Tofly

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From the old wiki discussion page:

I'm thinking we can leave most of the discussion of probability to Wikipedia. There might be more to say about Bayes as it applies to rationality but that might be best shoved in a separate article, like Bayesian or something. Also, I couldn't actually find any OB or LW articles directly about Bayes' theorem, as opposed to Bayesian rationality--if anyone can think of one, please add it. --A soulless automaton 19:31, 10 April 2009 (UTC)

  • I'd rather go for one article than break out a separate one for Bayesian - we can start splitting things out if the articles start to grow too long. --Paul Crowley (ciphergoth) 22:58, 10 April 2009 (UTC)
  • I added what I thought was the minimal technical information. I lean towards keeping separate concepts separate, even if the articles are sparse. If someone else feels it would be worthwhile to combine them though, go ahead. --BJR 23:07, 10 April 2009 (UTC)
  • I really would prefer to keep the maths and statistics separate from the more nebulous day-to-day rationality stuff, especially since Wikipedia already does an excellent job of covering the former, while the latter is much more OB/LW-specific. --A soulless automaton 21:59, 11 April 2009 (UTC)
The Wiki is Dead, Long Live the Wiki! [help wanted]

For wiki pages which are now tags, should we remove linked LessWrong posts, since they are likely listed below?

What should the convention be for linking to people's names? For example, I have seen the following:

  • LessWrong profile
  • Personal website/blog
  • Wiki/tag page on person
  • Wikipedia article on person
  • No link
  • No name

Finally, should the "see also" section be a comma-separated list after the first paragraph, or a bulleted list at the end of the page?

Tofly's Shortform

Thanks. I had skimmed that paper before, but my impression was that it only briefly acknowledged my main objection regarding computational complexity on page 4. Most of the paper involves analogies with evolution/civilization which I don't think are very useful-my argument is that the difficulty of designing intelligence should grow exponentially at high levels, so the difficulty of relatively low-difficulty tasks like designing human intelligence doesn't seem that important.

On page 35, Eliezer writes:

I am not aware of anyone who has defended an “intelligence fizzle” seriously and at great length.

I will read it again more thoroughly, and see if there's anything I missed.

Tofly's Shortform

I believe that fast takeoff is impossible, because of computational complexity.

This post presents a pretty clear summary of my thoughts. Essentially, if the problem of “designing an AI with intelligence level n” scales at any rate greater than linear, this will counteract any benefit an AI received from its increased intelligence, and so its intelligence will converge. I would like to see a more formal model of this.

I am aware that Gwern has responded to this argument, but I feel like he missed the main point. He gives many arguments showing ability to solve an NP-complete problem in polynomial time, or still do better than a human, or still gain a benefit from performing mildly better than a human.

But the concern here is really about A.I.s performing better than humans at certain tasks. It’s about them rapidly, and recursively, ascending to godlike levels of intelligence. That’s what is being argued is impossible. And there’s a difference between “superhuman at all tasks” and “godlike intelligence enabling what seems like magic to lesser minds”.

One of Eliezer’s favorite examples of how a powerful AI might take over the world is by solving the protein folding problem, designing some nano-machines, and using an online service to have them constructed. The problem with this scenario is the part where the AI “solves the protein folding problem”. That the problem is NP-hard means that it will be difficult, no matter how intelligent the AI is.

Something I’ve felt confused about, as I’ve been writing this up, is this problem of “what is the computational complexity of designing an AI with intelligence level N?” I have an intuition that there should be some “best architecture”, at least for any given environment, and that this architecture should be relatively “simple”. And once this architecture has been discovered, that’s pretty much it for self-improvement-you can still benefit from acquiring extra resources and improving your hardware, but these have diminishing returns. The alternative to this is that there’s an infinite hierarchy of increasingly good AI designs, which seems implausible to me. (Though there is an infinite hierarchy of increasingly large numbers, so maybe it isn’t.)

Now, it could be that even without fast takeoff, AGI is still a threat. But this is a different argument than "as soon as artificial intelligence surpasses human intelligence, recursive self-improvement will take place, creating an entity we can't hope to comprehend, let alone oppose."

Edit: Here is a discussion involving Yudkowsky regarding some of these issues.

Thoughts on the 5-10 Problem

Doesn't that mean the agent never makes a decision?

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Thoughts on the 5-10 Problem

Yes, you could make the code more robust by allowing the agent to act once its found a proof that any action is superior. Then, it might find a proof like

U(F) = 5

U(~F) = 10

10 > 5

U(~F) > U(F)

However, there's no guarantee that this will be the first proof it finds.

When I say "look for a proof", I mean something like "for each of the first 10^(10^100)) Godel numbers, see if it encodes a proof. If so, return that action.

In simple cases like the one above, it likely will find the correct proof first. However, as the universe gets more complicated (as our universe is), there is a greater chance that a spurious proof will be found first.