Lifespan Development

Development doesn’t stop at adulthood like I once thought; it keeps going. Lifespan development  proceeds by a process of resolving conflicts and overcoming obstacles. This means:

1. Resolving the conflicts between the teachings of my parents and what I find to be true. For instance, they used to tell me: ‘if mistakes can be made -they will!’ I found out later that this was a fallacy of mistaking what’s possible for what’s probable. Just because something is possible doesn’t make it extremely likely to occur. Now I can either decide to modify this teaching or reject it. That’s up to me. Sometimes it’s a good idea to err on the side of caution (that’s what I think they were really trying to say). It doesn’t do me any good to reject everything my parents taught me or get stuck quarreling about one thing or another.
    
2. Resolving differences between the way things are and the way I want them to be (or expect them to be). The direction I want to go in may not be the same as the one my employers want.
    
3. Resolving the conflicts between my immediate biological or sexual needs and socially acceptable means of satisfying these needs (courtship and dating for example).
    
4. Integration: Learning doesn’t stop after school -it changes. Now it’s more like a process of integration –accepting new information and modifying previously held beliefs. Clinging to previously held beliefs is a way of blocking development. Receptivity to the thoughts and ideas of others, without going instantly judgmental, is the first step to integration and ongoing development.

Bayesian AI

Bayesian Program Learning BPL: probability-based program that is able to deal with variation during recognition tasks –like the ability to recognize a Segue from different angles or embedded in scenes of differing complexity, after seeing it only once in a magazine. Traditional AI required storing many occurrences of an object or event ( E ) and many occurrences of ‘not E or ‘variations of E’. In order to correctly identify E under different conditions.

                  E plus ( €, ∑, £, Æ, È, Ę, Ǝ, Ǯ, ʒ, Ξ, Σ, З, Э, Ѥ, Ѱ, ₣, ₤ ,€, 8)

•   A 3-year old can correctly identify an event ( E ) with 95% accuracy after seeing it only once – an example of single-trial learning.
•   Traditional AI can identify event ( E ) with only 75% accuracy with only one stored     occurrence. P(H/D) where event ( E ) is the hypothesis (H) and (D) is a stored data-point.
•   Bayesian AI can identify an event ( E ) with 95% accuracy after one occurrence.

Bayes recognition is probabilistic –it compares the probabilities of getting E from different variations and contexts of ( E ) and returns the highest value P(D/H). Bayes probability comes closer to human recognition memory during single trial learning. (LA Times)