Complicated causal chains can be tricky to work with, but estimating the probability of a bad event (e.g. nuclear facility meltdown) is extremely important. This article showcases how complicated, inter-dependent causal chains can be analyzed through state-of-the-art propositional model counters.
Hahah yes. Thanks for defending me ;) Apparently making something accessible to the public is AI. I thought it was science communication :D It's what we are all supposed to do as scientists, but somehow nobody pays us to do. So I do it in my free time. And some people thinks it's AI. Weird times.
Hahha you must be kidding. I am the author. And it was not written with AI. I write that blog, and never used AI to make the blog, or to post here. Weird world we are living in... Ghosts, people are seeing ghosts.
We should have a beer and then you'll see I'm real. Also, look at my history :)
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[ 2.9 ms ] story [ 35.6 ms ] thread> extremely important
> this article showcases
and so on...
We should have a beer and then you'll see I'm real. Also, look at my history :)
The highlighted lines are what’s call the Tseitin transformation, which basically means that when H1 is FALSE, (NOT X or NOT y) must be TRUE"
Forgive me if I'm wrong, but I think it should be "when H1 is TRUE, (NOT X or NOT y) must be TRUE" since this is equivalent to "H1 -> NOT (X & Y)"