NancyLebovitz comments on Open Thread June 2010, Part 3 - Less Wrong

6 Post author: Kevin 14 June 2010 06:14AM

You are viewing a comment permalink. View the original post to see all comments and the full post content.

Comments (606)

You are viewing a single comment's thread. Show more comments above.

Comment author: Yoreth 14 June 2010 08:10:24AM 5 points [-]

A prima facie case against the likelihood of a major-impact intelligence-explosion singularity:

Firstly, the majoritarian argument. If the coming singularity is such a monumental, civilization-filtering event, why is there virtually no mention of it in the mainstream? If it is so imminent, so important, and furthermore so sensitive to initial conditions that a small group of computer programmers can bring it about, why are there not massive governmental efforts to create seed AI? If nothing else, you might think that someone could exaggerate the threat of the singularity and use it to scare people into giving them government funds. But we don’t even see that happening.

Second, a theoretical issue with self-improving AI: can a mind understand itself? If you watch a simple linear Rube Goldberg machine in action, then you can more or less understand the connection between the low- and the high-level behavior. You see all the components, and your mind contains a representation of those components and of how they interact. You see your hand, and understand how it is made of fingers. But anything more complex than an adder circuit quickly becomes impossible to understand in the same way. Sure, you might in principle be able to isolate a small component and figure out how it works, but your mind simply doesn’t have the capacity to understand the whole thing. Moreover, in order to improve the machine, you need to store a lot of information outside your own mind (in blueprints, simulations, etc.) and rely on others who understand how the other parts work.

You can probably see where this is going. The information content of a mind cannot exceed the amount of information necessary to specify a representation of that same mind. Therefore, while the AI can understand in principle that it is made up of transistors etc., its self-representation necessary has some blank areas. I posit that the AI cannot purposefully improve itself because this would require it to understand in a deep, level-spanning way how it itself works. Of course, it could just add complexity and hope that it works, but that’s just evolution, not intelligence explosion.

So: do you know any counterarguments or articles that address either of these points?

Comment author: NancyLebovitz 15 June 2010 01:34:28PM *  2 points [-]

Another argument against the difficulties of self-modeling point: It's possible to become more capable by having better theories rather than by having a complete model, and the former is probably more common.

It could notice inefficiencies in its own functioning, check to see if the inefficiencies are serving any purpose, and clean them up without having a complete model of itself.

Suppose a self-improving AI is too cautious to go mucking about in its own programming, and too ethical to muck about in the programming of duplicates of itself. It still isn't trapped at its current level, even aside from the reasonable approach of improving its hardware, though that may be a more subtle problem than generally assumed.

What if it just works on having a better understanding of math, logic, and probability?