--- "John G. Rose" <[EMAIL PROTECTED]> wrote: > But what I've been thinking and this is probably just reiterating what > someone else has worked through but basically a large part of intelligence > is chaos control, chaos feedback loops, operating within complexity. > Intelligence is some sort of delicate multi-vectored balancing act between > complexity and projecting, manipulating, storing/modeling, NN training, > genetic learning of the chaos and applying chaos in an environment and > optimizing it's understanding and application of. The more intelligent, the > better handle an entity has on the chaos. An intelligent entity can have > maximal effect with minimal energy expenditure on its environment in a > controlled manner; intelligence (or the application of) or even perhaps > consciousness is the real-time surfing of "buttery effects".
I think the ability to model a chaotic process depends not so much on "intelligence" (whatever that is) as it does on knowledge of the state of the environment. For example, a chaotic process such as x := 4x(1 - x) has a really simple model. Your ability to predict x after 1000 iterations depends only on knowing the current value of x to several hundred decimal places. It is this type of knowledge that limits our ability to predict (and therefore control) the weather. I think there is a different role for chaos theory. Richard Loosemore describes a system as intelligent if it is complex and adaptive. Shane Legg's definition of universal intelligence requires (I believe) complexity but not adaptability. From a practical perspective I don't think it matters because we don't know how to build useful, complex systems that are not adaptive. For example, large software projects (code + human programmers) are adaptive in the sense that you can make incremental changes to the code without completely breaking the system, just as we incrementally update DNA or neural connections. One counterexample is a mathematical description of a cryptographic system. Any change to the system renders any prior analysis of its security invalid. Such systems are necessarily brittle. Out of necessity, we build systems that have mathematical descriptions simple enough to analyze. Stuart Kaufmann [1] noted that complex systems such as DNA tend to evolve to the boundary between stability and chaos, e.g. a Lyapunov exponent near 1 (or its approximation in discrete systems). I believe this is because overly stable systems aren't very complex (can't solve hard problems) and overly chaotic systems aren't adaptive (too brittle). [1] Kauffman, Stuart A., “Antichaos and Adaptation”, Scientific American, Aug. 1991, p. 64. -- Matt Mahoney, [EMAIL PROTECTED] ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=231415&user_secret=fabd7936
