I’d like to write up a more in depth reply to this post eventually, but I have a few thoughts right off the bat.
When I first read your opening claim that CAH is weaker and more likely to be true, I found myself wondering what you thought the difference was. Then, when you explained how you see the difference, I wasn’t satisfied—you didn’t explain as clearly as I would like how abstractions which are features of the world at distance come apart from abstractions which are convergently used by a variety of similarly pressured learners.
One load bearing assumption imo is that the relevant pressures which different learners share are ~ universal—namely, pressures to make good predictions, and pressures to use resources (time and space) efficiently. If we start requiring that agents have to share more pressures than these in order to converge on equivalent abstractions we have lost the kind of strength that I care about.
I currently guess that “good” as humans know it is not a natural abstraction, or an abstraction which different kinds of agents will robustly converge on. Of course this is a very important question indeed and it would be wonderful if it is a natural abstraction when you are trying to predict your observations of humans! And it’s totally possible that is the case.
Oh and last thing—agents with different sensory equipment should still converge on a lot of the same abstractions! I think you were just meaning that they might have some idiosyncratic abstractions in the mix as well, which I grant.
I’d like to write up a more in depth reply to this post eventually, but I have a few thoughts right off the bat.
When I first read your opening claim that CAH is weaker and more likely to be true, I found myself wondering what you thought the difference was. Then, when you explained how you see the difference, I wasn’t satisfied—you didn’t explain as clearly as I would like how abstractions which are features of the world at distance come apart from abstractions which are convergently used by a variety of similarly pressured learners.
One load bearing assumption imo is that the relevant pressures which different learners share are ~ universal—namely, pressures to make good predictions, and pressures to use resources (time and space) efficiently. If we start requiring that agents have to share more pressures than these in order to converge on equivalent abstractions we have lost the kind of strength that I care about.
I currently guess that “good” as humans know it is not a natural abstraction, or an abstraction which different kinds of agents will robustly converge on. Of course this is a very important question indeed and it would be wonderful if it is a natural abstraction when you are trying to predict your observations of humans! And it’s totally possible that is the case.
Oh and last thing—agents with different sensory equipment should still converge on a lot of the same abstractions! I think you were just meaning that they might have some idiosyncratic abstractions in the mix as well, which I grant.
btw here is a cool example of some empirical evidence for convergent abstractions in the some of the contexts we care about: https://www.nature.com/articles/nn.4244