Supervised learning trains a model using labeled data, while unsupervised learning only learns from unlabeled data.
I think this is one of the sentences that I feel confused about. I feel like labeling is in some sense “cheating” or trading off efficiency vs. generality.
But learning to guess the teacher’s password is exactly what supervised learning is all about. It’s such a popular technique precisely because it converges so quickly on generating correct answers, even though it’s often at the expense of learning spurious correlations. That’s why these models tend to reproduce human biases and prejudices that exist in the training data. Learning the true causal structure of reality is a much harder problem.
I think this is one of the sentences that I feel confused about. I feel like labeling is in some sense “cheating” or trading off efficiency vs. generality.
But learning to guess the teacher’s password is exactly what supervised learning is all about. It’s such a popular technique precisely because it converges so quickly on generating correct answers, even though it’s often at the expense of learning spurious correlations. That’s why these models tend to reproduce human biases and prejudices that exist in the training data. Learning the true causal structure of reality is a much harder problem.
Might depend on the labeling, though. Like, a good math class is well-structured data intended to kick-start generalization for the learner.