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Don't you think people are, sometimes, just applying ML to their problem "because of hype" ? One example I have in mind, was a contest where participants were

by Fiahil 9y ago

Don't you think people are, sometimes, just applying ML to their problem "because of hype" ? One example I have in mind, was a contest where participants were given a series of satellite pictures and asked to write a classifier to detect icebergs and cargo ships (the two are quite similar). As someone else pointed out, trying to use classical computer vision and machine learning on these images will always have some error rate during identification. However, if we were able to extract speed and trajectory of all objects in the picture and mixing them with AIS data, finding which ones are ships, which ones are giant pieces of ice, and which one are non-moving structures to be avoided, becomes easy. So, you have to choose between a black box that will give you potential results with a given error-rate, and a predictable algorithm that anyone can audit. Seems like a no-brainer situation to me. For what other reason would you choose the first solution, except hype-related decisions ?

cm2187 9y ago
The thing is if you know exactly what you are looking for, like in your example, or a QR code, or a barcode, it makes sense to tailor an algorithm. But you may not want to have to maintain a complex algorithm every time a small change happens (say new kind of ships appear). Or you might want a generic approach (recognise any objects, including objects that did not even exist at the time the code is written, but will appear in the data). In such case I can see ML being a good choice.
nerdponx 9y ago
You wouldn't, and any data scientist worth their salt would recommend that the business choose the latter option.
radarsat1 9y ago
Your comparison seems like a false dichotomy, and I think you are agreeing with OP. OP says, spend less time worrying about the algorithm and more time worrying about what data you are feeding the algorithm. You are saying, what if you had to choose between dataset A with algorithm A and dataset B with algorithm B. You claim, (probably correctly) that dataset B, which includes velocity and trajectory, is more correct for the problem at hand, and given dataset B, I would suggest that either algorithm A or B would probably do just fine. You also claim that algorithm A has "some error rate during identification." But so will algorithm B, and so will either algorithm on dataset A and B! The question you should ask is, how much do I care about "black box" vs. "white box", and is there are trade-off? If the black-box solution (algorithm A, the "ML" solution) gives you 10% higher accuracy, and that accuracy is going to save lives, you bet I'd choose it. Or maybe I decide that interpretability is really important due to external audit reasons, so I need the white-box solution. But maybe I'd choose both, the interpretable one, and use the uninterpretable one as a flag for "a human should look at this." Or maybe I'd combine the results of both algorithms to get even higher accuracy. There are just so many ways to configure a solution to the problem you propose, and you are only distinguishing between two of them. In the end the appropriate choice depends on context.

by Fiahil 9y ago

cm2187 9y ago

The thing is if you know exactly what you are looking for, like in your example, or a QR code, or a barcode, it makes sense to tailor an algorithm. But you may not want to have to maintain a complex algorithm every time a small change happens (say new kind of ships appear). Or you might want a generic approach (recognise any objects, including objects that did not even exist at the time the code is written, but will appear in the data). In such case I can see ML being a good choice.

nerdponx 9y ago

You wouldn't, and any data scientist worth their salt would recommend that the business choose the latter option.

radarsat1 9y ago

Your comparison seems like a false dichotomy, and I think you are agreeing with OP. OP says, spend less time worrying about the algorithm and more time worrying about what data you are feeding the algorithm. You are saying, what if you had to choose between dataset A with algorithm A and dataset B with algorithm B. You claim, (probably correctly) that dataset B, which includes velocity and trajectory, is more correct for the problem at hand, and given dataset B, I would suggest that either algorithm A or B would probably do just fine. You also claim that algorithm A has "some error rate during identification." But so will algorithm B, and so will either algorithm on dataset A and B! The question you should ask is, how much do I care about "black box" vs. "white box", and is there are trade-off? If the black-box solution (algorithm A, the "ML" solution) gives you 10% higher accuracy, and that accuracy is going to save lives, you bet I'd choose it. Or maybe I decide that interpretability is really important due to external audit reasons, so I need the white-box solution. But maybe I'd choose both, the interpretable one, and use the uninterpretable one as a flag for "a human should look at this." Or maybe I'd combine the results of both algorithms to get even higher accuracy. There are just so many ways to configure a solution to the problem you propose, and you are only distinguishing between two of them. In the end the appropriate choice depends on context.