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Comment author: Toby_Ord 27 October 2014 12:48:22PM 2 points [-]

Regarding your question, I don't see theoretical reasons why one shouldn't be making deals like that (assuming one can and would stick to them etc). I'm not sure which decision theory to apply to them though.

Comment author: Toby_Ord 27 October 2014 12:46:13PM 2 points [-]

The Moral Parliament idea generally has a problem regarding time. If it is thought of as making decisions for the next action (or other bounded time period), with new distribution of votes etc when the next choice comes up, then there are intertemporal swaps (and thus pareto improvements according to each theory) that it won't be able to achieve. This is pretty bad, as it at least appears to be getting pareto dominated by another method. However, if it is making one decision for all time over all policies for resolving future decisions, then (1) it is even harder to apply in real life than it looked, and (2) it doesn't seem to be able to deal with cases where you learn more about ethics (i.e. update your credence function over moral theories) -- at least not without quite a bit of extra explanation about how that works. I suppose the best answer may well be that the policies over which the representatives are arguing include branches dealing with all ways the credences could change, weighted by their probabilities. This is even more messy.

My guess is that of these two broad options (decide one bounded decision vs decide everything all at once) the latter is better. But either way it is a bit less intuitive than it first appears.

Comment author: Toby_Ord 07 October 2014 10:47:20AM 1 point [-]

This is a good idea, though not a new one. Others have abandoned the idea of a formal system for this on the grounds that:

1) It may be illegal 2) Quite a few people think it is illegal or morally dubious (whether or not it is actually illegal or immoral)

It would be insane to proceed with this without confirming (1). If illegal, it would open you up to criminal prosecution, and more importantly, seriously hurt the movements you are trying to help. I think that whether or not it turns out to be illegal, (2) is sufficient reason to not pursue it. It may cause serious reputational damage to the movement which I'd expect to easily outweigh the financial benefits.

I also think that the 10% to 20% boost is extremely optimistic. That would only be achieved if almost everyone was using it and they all wanted to spend most of their money funding charities that don't operate in their countries. I'd expect something more like a boost of a few percent.

Note that there are also very good alternatives. One example is a large effort to encourage people to informally do this in a non-matched way by donating to the subset of effective charities that are tax deductable in their country. This could get most of the benefits for none of the costs.

Comment author: Toby_Ord 19 August 2014 04:11:55PM 7 points [-]

This is a really nice and useful article. I particularly like the list of problems AI experts assumed would be AI-complete, but turned out not to be.

I'd add that if we are trying to reach the conclusion that "we should be more worried about non-general intelligences than we currently are", then you don't need it to be true that general intelligences are really difficult. It would be enough that "there is a reasonable chance we will encounter a dangerous non-general one before a dangerous general one". I'd be inclined to believe that even without any of the theorising about possibility.

I think one reason for the focus on 'general' in the AI Safety community is that it is a stand in for the observation that we are not worried about path planners or chess programs or self-driving cars etc. One way to say this is that these are specialised systems, not general ones. But you rightly point out that it doesn't follow that we should only be worried about completely general systems.

Comment author: Toby_Ord 17 June 2014 07:40:52AM 11 points [-]

Thanks for bringing this up Luke. I think the term 'friendly AI' has become something of an albatross around our necks as it can't be taken seriously by people who take themselves seriously. This leaves people studying this area without a usable name for what they are doing. For example, I talk with parts of the UK government about the risks of AGI. I could never use the term 'friendly AI' in such contexts -- at least without seriously undermining my own points. As far as I recall, the term was not originally selected with the purpose of getting traction with policy makers or academics, so we shouldn't be too surprised if we can see something that looks superior for such purposes. I'm glad to hear from your post that 'AGI safety' hasn't rubbed people up the wrong way, as feared.

It seems from the poll that there is a front runner, which is what I tend to use already. It is not too late to change which term is promoted by MIRI / FHI etc. I think we should.

