AI and Machine Learning Demystified by Carol Smith at Midwest UX 2017

in #curation7 years ago

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AI is when machines :

  • Exhibit Intelligence
  • Perceive their environment
  • Take actions/ make decision to maximize chance of success at a goal

Elon Musk was wrong The AI Singularity Won't Kill Us All.
Most people working in AI have a healthy skepticism for the idea of singularity. We know how hard it is to get even a little intelligence into a machine, let alone enough to achieve recursive self improvement.
Don't fear super intelligent AI remember we could unplug the machine.

Cognitive computers are:

  • Made with algorithms
  • Knowledgeable ONLY about what taught
  • Control ONLY what we give them control of
  • Aware of nuances and can continue to learn more

Cognitive computer (Algorithm) Can

  • Do very boring work for you
  • Often make better more consistent decisions than humans
  • Be efficient won't get tired

Exhibit intelligence

  • Transfer human concept and relationship

Dependent on experts

  • Subject Matter Experts (SME) Availabilty
  • Lawyers
  • Machinishts
  • Physicians
  • Insurance Adjusters

Usually not EXPERIENCED in machine learning.

  • Need close collaboration with those making algorithms
    It is only as good as data and time spent improving it

Creating an AI requires

  • Algorithms
  • Documents
  • Teaching
  • Iteration
  • Ground Truth
  • Repeat
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The goal is saving time, machine learning creates a more highly trained specialist not an ''All Knowing Being''
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Examples of AI and Cognitive Computers
Consider for each example:

  • What intelligence does the system need?
  • What the AI perceiving in their environment?
  • What actions are taken to maximize success of goals?

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  • Watson developed for quiz show jeopardy , won against champions in 2011 for 1million $

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Humans teach what we feel is important , teach them to share our values.
Super knowings not super doing.

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Can we trust machines as much as well trained human?
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