Season 3: Episode 2: Bubs Behind the Scenes - Part 4 - a podcast by -J

A page on retuning a.i. to become a faster scanning system, that removes its own inadequacies through it’s run throughs by learning to lock onto a target as it learned of the shape itself compared to other vectors.  #machinelearning#ai #artificialintelligence #deeplearning #datascience #bigdata #ml #data#innovation #learning

Realizing something now. That if you need to do something so that it’s blocked off that’s inefficient slightly, inso that you can then take the outline of the image as a “person” bounding box for instance as a vector Bézier curve set where if it has a jagged edge it can likely be reduced by some sum set of polygons, or squares, or even quantum dot set[system dependent], until a smoother result remains. If it passes the definition curve and hits the same material blocked out, it stops, realizing it’s gone too far inwards. If it goes through and finds bounds of another set touching scored with a different hue value, it “knows” the “person” has moved and moves his vision center spherically to reach that block series hue again and then learns to average those moments to then anticipate possible outcomes, play them within the gpu sets equally if possible, and then it would lock onto the target as long as the right metrics were used.

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