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Something to ponder… The world was all model-based analysis, and then neural networks came along. Model-based analysis-which requires a thorough understanding

Something to ponder… The world was all model-based analysis, and then neural networks came along. Model-based analysis-which requires a thorough understanding of the model mathematics-went out the window. Now, we let the network develop its own model, but it’s based on the data that is used to train it. But what if that data is biased? What if the data doesn’t contain some necessary “truth?” I feel that I t’s like driving a car looking out of the rear window to see where you have been. This can’t be good.

PeterisP 4y ago
It's not news in ML (machine learning) that whenever you do have a reasonable understanding/model of the underlying task and an algorithm for solving it, you should use that and not try to apply ML to learn a model from data. If an acceptable non-ML solution exists, then usually nobody reasonable would suggest using ML (unless they just want to pad their resume) because the known algorithm will usually do the same thing with much less computation and more reliability and interpretability. However, there are many, many useful real world tasks where we haven't been able to do that (e.g. almost all of image analysis, image generation, a significant part of of text analysis and generation tasks), so for these tasks the (valid) criticism doesn't really matter, as we don't have any reasonable alternatives - if let the network develop its own model, we get a somewhat working solution (with all the risks you mention), and if we don't, then we best we can make is unusably bad. If all the windows in your car are painted opaque black except the rear window, then driving the car by looking out of the rear window to see where you have been can be as good as it gets.
- willy_k 4y ago
  However, for the sake of argument, it would probably be better to wait until you can see out the front, otherwise you’re pretty likely to cause an accident. (My personal opinion is that of yours to, that the good from what we have now outweighs the potential bad).
- nerdponx 4y ago
  It's also not a binary choice between "ML" or "hand-crafted model" either. The most successful applications of ML in my own career have always been judiciously building small task-specific models that fit inside a broader system. And there is also usually quite a lot of consideration being given to the model input data as well. Similarly, I have never seen a "just throw a model at it" project succeed in a finite amount of time.
mrkeen 4y ago
The classic approach is a human generating a function from test input data to test output data. The ML approach is a machine generating a function from test input data to test output data. Both approaches will be compromised by biased data. You could argue that you don't really need the data in the classic approach, if the human knows what he's doing. But that's kind of like writing code without writing tests. I'm not arguing for or against tests here, but perhaps you'd just be shifting the source of the bias from the data to the human doing the coding? > What if the data doesn’t contain some necessary “truth?” This is my gut reaction to a lot of data-driven-crap. "We increased our customer acquisition by-" No you didn't. "Customers are 15% more likely to-". Nope. > I feel that it's like driving a car looking out of the rear window I don't think this an apt metaphor here for ML in general, but I think it could apply to other HNer's utopian vision of ML, e.g. once GPT is "good enough", we'll be able to throw away the real world data and use GPT to train more models.

PeterisP 4y ago

It's not news in ML (machine learning) that whenever you do have a reasonable understanding/model of the underlying task and an algorithm for solving it, you should use that and not try to apply ML to learn a model from data. If an acceptable non-ML solution exists, then usually nobody reasonable would suggest using ML (unless they just want to pad their resume) because the known algorithm will usually do the same thing with much less computation and more reliability and interpretability. However, there are many, many useful real world tasks where we haven't been able to do that (e.g. almost all of image analysis, image generation, a significant part of of text analysis and generation tasks), so for these tasks the (valid) criticism doesn't really matter, as we don't have any reasonable alternatives - if let the network develop its own model, we get a somewhat working solution (with all the risks you mention), and if we don't, then we best we can make is unusably bad. If all the windows in your car are painted opaque black except the rear window, then driving the car by looking out of the rear window to see where you have been can be as good as it gets.

willy_k 4y ago
However, for the sake of argument, it would probably be better to wait until you can see out the front, otherwise you’re pretty likely to cause an accident. (My personal opinion is that of yours to, that the good from what we have now outweighs the potential bad).
nerdponx 4y ago
It's also not a binary choice between "ML" or "hand-crafted model" either. The most successful applications of ML in my own career have always been judiciously building small task-specific models that fit inside a broader system. And there is also usually quite a lot of consideration being given to the model input data as well. Similarly, I have never seen a "just throw a model at it" project succeed in a finite amount of time.

mrkeen 4y ago

The classic approach is a human generating a function from test input data to test output data. The ML approach is a machine generating a function from test input data to test output data. Both approaches will be compromised by biased data. You could argue that you don't really need the data in the classic approach, if the human knows what he's doing. But that's kind of like writing code without writing tests. I'm not arguing for or against tests here, but perhaps you'd just be shifting the source of the bias from the data to the human doing the coding? > What if the data doesn’t contain some necessary “truth?” This is my gut reaction to a lot of data-driven-crap. "We increased our customer acquisition by-" No you didn't. "Customers are 15% more likely to-". Nope. > I feel that it's like driving a car looking out of the rear window I don't think this an apt metaphor here for ML in general, but I think it could apply to other HNer's utopian vision of ML, e.g. once GPT is "good enough", we'll be able to throw away the real world data and use GPT to train more models.