Comment author: Oscar_Cunningham 23 May 2014 11:37:41AM *  12 points [-]

When algorithms have bad worst cases, these cases are often the inputs with a particular structure. For example if you use quicksort without randomising your pivots then you have an average case complexity of n.log(n) but a worst case complexity of n^2. The worst inputs are the ones where the list is already partially sorted. The "partially sorted" is the kind of structure I'm talking about.

If we expected to see inputs with some kind of structure (but we didn't know which kind of structure) then we might well be worried that we would get inputs with precisely the structure that would hit our worst case.

So suppose we do indeed have a prior over our inputs, but that this is a Solomonoff prior. Among all the inputs of length n we expect to see each one with probability proportional to exp(-k) where k is its Kolmogorov complexity. Then most of the strings will have complexity n and so probability ~ exp(-n). However our worst case string will have complexity at most m+log(n) where m is the length of our algorithm's code. This is by virtue of its description as the worst case for that algorithm among the strings of length n. So we will anticipate getting the worst case input with probability ~ exp(-m)/n. So if our worst case complexity is g(n) then our average case complexity is O(g(n)/n).

Under a Solomonoff prior the worst cases and average cases are the same! (up to a polynomial)

EDIT: So if there are cases where randomisation gives an exponentially better worst case complexity, then we won't be able to derandomise them under the Solomonoff prior.

Comment author: Toby_Ord 28 May 2014 11:47:52AM 3 points [-]

This is quite possibly the best LW comment I've ever read. An excellent point with a really concise explanation. In fact it is one of the most interesting points I've seen within Kolmogorov complexity too. Well done on independently deriving the result!

Comment author: Toby_Ord 31 March 2014 08:28:20AM 0 points [-]

Without good ways to overcome selection bias, it is unclear that data like this can provide any evidence of outsized impact of unconventional approaches. I would expect a list of achievements as impressive as the above whether or not there was any correlation between the two.

Comment author: Toby_Ord 17 June 2013 03:43:01PM 4 points [-]

Carl,

You are completely right that there is a somewhat illicit factor-of-1000 intuition pump in a certain direction in the normal problem specification, which makes it a bit one-sided. Will McAskill and I had half-written a paper on this and related points regarding decision-theoretic uncertainty and Newcomb's problem before discovering that Nozick had already considered it (even if very few people have read or remembered his commentary on this).

We did still work out though that you can use this idea to create compound problems where for any reasonable distribution of credences in the types of decision theory, you should one-box on one of them and two-box on the other: something that all the (first order) decision theories agree is wrong. So much the worse for them, we think. I've stopped looking into this, but I think Will has a draft paper where he talks about this alongside some other issues.

Comment author: Eliezer_Yudkowsky 06 September 2011 12:49:08AM 30 points [-]

Variants I'd like to see:

1) You can observe rounds played by other bots.

2) You can partially observe rounds played by other bots.

3) (The really interesting one.) You get a copy of the other bot's source code and are allowed to analyze it. All bots have 10,000 instructions per turn, and if you run out of time the round is negated (both players score 0 points). There is a standard function for spending X instructions evaluating a piece of quoted code, and if the evaled code tries to eval code, it asks the outer eval-ing function whether it should simulate faithfully or return a particular value. (This enables you to say, "Simulate my opponent, and if it tries to simulate me, see what it will do if it simulates me outputting Cooperate.")

Comment author: Toby_Ord 07 September 2011 08:27:03PM 3 points [-]

Regarding (2), this is a particularly clean way to do it (with some results of my old simulations too).

http://www.amirrorclear.net/academic/papers/sipd.pdf http://www.amirrorclear.net/academic/ideas/dilemma/index.html

Comment author: Toby_Ord 09 April 2010 01:29:34PM 8 points [-]

We can't use the universal prior in practice unless physics contains harnessable non-recursive processes. However, this is exactly the situation in which the universal prior doesn't always work. Thus, one source of the 'magic' is through allowing us to have access to higher levels of computation than the phenomena we are predicting (and to be certain of this).

Also, the constants involved could be terrible and there are no guarantees about this (not even probabilistic ones). It is nice to reach some ratio in the limit, but if your first Graham's number of guesses are bad, then that is very bad for (almost) all purposes.

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