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Comment by mrfusion | Hacker News Reader

I've always wondered if the problems an intelligence solves are exponentially hard so even if we build a super intelligence it wouldn't be all that much smarter than we are.

For example compare how many more cities in the traveling salesman problem a super computer can solve vs your grandmas pc. It's more but surprisingly not all that many more.

What do you think of that idea?

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I think the 98% of DNA we share with chimpanzees, with brains almost three times as large, suggests that isn't the case. Language appears to be a force-amplifier in intelligence, and we have no reason to believe that other such do not exist.

I think this basic concept of intractability, which programmers are very familiar with, hasn't penetrated far enough into AI world.

Bostrom and Yudkowsky in particular seem happy to hand-wave past computational complexity.

Have you seen gwern's essay on computational complexity arguments about AI? https://www.gwern.net/Complexity%20vs%20AI

I'm getting kind of sick of people who haven't read 2% of the stuff imagining what they think we've never talked about over the last 16 years.

https://intelligence.org/files/IEM.pdf

Reading 2% of your stuff is like reading 400% of my stuff, and I'm verbose as hell.

Edit.

First off, that's a blatant ad hominem.

Moreover, if your best argument against the guy you're claiming is defrauding everyone is "I can't be bothered to read his work"...

I've read Eliezer's stuff, which is what gives my request its special poignancy.

I haven't had time to read the entire thing, so I've skimmed over lots, but there doesn't seem to be much mention of complexity theory in the way I believe idlewords is talking about.

I'm very curious: What happens if the algorithms for general artificial intelligence, and the ability for an AI to improve itself are all NP-Hard problems? Is that covered?

It might be similar to the "intelligence combustion" scenario outlined. But that appears to be a scenario where do not need to be fearing superintelligence.

As it says in the paper, the evolution of hominids doesn't look like this; population genetics says that if hominid brains got larger, the marginal fitness returns on neurons went up.

Or in a more intuitive sense, humans didn't seem to need brains 1000x as large to get one unit of improvement in practical effectiveness over chimpanzee cognition. But yes, this stuff is complicated, hence the paper not being two paragraphs long.

Thank you, that is a very intuitive explanation.

You've probably read Scott Aaronson's Why Philosophers Should Care About Computational Complexity? [0]. This seems like the perfect area to apply a lot of the questions he brings up. That's what I was looking for as I skimmed through the paper. Maybe that's what idlewords was talking about as well.

[0] http://www.scottaaronson.com/papers/philos.pdf

It doesn't even hold with humans. Take a look at John von Neumann and his brain was pushed down the human birth canal.

We really have a poor idea of what super intelligence is - none of us can even understand people much more intelligent than ourselves.

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