Phil 11.30.15

7:00 – 2:30: ???

  • Introduction to Learning, Nearest Neighbors
    • Learning based on observations of regularity (Bulldozer Computing)
      • Nearest Neighbor
        • Pattern Recognition
      • Neural Networks
      • Boosting
    • Learning based on constraint (Human-Like)
      • One Shot Learning
      • Explanation-based learning
    • Pattern Recognition
      • Feature detector produces a vector of values.
      • Fed into a Comparator which tests the new vector against a library of other vectors
      • Can use decision boundaries
      • If something is similar in some respects, it is likely to be similar in other respects.
      • Robotic motion is a search problem these days??
  • Work
    • Standard first-day stuff
    • Discussions with Aaron about design
    • And the interesting thought for the day:
      • Do we need a sort of crowd-sourced weighting determination of machine ethics? Right now, the person that writes the code for the first self-driving car that decides the runaway trolley problem could reasonably be thought of as having committed premeditated murder. But what if we all together set those outcomes, in a way that reflected our current culture and local values?
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