The mistakes that AI Neural Nets make discussed.

Could this be a problem?

Only if you don’t supervise your machines.


2 responses to “The mistakes that AI Neural Nets make discussed.

  1. “Thinking” involves the process of extrapolating conclusions from data. Computers are excellent at gathering data – exactly as they are programmed.
    The data evaluation process is more problematic – ‘neural nets’ were developed to calculate *probabilistic* conclusions when data was found to be insufficient for an exact (logically and/or mathematically) conclusion.
    This occurs when there are multiple possible conclusions that can be extracted from a given set of data, each with it’s own ‘likelihood’.
    Neural nets require some additional data (past history, user preferences, contextual data, etc.) that provide criteria for evaluating likelihoods of possible conclusions as a prerequisite to selecting the ‘best’ answer.
    The problem of including ‘ethics’ in the evaluation processes of the neural nets is the same as it is in human beings. The ‘additional data’ (aka ‘Principles’) must be ranked/rated in a hierarchy that identifies the relative importance of each as a constraint on the process.
    The earliest clear example I can find of the establishment of a hierarchy of considerations for the application of what has come to be called ‘fuzzy logic’ is the Laws of Robotics formulated by Dr. Isaac Asimov.
    In the end, Ethics are defined by the Principle assigned the highest priority.
    For myself, that highest principle is that ‘innocent human life is the most valuable.’
    In effect this is similar to Asimov’s First Law of Robotics: “A robot may not injure a human being or, through inaction, allow a human being to come to harm.”

  2. very nice Tadchem, but you always make sense

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