To transition from practitioner to engineer, one will need to know low level details including directly relevant math and popular prior art. One of the distinguishing difference between a practitioner and an engineer is that later needs to be able do systematic debugging to identify and fix the problem. I often ask folks during interviews how they would go about when their ML model to do X doesn't work as expected. This usually reveals the difference between practitioner and engineer with high accuracy.

To transition from engineer to scientist, one needs to know much broader body of prior art and beyond directly related math. A typical interview that would differentiate between engineer and researcher would be to discuss some known unsolved problem and see if the person can elaborate details of prior art, opinions of top researchers, wins achieved so far and directions for future efforts.

This is again not to say that practitioners can't do debugging OR that engineers can't make ground breaking innovations. Faraday, Edison, Wright Brothers - all either started out as practitioners or engineers and successfully attacked some of the hardest unsolved problems of their time, never obtaining formal academic credentials. On the other end far many more academics have broken new grounds then non-academics. At the end of the day its all probabilities. Claiming that PhD is no longer required to do "AI" doesn't reveal all these subtlety and marginalizes tools and foundation that PhD equips oneself on this journey.

I think even that level of knowledge is not always necessary. Often, just having the intuitive understanding can be enough to get great results e.g: understand that this is a black box that takes features in, predicts things, and you have to help it by giving good features; or understand that word2vec builds vector representations based on words that co-occur in the same context.

preview: https://minireference.com/static/excerpts/noBSguide2LA_previ... condensed 4 page tutorial: https://minireference.com/static/tutorials/linear_algebra_in... reviews on amazon: https://www.amazon.com/dp/0992001021/noBSLA

If you don't want to bother with pen-and-paper (which is the best, but takes very long), you should at least try some problems using the computer-assisted approach. See samples solutions to first few chapters: https://github.com/minireference/noBSLAnotebooks

Extremely basic. Anyone with an undergrad engineering degree will have covered what's needed for the course. His course was almost patronizing, calling people who knew the basics of calculus as "advanced students".

Throwing X at Y and seeing what sticks is literally how life got from single-cell organisms to "The Apprentice".

We'll keep doing it that way, it's going to be fantastic.

For any arbitrary range of values for $DATA_TO_COLLECT and $ML_MODEL? Maybe not. For many real world scenarios? I'd say yes. Heck, sometimes the data you have available simply "is what it is" and there is no question of "what data to best collect".

Let me also add re-iterate that learning to use ML is a continuum... and I would not posit that one could take Andrew's class, or the fast.ai class, and claim to be "done" with their ML education. To me either (or both) of those is just step one on a journey that could continue more or less forever (or until ASI comes along and enslaves the human race, ala The Matrix).

Personally I took Andrew's (original) Coursera course, but since then I've continued to study Linear Algebra and Calculus through other forums, have been working through several other ML books, and have a whole litany of stuff queued up to study, up to and including the aforementioned Measure Theory, Complex Analysis, etc. I definitely value that stuff, I just think you can accomplish some useful tasks before you get to that level.

Once you get your hands dirty, and need to debug unexpected issues, understanding what happens under the hood becomes more important.

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I had someone contact me for a "consulting" position, to replace their rule-based insurance claim denial system, with "ML". Contract-work, nobody in-house to review its performance, no insight to the problems of the domain and they wanted to throw ML at it to "solve their problem".

When I refused, and pointed out that it would be highly irresponsible, to "contract-out" this type of work, particularly given its life-or-death implications; the CEO got angry. Told me; its "post-claims processing" and it isn't life or death. To which I told him bullshit; your denial of claims is going to directly influence how doctors practice their treatment. There is a direct feedback cycle. The fact that you don't see it, makes it even more dangerous.

They simply, didn't understand, how HIGHLY inappropriate it was to just throw random ML at a problem, particularly as a one-off consulting project (and no in-house expertise).

^ thats real-world.

Personally I don't think you should be allowed anywhere near ML, UNLESS you have that PHD in computer science. I don't even think you should be allowed to HIRE people for ML until you fully understand the hazards with letting a computer control critical decisions.

So yeah, with respect to your distinction of "ML research" and "applied ML".

"NO."

A lot of ML research is done by PhDs in other fields. I did research that focused on developing compact group invariant features (for neural networks) for predicting local atomic energies in materials science, and a few mathematicians I follow did work on developing convolutional neural networks that utilize Clebsch–Gordan coefficients to generalize the translational invariance to other symmetries.

On the contrary, a lot of CS machine learning research is heavily application focused (generate such-and-such new thing using a GAN). If anything, mathematicians are the ones who understand machine learning at the very deepest level of theory. This isn't to say there aren't many theory-focused CS presentations/publications; I'm just refuting your point that highly theoretical machine learning research is purely the domain of CS PhDs.

Sure, but most problems aren't life-or-death. They're mundane problems related to improving a business process for a widget manufacturer, etc.

I rather have people have a PhD in logic or ethics, so they hopefully wont make for example racist programs without even thinking about it. Unfortunately, so far i dont have a lot of faith in computer science as a field when it comes to ethics, as it's all about the technical challenge. That something can be build does not mean it should be build.

Interesting. I don't see anything in the story above that supports that position. There's nothing about the scenario you described that would be affected by having, or not having, a Phd in CS. If anything, as somebody else pointed out, this is more of question of philosophy or ethics.

It's also a pretty niche example, which is not representative of the kinds of things that ML can be used for. If you want somebody who work on pricing optimization or a recommender system for your e-commerce site, you really don't need somebody who's doing cutting edge ML research.