Perhaps what is missing is these rules:
AT = A (1)
AF = F (2)
A + T = T (3)
A + F = A (4)
Which can be derived from the given axioms, apparently. I’m not sure if some necessary axioms were omitted.
Using some of these, here’s one way to derive B!A=!A from !B=AD:
!B = AD
!B + A = AD + A
!B + A = AD + AT (1)
!B + A = A(D + T) (Distributivity)
!B + A = AT (3)
!B + A = A (1)
!!B!A = !A (Duality)
B!A = !A
I’m in.
Post also mentioned Tolerate Tolerance
Voted down for being off-topic.
Verifying a proof is quite a bit simpler that coming up with the proof in the first place.
Perhaps keep anonymous votes too, but make them worth less or only use them to break ties.
A sequence of wins and non-wins is enough to tell you whether a given approach can result in intelligent behaviour. That alone is enough to make it a useful experiment.
A man with one watch might have the wrong time; a man with two watches is more aware of his own ignorance.
The basement is the biggest
I like that turn of phrase.
I was about to point out that the fascinating and horrible dynamics of over-the-top threats are covered in length in Strategy of Conflict. But then I realised you’re the one who made that post in the first place. Thanks, I enjoyed that book.
It’s much easier to limit output than input, since the source code of the AI itself provide it with some patchy “input” about what the external world is like. So there is always some input, even if you do not allow human input at run-time.
ETA: I think I misinterpreted your comment. I agree that input should not be unrestricted.
You sir, have made a gender assumption.
Similar topics were discussed in an Open Thread.
Luckily digital constructs are easier to perfect that wooden ones. Although you wouldn’t think so with the current state of most software.
It is difficult to constrain the input we give to the AI, but the output can be constrained severely. A smart guy could wake up alone in a room and infer how he evolved, but so long as his only link to the outside world is a light switch that can only be switched once, there is no risk that he will escape.
If Dave holds a consequentialist ethical theory that only values his own life, then yes we are screwed.
If Dave’s consequentialism is about maximizing something external to himself (like the probable state of the universe in the future, regardless of whether he is in it), then his decision has little or no weight if he is a simulation, but massive weight if he is the real Dave. So the expected value of his decision is dominated by the possibility of him being real.
If we accept the simulation hypothesis, then there are already gzillions of copies of us, being simulated under a wide variety of torture conditions (and other conditions, but torture seems to be the theme here). An extortionist in our world can only create a relatively small number of simulations of us, relatively small enough that it is not worth taking them into account. The distribution of simulation types in this world bears no relation to the distribution of simulations we could possibly be in.
If we want to gain information about what sort of simulation we are in, evidence needs to come directly from properties of our universe (stars twinkling in a weird way, messages embedded in π), rather than from properties of simulations nested in our universe.
So I’m safe from the AI … for now.
Here’s an idea for how a LW-based commercial polling website could operate. Basically it is a variation on PredictionBook with a business model similar to TopCoder.
The website has business clients, and a large number of “forecasters” who have accounts on the website. Clients pay to have their questions added to the website, and forecasters give their probability estimates for whichever questions they like. Once the answer to a question has been verified, each forecaster is financially rewarded using some proper scoring rule. The more money assigned to a question, the higher the incentive for a forecaster to have good discrimination and calibration. Some clever software would also be needed to combine and summarize data in a way that is useful to clients.
The main advantage of this over other prediction markets is that the scoring rule encourages forecasters to give accurate probability estimates.
EDIT: The original post now has updated times and links, so refer to that instead.
Here are links to the times suggested, for convenience:
New York City: Fridays at 1pm
Paris: Tuesdays at 1pm
San Francisco: Wednesdays at 1pm
Melbourne: Tuesdays at 9pm (edited to actually coincide with Paris)
I’d suggest posting meeting times using timeanddate.com, to help avoid confusion about time zones and daylight savings.