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Ben Pace | v1.0.0Jul 17th 2020 | (+79/-2925) | ||

pedrochaves | v0.0.22Oct 2nd 2012 | (+90/-15) | ||

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pedrochaves | v0.0.19Sep 29th 2012 | (+42/-12) /* Further Reading & References */ | ||

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Alex_Altair | v0.0.15Jun 17th 2012 | (+136/-2) /* Measuring optimization power */ |

~~A system~~An **optimization process** is ~~performing ~~~~optimization~~~~ if it is moving~~any kind of process that systematically comes up with solutions that are better than the solution used before. More technically, this kind of process moves the world into a specific and unexpected set of ~~states.~~states by searching through a large search space, hitting small and low probability targets. When this process is gradually guided by some agent into some specific state, through searching specific targets, we can say it prefers that state.

The best way to exemplify an optimization process is through a simple example: Eliezer Yudkowsky suggests natural selection is such a process. Through an implicit preference – better replicators – natural selection searches all the genetic landscape space and hit small targets: efficient mutations.

Consider the human being. We are a highly complex object with a low probability to have been created by chance - natural selection, however, over millions of years, built up the infrastructure needed to build such a functioning body. This body, as well as other organisms, had the chance (was *selected*) to develop because it is in itself a rather efficient replicator suitable for the environment where it came up.

Or consider the famous chessplaying computer, Deep Blue. Outside of the narrow domain of selecting moves for chess games, it can't do anything impressive: but *as* a chessplayer, it was massively more effective than virtually all humans. It has a high optimization power in the chess domain but almost none in any other field. Humans or evolution, on the other hand, are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this *optimization process* abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)

One way to think mathematically about optimization, like evidence, is in information-theoretic bits. The optimization power is the amount of surprise we would have in the result if there were no optimization process present. Therefore we take the base-two logarithm of the reciprocal of the probability of the result. A one-in-a-million solution (a solution so good relative to your preference ordering that it would take a million random tries to find something that good or better) can be said to have log_2(1,000,000) = 19.9 bits of optimization. Compared to a random configuration of matter, any artifact you see is going to be much more optimized than this. The math describes only laws and general principles for reasoning about optimization; as with probability theory, you oftentimes can't apply the math directly.

- Optimization and the Singularity by Eliezer Yudkowsky
- Measuring Optimization Power by Eliezer Yudkowsky

~~An~~A system is performing **optimization process**

~~The best way to exemplify an optimization process is through a simple example: ~~~~Eliezer Yudkowsky~~~~ suggests natural selection is such a process. Through an implicit preference – better replicators – natural selection searches all the genetic landscape space and hit small targets: efficient mutations.~~

~~Consider the human being. We are a highly complex object with a low probability to have been created by chance - natural selection, however, over millions of years, built up the infrastructure needed to build such a functioning body. This body, as well as other organisms, had the chance (was ~~~~selected~~~~) to develop because~~if it is ~~in itself~~moving the world into a ~~rather efficient replicator suitable for the environment where it came up.~~

~~Or consider the famous chessplaying computer, ~~~~Deep Blue~~~~. Outside~~specific and unexpected set of ~~the narrow domain of selecting moves for chess games, it can't do anything impressive: but ~~~~as~~~~ a chessplayer, it was massively more effective than virtually all humans. It has a high optimization power in the chess domain but almost none in any other field. Humans or evolution, on the other hand, are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this ~~~~optimization process~~~~ abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)~~states.

~~One way to think mathematically about optimization, like ~~~~evidence~~~~, is in information-theoretic bits. The optimization power is the amount of ~~~~surprise~~~~ we would have in the result if there were no optimization process present. Therefore we take the base-two logarithm of the reciprocal of the probability of the result. A one-in-a-million solution (a solution so good relative to your preference ordering that it would take a million random tries to find something that good or better) can be said to have log_2(1,000,000) = 19.9 bits of optimization. Compared to a random configuration of matter, any artifact you see is going to be much more optimized than this. The math describes only laws and general principles for reasoning about optimization; as with ~~~~probability theory~~~~, you oftentimes can't apply the math directly.~~

~~Optimization and the Singularity~~~~by Eliezer Yudkowsky~~~~Measuring Optimization Power~~~~by Eliezer Yudkowsky~~

Consider the human being. We are a highly complex object with a low probability to have been created by chance - natural selection, however, over millions of years, built up the infrastructure needed to build such a functioning body. This ~~body~~body, as well as other organisms, had the chance (was *selected*) to develop because it is in itself a rather efficient ~~replicator.~~replicator suitable for the environment where it came up.

