Make sure you know which "SOPA" you're referring to. This piece of legislation has undergone significant change from the version that sparked popular outrage.
Added after reading some other comments: if you've made cynical predictions about SOPA's progress through Congress or its effects in the real world, don't forget to update your beliefs on the eventual outcome. Write this prediction down somewhere.
I strongly suspect that what's going on with "people who talk to children like they're adults" is that they talk to children like they're people.
The morality of convincing children of arbitrary stuff is questionable. Though less than usual, because children are designed to work that way (also changing their preferences in cartoons isn't the end of the world). Do you know if the liking is sincere - i.e., if they enjoy cooking, or only believe they do and are surprised to find they didn't after each time?
Regarding "convincing" children of things: this AI koan is relevant.
In the days when Sussman was a novice, Minsky once came to him as he sat hacking at the PDP-6.
“What are you doing?”, asked Minsky.
“I am training a randomly wired neural net to play Tic-Tac-Toe” Sussman replied.
“Why is the net wired randomly?”, asked Minsky.
“I do not want it to have any preconceptions of how to play”, Sussman said.
Minsky then shut his eyes.
“Why do you close your eyes?”, Sussman asked his teacher.
“So that the room will be empty.”
At that moment, Sussman was enlightened.
So, I missed my goal of scoring 100% in the Stanford AI class. Time to do better - to do what others can't, or just haven't thought of yet.
Maybe I'm missing something obvious here, but I'm unsure how to calculate P(S). I'd appreciate it if someone could post an explanation.
Sure. S results from HH or from TT, so we'll calculate those independently and add them together at the end. We'll do that by this equation: P(p=x|S) = P(p=x|HH) * P(H) + P(p=x|TT) * P(T).
We start out with a uniform prior: P(p=x) = 1. After observing one H, by Bayes' rule, P(p=x|H) = P(H|p=x) * P(p=x) / P(H). P(H|p=x) is just x. Our prior is 1. P(H) is our prior, multiplied by x, integrated from 0 to 1. That's 1/2. So P(p=x|H) = x*1/(1/2) = 2x.
Apply the same process again for the second H. Bayes' rule: P(p=x|HH) = P(H|p=x,H) * P(p=x|H) / P(H|H). The first term is still just x. The second term is our updated belief, 2x. The denominator is our updated belief, multiplied by x, integrated from 0 to 1. That's 2/3 this time. So P(p=x|HH) = x*2x/(2/3) = 3x^2.
Calculating tails is similar, except we update with 1-x instead of x. So our belief goes from 1, to 2-2x, to 3x^2-6x+3. Then substitute both of these into the original equation: (3/2)(x^2) + (3/2)(x^2 - 2x + 1). From there it's just a bit of algebra to get it into the form I linked to.
Why is your name Miley Cyrus?
This paper discusses the Fermi Paradox in the context of civilizations that can build self-replicating probes (SRPs) to explore/exploit the galaxy. In passing, it discusses some FAI-related objections to self-replicating machine intelligence.
One popular argument against SRPs is presented by Sagan and Newman (Sagan and Newman, 1983). They argue that any presumably wise and cautious civilization would never develop SRPs because such machines would pose an existential risk to the original civilization. The concern is that the probes may undergo a mutation which permits and motivates them to either wipe out the homeworld or overcome any reasonable limit on their reproduction rate, in effect becoming a technological cancer that converts every last ounce of matter in the galaxy into SRPs.
Bad Clippy.
Upvote :-)
Followup questions spring to mind... Is there standard software for managing large trees of this sort? Is any of it open source? Are there file formats that are standard in this area? Do any posters (or lurkers who could be induced to start an account to respond) personally prefer particular tools in real life?
Actionable advice would be appreciated!
There's a Stanford online course next semester called Probabilistic Graphical Models that will cover different ways of representing this sort of problem. I'm enrolled.
= f037147d6e6c911a85753b9abdedda8d){kind=logo}
= 27d567bc2d48d9f40e9ccc20ea370b15)
= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Voted you down. This is deontologist thought in transhumanist wrapping paper.