That might have been discussed in the comments, but my gut reaction to the tree example was not “It’s not really understanding tree” but “It’s understanding trees visually”. That is, I think the examples point to trees being a natural abstraction with regard to images made of pixels. In that sense, dogs and cats and other distinct visual objects might fit your proposal of natural abstraction. Yet this doesn’t entail that trees are a natural abstraction when given the position of atoms, or sounds (to be more abstract). I thus think that natural abstractions should be defined with regard for the sort of data that is used.
For human values, I might accept that they are natural abstraction, but I don’t know for which kind of data. Is audiovisual data (as in youtube videos) enough? Do we also need textual data? Neuroimagery? I don’t know, and that makes me slightly more pessimistic about a unsupervised model learning human values by default.
My model of abstraction is that high-level abstractions summarize all the information from some chunk of the world which is relevant “far away”. Part of that idea is that, as we “move away” from the information-source, most information is either quickly wiped out by noise, or faithfully transmitted far away. The information which is faithfully transmitted will usually be present across many different channels; that’s the main reason it’s not wiped out by noise in the first place. Obviously this is not something which necessarily applies to all possible systems, but intuitively it seems like it should apply to most systems most of the time: information which is not duplicated across multiple channels is easily wiped out by noise.
Great post!
That might have been discussed in the comments, but my gut reaction to the tree example was not “It’s not really understanding tree” but “It’s understanding trees visually”. That is, I think the examples point to trees being a natural abstraction with regard to images made of pixels. In that sense, dogs and cats and other distinct visual objects might fit your proposal of natural abstraction. Yet this doesn’t entail that trees are a natural abstraction when given the position of atoms, or sounds (to be more abstract). I thus think that natural abstractions should be defined with regard for the sort of data that is used.
For human values, I might accept that they are natural abstraction, but I don’t know for which kind of data. Is audiovisual data (as in youtube videos) enough? Do we also need textual data? Neuroimagery? I don’t know, and that makes me slightly more pessimistic about a unsupervised model learning human values by default.
My model of abstraction is that high-level abstractions summarize all the information from some chunk of the world which is relevant “far away”. Part of that idea is that, as we “move away” from the information-source, most information is either quickly wiped out by noise, or faithfully transmitted far away. The information which is faithfully transmitted will usually be present across many different channels; that’s the main reason it’s not wiped out by noise in the first place. Obviously this is not something which necessarily applies to all possible systems, but intuitively it seems like it should apply to most systems most of the time: information which is not duplicated across multiple channels is easily wiped out by noise.