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 in

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.