementation engineer. These types of crash courses help get you to the point where you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas.

For many current applications of ML this is acceptable because you're just stealing an idea from a paper or stealing ImageNet to recognize your problem. For anything else you really need to pay up and fight with Google for a real expert.

[akmiller]

Exactly. Somewhat akin to graphics programming. There are much smaller groups that work on actually building 3D graphics engines, however, many developers take those engines and use them to build successful applications and games.

[stealthcat]

So we need to wait for Unity of ML? With Asset Store selling models and datasets.

[maxxxxx]

Will we reach a state where ML is as accessible for implementers as SQL databases? I still remember the time when databases were only for experts.

[infinite8s]

Yes hopefully. Take a look at BayesDB (and the underlying crosscat algorithm) and probabilistic programming.

[deviationblue]

>you can reasonably work under PhD level people and write code to test, scale, and deploy their ideas.

Which phd, though? All PhDs are not equal (see politics vs computer vision). Also, PhDs are hardly the holy grail of demonstrating capability, accuracy or intellect, especially given the reproducibility crisis, phds as a measure of any of those things should be used carefully.

[jebediah]

They are talking about PhDs in Machine Learning of course

[aje403]

They really don't. There is a link to the Wikipedia page for matrix multiplication. If these are the people you want to hire, you might as well start outsourcing or generating random numbers