"The future ain't what it used to be."

Triplex-Ortho AI Technologies:Chaos/Fuzzy/Neural


There are 3 prominent Artificial Intelligence (AI) technologies that closely fit the basic "triplex orthogonal" system model suggested by the Qabalistic Tree Of Life Neural Network Diagram (QTOLNND). As with all triplex systems that map to the TOL, there is an Active component, a Passive component, and a Neutral component. For those of you who have basic, or advanced knowledge of typical AI techniques....consider the benefits of integrating the following 3 AI techniques into a single, balanced, intelligent platform:

ACTIVE AI - Chaos (Non-Linear) Control
There is a direct mathematical link between Chaos Dynamics, and the fields of fractal computation and closed-loop control systems. When we replace today's simple linearly-programmed control laws with tomorrow's fractally-driven, chaotic dynamical control laws we will see orders of magnitude improvements in system efficiencies.

NEUTRAL AI - Neural Network Topology
The NETWORK is the most prevalent structure throughout all of Nature. Networks of anything permit multiple, parallel paths for Energy (information) to travel. The more branches you have in your networks, the more information they can pass...the more information your networks can process, the "smarter" you are as a being. The neural network is the neutral background structure upon which the energy signals of ACTIVE and PASSIVE intelligence are played-out. Interesting that the word NEURAL is only different from NEUTRAL by one letter, in the middle....T

PASSIVE AI - Fuzzy Logic Inference Engine
Fuzzy logic inference engines are 3-component AI systems. There is the "input fuzzifier function" which converts crisp sensor measurements to fuzzy references. Then there is the "fuzzy rules matrix" which is the heart of the inference engine, since it relates fuzzy inputs to fuzzy outputs. And finally there is the "output defuzzification function" which takes the fuzzy commands generated by the rules matrix, and converts them into crisp command outputs for the system.

Blending these 3 forms of AI into a single entity would create a 3-way dynamic balance that is highly conducive to learning and improvement.