>> Arguably, that is equivalent to saying the real problem is still unsupervised/semi-supervised learning.
I don't agree because data pre-processing and labeling are two distinct parts of the pipeline and you can totally have one without the other.
And there's more to it than that. Currently we have to provide the context for an algorithm to learn. We do this by selecting training examples. Whether these examples are labelled or not, they are only a small part of the world we wish the algorithm to learn about.
You don't even have to go as far as the wider physical world to see this in action. In any training context, if your training set is missing a category of entities, Y, then your algorithm will never model Y. It doesn't make any difference if your model is trained in a supervised manner or not. What matters is that there is a part of the world that it hasn't seen.
I guess you can say that humans don't have a way to learn this way either, but human learning has a big advantage: we need very little data and very little training to incorporate new knowledge and our context of a world is very broad to begin with. It's at once broad, specialised, robust and flexible. We're a bit scary if you think about it.
Which leads me to believe that the limitation of our machine learning algorithms is not in the labeling, or even in the data pre-processing but in some fundamental aspect of building a context from examples only. There's something missing and it's not something we know about (hah!). The missing part means that you can learn from examples until the heat death of the universe and there will still be an infinity of things you don't know anything about- and that are potentially part of your immediate environment.
Obviously, removing the need for pre-processing will make things much cheaper and there will be progress, ditto removing the need for supervision. But it won't get us anywhere nearer human learning, despite people's best wishes, because we're missing a part of the puzzle that's a whole other ball game.
(and which I obviously don't claim to have any idea about)
I don't agree because data pre-processing and labeling are two distinct parts of the pipeline and you can totally have one without the other.
And there's more to it than that. Currently we have to provide the context for an algorithm to learn. We do this by selecting training examples. Whether these examples are labelled or not, they are only a small part of the world we wish the algorithm to learn about.
You don't even have to go as far as the wider physical world to see this in action. In any training context, if your training set is missing a category of entities, Y, then your algorithm will never model Y. It doesn't make any difference if your model is trained in a supervised manner or not. What matters is that there is a part of the world that it hasn't seen.
I guess you can say that humans don't have a way to learn this way either, but human learning has a big advantage: we need very little data and very little training to incorporate new knowledge and our context of a world is very broad to begin with. It's at once broad, specialised, robust and flexible. We're a bit scary if you think about it.
Which leads me to believe that the limitation of our machine learning algorithms is not in the labeling, or even in the data pre-processing but in some fundamental aspect of building a context from examples only. There's something missing and it's not something we know about (hah!). The missing part means that you can learn from examples until the heat death of the universe and there will still be an infinity of things you don't know anything about- and that are potentially part of your immediate environment.
Obviously, removing the need for pre-processing will make things much cheaper and there will be progress, ditto removing the need for supervision. But it won't get us anywhere nearer human learning, despite people's best wishes, because we're missing a part of the puzzle that's a whole other ball game.
(and which I obviously don't claim to have any idea about)