Computing Tricky Probabilities Using Model Counting | Hacker News Reader

Computing Tricky Probabilities Using Model Counting

msoos.org

19 pointszero_k12 comments

"-H1 or -X or -Y = True

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)"

Ooopps... it should read "when H1 is TRUE" not "when H1 is FALSE". Fixing now :) Thanks for spotting this!

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.

Bad, bad AI.

Pretty sure it's just the author giving a summary of their work.

> can be tricky

> extremely important

> this article showcases

and so on...

Claiming thst the phrase “extremely important” is indicative of AI authorship is preposterous.

Nobody ever made that claim

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 :)

You can tell it’s not AI because the ASCII diagram is actually coherent ;)

Hahahhaah yes, true.

Reply on news.ycombinator.com