By Ben Goertzel
This ebook summarizes a community of interrelated principles which i've got constructed, on and off, over the last 8 or ten years. The underlying subject matter is the mental interaction of order and chaos. Or, to place it otherwise, the interaction of deduction and induction. i'm going to attempt to clarify the connection among logical, orderly, unsleeping, rule-following cause and fluid, self organizing, habit-governed, subconscious, chaos-infused instinct. My earlier books, The constitution of Intelligence and The Evolving brain, in brief touched in this courting. yet those books have been essentially fascinated about different issues: SI with developing a proper language for discussing mentality and its mechanization, and EM with exploring the function of evolution in inspiration. They danced round the edges of the order/chaos challenge, with out ever absolutely stepping into it. My objective in penning this booklet was once to move on to the middle of psychological approach, "where angels worry to tread" -- to take on the entire sticky concerns which it's thought of prudent to prevent: the character of attention, the relation among brain and truth, the justification of trust structures, the relationship among creativity and psychological illness,.... All of those matters are handled right here in an easy and unified method, utilizing a mixture of techniques from my prior paintings with rules from chaos idea and intricate platforms science.
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Additional resources for Chaotic Logic: Language, Thought, and Reality from the Perspective of Complex Systems Science
10 slightly more complex than 000 ... 0, because there are more different economical ways of computing the former (for instance, one may repeat lO's, or one may repeat 01 's and then append a 0 at the end). It considers all the different ways of "looking at" a sequence. For future reference, let us define the structure St(D;r,s) of a discrete dynamical system D on the interval (r,s) as the set of all approximate patterns in the ordered tuple [D(r), ... ,D(s)], where D(t) denotes the state of S at time t.
Structural complexity differs significantly from all of the algorithmic information based complexity measures discussed above. It does not refer to one distinguished way of computing a sequence -- the shortest, the most "sophisticated," etc. Rather, it considers all possible economical strategies for computing a sequence, where an economical strategy for computing x -- or more succinctly a pattern in x -- may be defined as follows, given a fixed universal Turing machine U. 000zOOO... (it is understood that z extends to the right of the tape head of U), so that the length of y plus the length of z is less than the length of x.
The way the dual network unifies memory reorganization with genetic optimization has not previously been discussed; nor has the dynamics of barrier formation and its relationship with consciousness, language and perception (to be explored in Chapter Six). 4 PREDICTION The dual network model, as just presented, dismisses the problem of predicting the future rather cursorily. But this is not entirely justified. Prediction of the behavior of a complex system is an incredibly very difficult task, and one which lies at the very foundation of intelligence.