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AI: The Good, the Bad, and the Practical for CloudExpo 2018

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Artificial intelligence, machine learning, neural networks. We're in the midst of a wave of excitement around AI such as hasn't been seen for a few decades. But those previous periods of inflated expectations led to troughs of disappointment. This time is (mostly) different.

Applications of AI such as predictive analytics are already decreasing costs and improving reliability of industrial machinery. Pattern recognition can equal or exceed the ability of human experts in some domains. It's developing into an increasingly commercially important technology area. (Although it's also easy to look at wins in specific domains and generalize to an overly-optimistic view of AI writ large.)

In this session, Red Hat Technology Evangelist for Emerging Technology Gordon Haff will examine the AI landscape and identify those domains and approaches that have seen genuine advance and why. He'll also discuss some of the specific ways in which both organizations and individuals are getting up to speed with AI today.

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AI: The Good, the Bad, and the Practical for CloudExpo 2018

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  2. 2. CONSUMER IoT/ MOBILITY “Software is eating the world” “Data is the new oil”
  3. 3. Philosophy Logic, methods of reasoning Mathematics Algorithms, probability Psychology Behaviorism, cognitive psychology Economics Utility/game/decision theory, operations research Linguistics Grammar, knowledge representation Control theory Objective functions, feedback loops
  4. 4. 1956 Dartmouth Summer workshop 1952-1969 Early enthusiasm Lisp, formal logic vs. working models Partially based on Russell & Norvig 1966-1973 Reality sets in Lack of real world context Computing power limits 1969-1979 Knowledge-based systems Expert systems, language Early-mid 1980s Becomes an industry Ambitious goals Neural nets return Late 80s AI winter Collapse of Lisp machine market Failures: expert systems, Fifth Generation project, etc. 1995- Intelligent agents Modular approaches Internet cross-pollination 21st century Data, data, data Machine learning Deep learning
  5. 5. Thinking Humanly Cognitive modeling Informed by neurophysiology Thinking Rationally Logicist tradition Intelligence based on logical relationships Acting Humanly Turing Test Computer vision, robotics, language, reasoning Acting Rationally Rational agent approach Achieve best or best expected outcome
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  10. 10. Backpropagation
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  13. 13. Supervised Learning Reinforcement Learning (semi-supervised)
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  16. 16. http://nautil.us/issue/40/learning/is-artificial-intelligence-permanently-inscrutable
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