What bothers me in The Basic AI Drives is a complete lack of quantitativeness.
Temporal discount rate isn’t even mentioned. No analysis of self-improvement/getting-things-done tradeoff. Influence of explicit / implicit utility function dichotomy on self-improvement aren’t considered.
Diversity of a population plays a role too. If I’m well below Feynman level (and I am), then there’s a possibility that I can slightly improve my cognitive abilities without any negative consequences.
My experience with nootropics (racetams) seems to support this, as far as it is possible for anecdotal evidence.
It is valuable information, thanks. I underestimated relative weight of communication style in the feedback I got.
Thank you. It is something I can use for improvement.
Can you point at the flaws? I can see that the structure of sentences is overcomplicated, but I don’t know how it feels to native English speakers. Foreigner? Dork? Grammar Illiterate? I appreciate any feedback. Thanks.
One year and one level-up (thanks to ai-class.com) after this comment I’m still in the dark about the cause of downvoting the above comment.
I’m sorry for whining, but my curiosity took me over. Any comments?
Problem 2 by Bayes rule.
N is a random variable (RV) of number of filled envelopes.
C is a RV of selected envelope contains coin. P(C) means P(C=true) when appropriate.
Prior distribution
P(N=n) = 1/(m+1)
by the problem setup
P(C|N=n) = n/m
by the rule of total probability
P(C)=sum_n P(C|N=n)P(N=n) = sum_n (n/m/(m+1))=m(m+1)/2/m/(m+1)=1/2
by Bayes rule
P(N=n|C) = P(C|N=n)P(N=n)/P(C) = 2n/m/(m+1)
Let C’ is a RV of picking filled envelope second time.
by the problem statement
P(C'|N=n,C) = (n-1)/m
by the rule of total probability
P(C'|C)=sum_n P(C'|N=n,C)P(N=n|C) = ... substitutions and simplifications ... = 2(m-1)/(3m)
solving P(C’|C)=P(C) obtains
m=4
I suspect it’s highly relevant that if someone were to actually grow up in a grayscale environment, they wouldn’t be capable of experiencing blue.
Results of gene therapy for color blindness suggest otherwise. Maybe those monkeys and mice cannot experience colors, but they react as if they can.
I’m really want to try this myself. Infrared sensitive opsin in a retina, isn’t it wonderful?
I don’t understand what the question is getting at.
I am getting there. There’s a phenomenon called blindsight type 1. Try to imagine that you have “color blindsight”, i.e. you can’t differentiate between colors, but you can guess above chance what color it is. In this condition you lack qualia of colors.
I doubt that you think about rods and cones when you are deciding if it’s safe to cross the road. The question is: is there something in your perception of illuminated traffic light, that allows you to say that it is red or green or yellow? Or maybe you just know that it is green or yellow, but you can’t see any differences but position and luminosity?
Maybe it’s better to start from obvious things. Color experience, for example. Can you tell which light of traffic lights is illuminated while you are not using position of light and you aren’t asking himself which color it is? Is there something in your perception of different lights that allows you to tell that they are different?
I think communication cost isn’t main reason for P’s failure. O, for example, defects on 3 last turns even when playing against itself (rule 1 is of highest priority). Reasons are too strong punishment of other strategies (and consequently itself) and too strict check for self identity.
Strategy I described here should perform much better, when n is in range 80-95.
Huh? Do you think that selfishness unambiguously means: dominate Earth (or what left of it) as fast as possible?
Try another strategy. I[n] - TfT until turn n, defect on turn n, on later turns check if result on turn n was (defect,defect) and play TfT, otherwise defect. Idea is selfcooperation.
Moreover, simulations I ran using your rules for evolutionary tournament show that one strategy quickly dominates and others go extinct. Defectbot is among strategies which are fastest to go extinct (even in presence of cooperatebot) as it feeds off overaltruist strategies, which in turn fail to compete with tit-for-tat. So I doubt that at least evolutionary tournament will converge into Nash.
I predict that strategy that tit-for-tats 99 turns and defects on 100-th one will win in evolutionary tournament, given that tit-for-tat is also in the population.
ETA: I’ve sent another strategy.
In the meantime I’ve run my own simulation, studying a group of strategies, which perform as tit-for-tat except that at specific turn they defect and then they use result of this turn to switch to defect stone or continue tit-for-tatting. Thus they recognize copies of itself and cooperate with them. Such strategy can be exploited by switching to defect stone before it does, or by mimicking its behavior (second defect check after first. This case I didn’t analyze).
It leads to interesting results in evolutionary tournament. Second fairest (second longest period of tit-for-tatting) strategy wins. It outperforms less fair strategies by longest cooperation with fairest strategy. And it outperforms fairest strategy by exploiting it.
Then the goal of lesswrong (in this framework) seems to make brain act like it contains command and control center which corrects for errors caused by another parts of brain. And the list of errors includes the idea that brain contains command and control center. Sophisticated.
I wonder why rational consequentialist agent should do anything but channel all available resources into instrumental goal of finding a way to circumvent heat death. Mixed strategies are obviously suboptimal as expected utility of heat death circumvention is infinite.
Below is very unpolished chain of thoughts, which is based on vague analogy with symmetrical state of two indistinguishable quantum particles.
When participant is said ze is decider, ze can reason: let’s suppose that before coin was flipped I changed places with someone else, will it make difference? If coin came up heads, than I’m sole decider and there are 9 swaps which make difference in my observations. If coin came up tails then there’s one swap that makes difference. But if it doesn’t make difference it is effectively one world, so there’s 20 worlds I can distinguish, 10 correspond to my observations, 9 have probability (measure?) 0.5 0.1 (heads, I’m decider), 1 have probability 0.5 0.9 (tails, I’m decider). Consider following sentence as edited out. What I designated as P(heads) is actually total measure (?) of worlds participant is in. All this worlds are mutually exclusive, thus P(heads)=9 0.5 0.1+1 0.5 0.9=0.9.
What is average benefit of “yea”? (9 0.5 0.1 $100 + 1 0.5 0.9 $1000)=$495
Same for “nay”: (9 0.5 0.1 $700+1 0.5 0.9 $700)=$630
I’m still unsure if it is something more than intuition pump. Anyway, I’ll share any interesting thoughts.
Probabilistic inference for general belief networks is NP-hard (see The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks (PDF)). Thus straitforward approach is not an option. The problem is more like finding computationally tractable yet sufficiently powerful subtype of belief networks.