Curious if you ever found what you were looking for.
Peter Chatain
Karma: 33
As stated by others, there are counter examples. An important class of counter examples I can think of is when you want to pick up on mental attitudes or traits that likely only the best have–think “You are the average of your 5 closest friends.”
The link for the AI crafting a super weapon seems to be broken. Here is a later article that is the best I could find: https://www.digitalspy.com/videogames/a796635/elite-dangerous-ai-super-weapons-bug/
Although this isn’t a direct answer, I think there’s something that changed recently with chat gpt such that it is now much better at filtering out illegal advice. It appears to be more complex than simply running a filter over what words were in the prompt or what words are in chat gpt’s output. By recent, I mean in the last 24 hours, and many tricks to “jailbreak” chat gpt no longer work.
It gives the impression that they modified the design of it to train on not providing illegal information.
I was thinking something similar, but I missed the point about the prior. To get intuition, I considered placing like 99% probability on one day in 2030. Then generic uncertainty spreads out this distribution both ways, leaving the median exactly what it was before. Each bit of probability mass is equally likely to move left or right when you apply generic uncertainty. Although this seems like it should be slightly wrong since the tiny bit of probability that it is achieved right now can’t go back in time, so will always shift right.
In other words, I think this will be right for this particular case, but an incorrect argument for when significant probability mass is on it happening very soon, or for when there is a very large amount of correcting done.
Does this hide the text? (Sorry just testing things out rn)
Wow
Ok so you can hide stuff by typing >! on a new line
Yep that’s right! And it’s a good thing to point out, since there’s a very strong bias towards whatever can be expressed in a simple manner. So, the particular universal Turing machine you choose can matter a lot.
However, in another sense, the choice is irrelevant. No matter what universal Turing machine is used for the Universal prior, AIXI will still converge to the true probability distribution in the limit. Furthermore, for a certain very general definition of prior, the Universal prior assigns more* probability to all possible hypotheses than any other type of prior.
*More means up to a constant factor. So f(x)=x is more than g(x)=2x because we are allowed to say f(x)>1/3g(x) for all x.
Here’s some mantras I have:
That which you are aware of, you are free from.
And some variation of:
Truth comes knocking. You say “go away, I’m looking for the truth.” It goes away, puzzling.
The above I rediscovered recently through reading Zen and the Art of Motorcycle Maintenance.
This should say :
JumpReLU (top)