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Comment by EGreg | Hacker News Reader

The comments here all sound like "this thing can never replace me". But if it's an inscrutable black box that performs better on the bottom line metrics (which is something AI can be trained for) then how is it different than all the other AIs that play chess better etc. but can't explain how they do it, because it's a strategy encoded in a million-dimensional vector space?

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Most real world business problems don't involve board positions and rules and winning moves as simple as chess or Go, and do involve a lot of people that need to be persuaded. So the ability to answer "why's that the winning move?" or "what would change that conclusion?" or "how confident are you are data is reliable enough?" is a lot more useful than a slightly better [over]fit.

Even with this example where it's analysing probabilities of heart attacks which can be [retrospectively] validated with precision, I absolutely do want the doctor who says "you're high risk, these are the main reasons why and how you mitigate them" than just a model with a lower false negative rate that can't tell me why.

In line with the other comments, let me give you a recent example from my work.

We had a dataset with just about 30 records and about twice as many features, about 1:3 class balance between positive and negative. Not an impossible situation at all, but there was a big premium on performance, so we threw the book at it.

Lo and behold, SVM with some particular parameters comes out on top. Ship it, right?

Not so fast, what decision did the model learn? Turns out, the model was learning decision boundaries that did not make sense. It was overfitting because the model did not know about what kinds of decision boundaries were allowable.

In the end, someone looked at a 2D scatter plot, drew a line and called it a day. It took a fraction of the effort and time of running through a box of models. It'll save the company $MM and it's trivially explainable to engineers and execs.

AutoML with blind application would've completely botched this.

Or you have the classic case where the model trains on the wrong things (e.g. the classic husky/wolf and snow case). One example I heard was in heart attack risk prediction from x-rays, the model was focusing on the present of stents in the x-rays. Which is a trivial prediction because the doctor already knows the patient has a stent, so the model is not adding anything of value.

No, I think the arguments you are reading is that this article was poorly written (granted its Ars) and a more appropriate article would have considered broader perspectives.

This feels like something an undergrad would write for their business management class...btw, your chess analogy is flawed in that there are millions of patterns that a computer can simulate for next play. I wouldn't really call it AI per se.

The problem is that the real job that the data scientist does is what the article suggests the business analyst is doing. The business analyst doesn't have a clue what they're doing, and training the ML model is not the difficult part.

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