[bonoboTP]

Depends on your baseline excitement. If you're so hyped up that you think it can classify bad and good prospective employees from a single photo, you should tone it down because that's nonsense. If you think it's all fluff, then you are also wrong. There are many great ML applications for constrained scenarios.

But this pearl counting does not require modern ML at all. It can be done with decades old image processing algorithms like Canny edge detection, Hough transform, thresholding, Hu moments etc. How reliably is another question. This kind of stuff is/used to be called "Machine Vision" (related to computer vision, but in hard industry they like to say machine vision).

[JackFr]

There’s also a technique called “weighing”.

It’s a transformation which, by assuming the items are identical, turns quantity from a discrete value to a continuous one with some loss of precision. In many cases measurement times can be reduced my more than 99%.

[matsemann]

Cut the snark. And if you check the link, the assumption that items are identical is far off.

[JackFr]

Yeah I know. And the use cases of people on a beach, crowded theater, trees in the woods, etc. don't lend themselves to weighing.

Honestly didn't intend to come off as snarky, just clever and amusing. Tough to convey tone -- risk I take I guess. Oh well.

[carlmr]

I think you're not wrong. Even if the pearls aren't identical, if you weigh enough of them at once the central limit theorem will be on your side.

[TchoBeer]

Maybe I misunderstand, but I don't think you meant central limit theorem.

[carlmr]

I did indeed. Your pearls might be heterogenous, but they will probably have an average weight that doesn't change much over time. The central limit theorem makes it possible to estimate quite accurately how much you have of a sum quantity including having a somewhat known accuracy of that estimate, since you get a nice normal distribution with known mean and variance.