All of Tyrin's Comments + Replies

the numbers you extract will be badly inaccurate most of the time

As its the case with an myopic view on any Bayesian inference process that involves a lot of noise. The question is just whether rationality is about removing the noise, or whether it is about something else; whether "rationality is more than ‘Bayesian updating’". I do not think we can answer this question very satisfyingly yet.

I tend to think what Cumming says is akin to saying something like: "Optimal evolution is not about adapting according to Bayes rule, because look at... (read more)

I didn't mean 'similar'. I meant that it is equivalent to Bayesian updating with a lot of noise. The great thing about recursive Bayesian state estimation is that it can recover from noise by processing more data. Because of this, noisy Bayes is a strict subset of noise-free Bayes, meaning pure rationality is basically noise-free Bayesian updating. That idea contradicts the linked article claiming that rationality is somehow more than that.

There is no plausible way in which the process by which this meme has propagated can be explained by Bayesian updati

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0moridinamael
Let's taboo "identical". In the limit of time and information, natural selection, memetic propagation, and Bayesian inference all converge on the same result. (Probably(?)) In reality, in observable timeframes, given realistic conditions, neither natural selection nor memetic propagation will converge on Bayesian inference; if you try to model evolution or memetic propagation with Bayesian inference, you will usually be badly wrong, and sometimes catastrophically so; if you expect to be able to extract something like a Bayes score by observing the movement of a meme or gene through a population, the numbers you extract will be badly inaccurate most of the time. Both of the above are true. I think you are saying the first one, while I am focusing on the second one. Do you agree? If so, our disagreement is a boring semantic one.

Even if stories are selected for plausibility, truth and whatever else leads most directly to maximal reward only once in a while, that would probably still be equivalent to Bayesian updating, just interfered by an enormous amount of noise.

Natural selection is Bayesian updating too: http://math.ucr.edu/home/baez/information/information_geometry_8.html

0moridinamael
I don't think you can justify using the word "equivalent" like that. I think maybe you mean "evolution and memetics are similar to Bayesian updating in some ways". That is not the same thing as "equivalence". It is not really helpful to take a very specific thing and say that it is "equivalent" to other very very different things, especially if such a comparison does not help you make any predictions. My culture has a story in it that the Creator of the Universe is going to come down in the form of a man and destroy the world if people do too many things that are said to be bad by a certain book. There is no plausible way in which the process by which this meme has propagated can be explained by Bayesian updating on truth value.

But it is not clear at all why stories do not approximate Bayesian updating. Stories do allow us to reach far into the void of space which cannot be mapped immediately from sensory data, but stories also mutate and get forgotten based how useful they are which at least resembles Bayesian updating. The question is whether this kind of filtering throws off the approximation so far that it is qualitatively a different computation.

0moridinamael
I don't think we can say that the mutation or loss of stories is very close to Bayesian updating. It may be a form of natural selection, and maybe sometimes the trait being selected for is "truth", but very often it's going to be something other than truth. Memes mutate in order to be more viral, and may lose truth on the way. Stories about big, shocking, horrible events are more memetically contagious and will thus look more probable, if you're assuming that their memetic availability reflects their likelihood.

But if you consider only US' well being, things might be a net positive.

If actions can be traced down to cause a whole lot of suffering, then it might be less certain to get a net positive outcome (for example due to empathic people revolting against these actions or feelings of guilt harming education and innovation; exodus of professionals to metropolitan regions in Europe, Asia etc.).

Isn't the idea more that the neural network just learns rough subgraphs of the underlying DAG that captures the causal structure up to quantum detail? Whole-part relationships are such subgraphs: a person being present causes a face to be present, which causes eyes to be present etc.

I had exactly the same insight as James_Miller a couple of days ago. Are you sure this is Grace's Doomsday argument? Her reasoning seems to be rather along the line that it is more likely that we'll be experiencing a late Great Filter (argued by SIA which I'm not familiar with). The idea here is rather that for life to likely exist for a prolonged time there has to be a late Great Filter (like space travel being extremely difficult or UFAI), because otherwise Paperclippers would quickly conquer the entire space (at least in universes like ours where all points in space can be travelled to in principle).

0turchin
Yes, I now see the the difference: "where life develops and there is a great filter that destroy civilizations before paperclip maximizers get going." But I understand it in the way that great filter is something that usually happens during tech development of a civilization before it creates AI. Like nuclear wars and bio catastrophes are so likely that no civilization survive until creation of strong AI. It doesn't contradict Katja's version, which only claims that GF is in the future. It still in the future. https://meteuphoric.wordpress.com/2010/03/23/sia-doomsday-the-filter-is-ahead/