Machine’s and Ideas<\/p>\n
Modern computers can’t integrate arguments from different sources into new conclusions. They are unable to create comparatives, metaphors, or analogies, because they are essentially Turing machines (a hypothetical device that manipulates symbols on a strip of tape according to a set of rules, i.e. linear logic).\u00a0The storage requirement for\u00a0associative memory in a hierarchical structure increases exponentially as details add up.\u00a0Data contained in capsulated in hierarchical structures, like the internet, has\u00a0no comparative capabilities.<\/p>\n
Biological Systems are Knowledge Based Polylogical Learning Systems<\/p>\n
Hierarchical deductive inference system, like a computer, has only one way to look at things, but learning systems integrate patterns from external and internal events, and\u00a0compare experiences to create new knowledge. It then uses the knowledge generated to transcend logical domain and apply the map to a new system. Biological systems create an abstract conceptual map of a solution and apply it no a new context.\u00a0For example, a toddler taking the experiences objects falling combined with experience with the application of force to an object to come to the conclusion that when he or she pushes their plate off the table; it will fall onto the floor creating a mess.<\/p>\n
Humans simulate “autopoipsis” (self organization a learning system develops through survival) in conversation. We abstract structural similarities between language and the adaptive behavior of survival. Data storage in cognitive systems can be thought of as generative, in the way we create conceptual symbols, rather than transcribe every event. For example, I might\u00a0be read\u00a0a long article, but I will probably only remember general idea as a sequence symbolic representations of the data I find relevant… (i.g. If I wasn’t taking notes, I would probably only remember this as a long article about how people are complex, and machines are dumb.)<\/p>\n
Deep Blue Cheated,\u00a0Virtual Reality\u00a0Adapts, after that Everything gets Fuzzy<\/p>\n
When you ask a person to\u00a0factor 21, we don’t have to try every number until we get it right.\u00a0A computer’s approach to problem solving is to test every possible solution, and though they can do this with increasing speed, it is an inefficient approach. While current computer technology does not think like we do, there are some similarities to our symbolic memory and the way some virtual reality systems are generating dynamic maps and dialogue. New “Pile Systems,” store data as input\/output patterns.<\/p>\n
On the last two pages Kreig describes Fuzzy Logic (which I cannot differentiate from a Pile System) and predicts the rise of the machines…<\/p>\n
What’s Bugging Me About All That<\/p>\n
He says “high end computers can handle 14 dimensions” (p.24), which seems to conflict with his premise of the mono-logical nature of computers?<\/p>\n
At the top of page 24 Kreig says “knowledge system must be able to analyze data and create new data from it,” but isn’t that what a computer does, compare data\u00a0with a\u00a0function that generates an output? It does not create a new idea, just applies an existing formula to a pre-categorized set of variables, but doesn’t it\u00a0generate new data?<\/p>\n