Done it all!

With all those personality tests and surveys it took me a bit more than an hour, but it was quite interesting (particularly CFAR questions) so I won't complain, much. :)

"Mass-energy is neither created nor destroyed..." It is then an effect of that rule, combined with our previous observation of the ship itself, which tells us that there's a ship that went over the cosmological horizon and now we can't see it any more.

It seems to me that this might be a point where logical reasoning takes it over from causal/graphical models, which in turn suggests why there are some problems with thinking about the laws of physics as nodes in a graph or even arrows as opposed to... well, I'm not really sure what specifically - or perhaps I'm just overapplying a lesson from the nature of logic where AI researchers tried to squeeze all the variety of cognitive processes into a logical reasoner and spectacularly failed at it.

Causal models, being as powerfull as they are, represent a similar temptation as logic did, and we should be wary not to make the same old (essentialy "hammer & nail") mistake, I think.

(Just thought I'd mention this so I don't forget this strange sense of something left not-quite-completely explained.)

Um... perhaps Wei Dai's analysis of the absent-minded driver problem (with it's subsequent resolution in the comments) and paulfchristiano's AIXI and existential despair would qualify?

How do a lot of you guys read so many things so quickly and retain all the knowledge? This seems like perhaps THE MOST VALUABLE skill I could learn, and I can't find ANY good resources on it!

Um, let's see if I get this (thinking to myself but posting here if anyone happens to find this useful - or even intelligible)...

claiming you know about X without X affecting you, you affecting X, or X and your belief having a common cause, violates the Markov condition on causal graphs

The causal Markov condition is that a phenomenon is independent of its noneffects, given its direct causes. It is equivalent to the ordinary Markov condition for Bayesian nets (any node in a network is conditionally independent of its nondescendents, given its parents) when the structure of a Bayesian network accurately depicts causality.

So, this condition induces certain (conditional) independencies between nodes in a causal graph (that can be found using the D-separation trick), and when we find two such nodes, they must also be uncorrelated (this follows from probabilistic independence being a stronger property than uncorrelatedness).

If one therefore claims there's a persistent correlation between X and belief about X, this means there's got to be some active path in Bayesian network for probabilistic influence to flow between them - otherwise, X and Belief(X) would be D-separated and thereby independent and uncorrelated. Insisting there's no such path (e.g. no chain of directed links) leads to violation of Markov condition, since it maintains there's probabilistic dependence between two nodes in a graph that cannot be accounted for by the causal links currently in the graph.

which is totally made up

you can't just do that without consequences

So... could this style of writing, with koans and pictures, be applied to transforming the majority of sequences into an even greater didactic tool?

Besides the obvious problems, I'm not sure how this would stand with Eliezer - they are, after all, his masterpiece.

Is there a difference between "truth" and "accuracy"?

This post starts out by saying that we know there is such a thing as truth, because there is something that determines our experimental outcomes, aside from our experimental predictions. But by the end of the post, you're talking about truth as correspondence to an arrangement of atoms in the universe. I'm not sure how you got from there to here.

Maybe. There is the time when your code is compiling, for one. But then, my job isn't the most cognitively demanding software job- or even, I would guess, the average- so it might not be the most useful sample.

I have heard, however, that programmers can be quite successful working as part-time consultants. That might be a good avenue for you to look into.