Or consider the famous chessplaying computer, Deep Blue. Outside of the narrow domain of selecting moves for chess games, it can't do anything impressive: but *as* a chessplayer, it was massively more effective than virtually all humans. It has a high optimization power in the chess domain but almost none in any other field. Humans or evolution, on the ~~otehr~~other hand, are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this *optimization process* abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)

Consider the human being. We are a * rather unlikely*highly complex object with a low probability to have

Or consider the famous chessplaying computer, Deep Blue. Outside of the narrow domain of selecting moves for chess games, it can't do anything impressive: but *as* a chessplayer, it was massively more effective than virtually all humans. It has a high optimization power in the chess domain but almost none in any other field. Humans or ~~evolution~~evolution, on the otehr hand, are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this *optimization process* abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)

- Optimization and the Singularity
~~Optimization~~by Eliezer Yudkowsky- Measuring Optimization Power by Eliezer Yudkowsky

An **optimization process** is ~~a~~any kind of process that systematically comes up with solutions that are ~~higher rather~~better than ~~lower relative to some ordering over outcomes; it hits small targets~~the solution used before. More technically, this kind of process is one that performs searches in a large search space, ~~comes up with outcomes that you would ~~~~not~~~~ expect to see~~hitting small, low probability targets. When this process is gradually guided by ~~random chance, atoms bumping up against each other with no direction or ordering at all. If an entity pushes reality~~some agent into some ~~state — across many contexts, not just by accident — then you could~~specific state, through searching specific targets, we can say it prefers that state.

~~Optimization is a very general notion that encompasses all kinds of order-generating processes other than emergence; optimization is about choosing or selecting outcomes defined as better.~~

~~Probably the~~ The best way to exemplify an optimization process ~~you're most familiar with ~~is ~~that of human intelligence. Humans don't do things randomly: we have~~through a simple example: ~~very specific goals~~Eliezer Yudkowsky suggests natural selection is such a process. Through an implicit preference – better replicators – natural selection searches all the genetic landscape space and ~~rearrange the world in specific ways to meet our goals.~~hit small targets: efficient mutations. Consider the ~~monitor on which you read these words. That monitor is~~human being. We are a *rather unlikely* object to have come about by chance, and so of course, it didn't. ~~Human economies~~Natural selection, over ~~many years~~millions of years, built up the infrastructure needed to build a ~~monitor,~~full functioning body, and ~~then built~~it ended up creating it, because people ~~prefer to be able to see their files. It might not seem so impressive if you're used to it, but there's a lot of cognitive work that goes on behind the scenes.~~

~~Another example of an optimization process would be ~~~~natural selection~~~~, notable for its "first" status if not its ~~~~power~~~~ or ~~~~speed~~~~. Evolution works because organisms that do better at surviving and reproducing propagate more of their traits to the next generation; in this way genes with higher fitness ~~are ~~systematically preferred, and complex machinery bearing the ~~~~strange design signature~~~~ of evolved things can be built up over time.~~

rather efficient replicators. Or consider the famous chessplaying computer, Deep Blue. Outside of the narrow domain of selecting moves for chess games, it can't do anything impressive: but *as* a chessplayer, it was massively more effective than virtually all humans. Humans or evolution are more domain-general optimization processes than Deep Blue, but that doesn't mean they're more effective at chess specifically. (Although note in what contexts this *optimization process* abstraction is useful and where it fails to be useful: it's not obvious what it would mean for "evolution" to play chess, and yet it is useful to talk about the optimization power of natural selection, or of Deep Blue.)

One way to think mathematically about optimization, like evidence, is in information-theoretic bits. The optimization power is the amount of surprise we would have in the result if there were no optimization process present. Therefore we take the base-two logarithm of the reciprocal of the probability of the result. A one-in-a-million solution (a solution so good relative to your preference ordering that it would take a million random tries to find something that good or better) can be said to have log_2(1,000,000) = 19.9 bits of optimization. Compared to a random configuration of matter, any artifact you see is going to be much more optimized than this. The math describes only laws and general principles for reasoning about optimization; as with probability theory, you oftentimes can't apply the math directly.

One way to think mathematically about optimization, like evidence, is in information-theoretic bits. ~~We~~The optimization power is the amount of surprise we would have in the result if there were no optimization process present. Therefore we take the base-two logarithm of the reciprocal of the probability of the result. A one-in-a-million solution (a solution so good relative to your preference ordering that it would take a million random tries to find something that good or better) can be said to have log_2(1,000,000) = 19.9 bits of optimization. Compared to a random configuration of matter, any artifact you see is going to be much more optimized than this. The math describes laws and general principles for reasoning about optimization; as with probability theory, you oftentimes can't apply the math directly.