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Daniel_Burfoot comments on Open Thread: October 2009 - Less Wrong
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I plan to develop this into a top level post, and it expands on my ideas in this comment, this comment, and the end of this comment. I'm interested in what LWers have to say about it.
Basically, I think the concept of intelligence is somewhere between a category error and a fallacy of compression. For example Marcus Hutter's AIXI purports to identify the inferences a maximally-intelligent being would make, yet it (and efficient approximations) does not have practical application. The reason (I think) is that it works by finding the shortest hypothesis that fits any data given to it. This means it makes the best inference, on average, over all conceivable worlds it could be placed in. But the No Free Lunch theorems suggest that this means it will be suboptimal compared to any algorithm tailored to any specific world. At the very least, having to be optimal for the all of the random worlds and anti-inductive worlds, should imply poor performance in this world.
The point is that I think "intelligence" can refer to two useful but very distinct attributes: 1) the ability to find the shortest hypothesis fitting the available data, and 2) having beliefs (a prior probability distribution) about one's world that are closest to (have the smallest KL divergence from) that world. (These attributes roughly correspond to what we intuit as "book smarts" and "street smarts" respectively.) A being can "win" if it does well on 2) even if it's not good at 1), since using a prior can be more advantageous than finding short hypothesis since the prior already points you to the right hypothesis.
Making something intelligent means optimizing the combination of each that it has, given your resources. What's more, no one algorithm can be generally optimal for finding the current world's probability distribution, because that would also violate the NFL theorems.
Organisms on earth have high intelligence in the second sense. Over their evolution history they had to make use of whatever regularity they could find about their environment, and the ability to use this regularity became "built in". So the history of evolution is showing the result of one approach to finding the environment's distribution (ETC), and making an intelligent being means improving upon this method, and programming it to "springboard" from that prior with intelligence in the first sense.
Thoughts?
This may be tangential to your point, but it's worth remembering that human intelligence has a very special property, which is that it is strongly domain-independent. A person's ability to solve word puzzles correlates with her ability to solve math puzzles. So you can measure someone's IQ by giving her a logic puzzle test, and the score will tell you a lot about the person's general mental capabilities.
Because of that very special property, people feel more or less comfortable referring to "intelligence" as a tangible thing that impacts the real world. If you had to pick between two doctors to perform a life-or-death operation, and you knew that one had an IQ of 100 and the other an IQ of 160, you would probably go with the latter. Most people would feel comfortable with the statement "Harvard students are smarter than high school dropouts", and make real-world predictions based on it (e.g a Harvard student is more likely to be able to write a good computer program than a high school dropout, even if the former didn't study computer science).
The point is that there's no reason this special domain-independence property of human intelligence should hold for non-human reasoning machines. So while it makes sense to score humans based on this "intelligence" quantity, it might be totally meaningless to attempt to do so for machines.
Not so fast. Human intelligence is relatively domain independent. But human minds are constantly exploiting known regularities of the environment (by making assumptions) to make better inferences. These reguarities make up a tiny sliver of the Platonic space of generating functions. By (correctly) assuming we're in that sliver, we vastly improve our capabilities compared to if we were AIXIs lacking that knowledge.
Human intelligence appears strongly domain-indepdent because it generalizes to all the domains that we see. It does not generalize to the full set of computable environments -- no intelligence can do that while still performing as well in each as we do in this environment.
Non-human animals are likewise "domain-independently intelligent" for the domains that they exist in. Most humans would die, for example, if dropped in the middle of the desert, ocean, or arctic.
Not just by making assumptions: you can learn (domain-specific) optimizations that don't introduce new info, but improve ability, allowing to understand more from the info you have (better conceptual pictures for natural science; math).
Another example of how domain-dependent human intelligence actually is, is optical illusions.
Optical illusions are when an image violates an assumption your brain is making to interpret visual data, causing it to misinterpret the image. And remember, this is only going slightly outside of the boundary of the assumptions your brain makes.