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)