Keene Macheene

What is the purpose of a brain and how does that effect the evolution of design? Survival It is obvious that the reason any animals have neural systems is because the neural system provides a survival advantage. How does a brain provide an advantage? The simplest neural systems are in effect hard-wired with little or no learning. These simple systems provide reactions or reflexes to environment changes that may be harmful or beneficial.  Attraction toward food or mates, and fleeing from predators is very valuable. As neural systems become more complex and have learning on can ask: What is being learned? The answer is often stated as "better behaviors" and such. A better formulation would be: Brain impart a survival advantage by predicting the future state of the  environment  and pre-reacting to the environment. Here are several examples at varying levels of intelligence: A mouse is hungry and predicts a future state that includes food, and...

In a Subsumptive Regular System (SRS  here )  the  regular array of neurons (cortex) that has the goal of predicting the future state of the subsumptive (cerebrum) system . If the match is strong on the present state of the subsumptive system then the stimulus of the subsumptive system into the predicted state is strong.  In the event of sudden changes in the actual external environment one wants to ignore the predicted state and react to the immediate environment. This has an evolutionary advantage of pre-reacting to the environment and getting a jump on surviving the future state. This document calls out the various axis of selection for algorithms.  Research has yet to show where the decision points are optimal. There are several decision points on the algorithms to select: Connectivity from the Subsumptive to the Cortex The subsumptive part of the SRS is hard-wired to take inputs, compute concept mappings (abstractions) of the external environment an...

Current neural network systems have a general paradigm of being given some input, and then producing a resulting output. A true general AI should act like a biological system and be self-sequencing. The neural system should just free-run, responding to inputs and creating outputs and learning. One method I am experimenting with in Cognate and NuTank is having a hard-wired set of concept maps (a N-dimensional set of neurons used to embody a concept, e.g. foveolar edge angles.) that take inputs from the external world and produce hard-wired reactions as outputs to control a real or simulated robot. Such a system also uses subsumption to aid in much easier design. This is the Concept Map System (CMS) for the purposes of this post. For now lets ignore the idea that the hard-wired concept map system might also be adapting and evolving over time simulating neroplasticity. One then has a large 3D array of neurons that are in acting as a pattern matcher on the entire CMS.  What this ...