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Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc.

Artificial Intelligence and Neural Networks. Humans: Decision Making Process. Data: Facts pertinent to the decision at hand. DSS. Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc.

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Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc.

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  1. Artificial Intelligence and Neural Networks Humans: Decision Making Process Data: Facts pertinent to the decision at hand. DSS Algorithms: Math/Flow Chart stuff that helps the tools help the humans make decisions. Tools: Computers and IT. VB, VBA, Excel, InterDev, Etc.

  2. MACHINE INTELLIGENCE Will computers become as smart as humans within the next 50 years?

  3. IBM’S “DEEP BLUE” CHESS PLAYING COMPUTER A couple of years ago (1997), IBM’s Deep Blue computer beat world chess champion Gary Kasporov in a chess match. Does that mean Deep Blue is “smarter” than Kasporov when it comes to playing chess?

  4. IBM’S “DEEP BLUE” CHESS PLAYING COMPUTER What if I told you Deep Blue has to look at a million times more scenarios than Kasporov to settle on a move? See http://www.ishipress.com/hamlet.htm

  5. Raw power

  6. Artificial Intelligence • Artificial intelligence is behavior by a machine that, if performed by a human being, would be called intelligent • "Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better" (Rich and Knight [1991]) • AI is basically a theory of how the human mind works (Mark Fox)

  7. Objectives of Artificial Intelligence (Winston and Prendergast [1984]) • Make machines smarter (primary goal) • Understand what intelligence is (Nobel Laureate purpose) • Make machines more useful (entrepreneurial purpose)

  8. Signs of Intelligence • Learn or understand from experience • Make sense out of ambiguous or contradictory messages • Respond quickly and successfully to new situations • Use reasoning to solve problems

  9. Signs of Intelligence (cont’d) • Deal with perplexing situations • Understand and Infer in ordinary, rational ways • Apply knowledge to manipulate the environment • Think and reason • Recognize the relative importance of different elements in a situation

  10. Turing Test for Intelligence A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, could not determine which is which

  11. AI Computing • Based on symbolic representation and manipulation • A symbol is a letter, word, or number represents objects, processes, and their relationships • Objects can be people, things, ideas, concepts, events, or statements of fact • Create a symbolic knowledge base

  12. AI Computing (cont’d) • Uses various processes to manipulate the symbols to generate advice or a recommendation • AI reasons or infers with the knowledge base by search and pattern matching • Hunts for answers (Algorithms often used in search)

  13. Some interesting AI Web Destinations AI software and FAQshttp://www.cs.cmu.edu/Groups/AI/html/faqs/ai/ (fairly techie) American Association for Artificial Intelligencehttp://www.aaai.org (fairly general) PC Artificial Intelligence magazinehttp://www.pcai.com/pcai (just right for OMIS 661, in my opinion) The AI Laboratory at MIT: http://www.ai.mit.edu

  14. An Overview ofNeural Computing • Constructing computers that mimic certain processing capabilities of the human brain • Knowledge representations based on • Massive parallel processing • Fast retrieval of large amounts of information • The ability to recognize patterns based on historical cases Neural Computing = Artificial Neural Networks (ANNs)

  15. Learning Three Tasks (over-simplified) 1. Compute Outputs 2. Compare Outputs with Desired Targets 3. Adjust Weights and Repeat the Process

  16. Set the weights by either some rules or randomly • Set Delta = Error = actual output minus desired output for a given set of inputs • Objective is to Minimize the Delta (Error) • Change the weights to reduce the Delta • Information processing: pattern recognition • Different learning algorithms

  17. Benefits of Neural Networks • Usefulness for pattern recognition, learning, classification, generalization and abstraction, and the interpretation of incomplete and noisy inputs • Specifically - character, speech and visual recognition • Potential to provide some of human problem solving characteristics • Ability to tackle new kinds of problems • Robustness • Fast processing speed • Flexibility and ease of maintenance • Powerful hybrid systems

  18. Limitations ofNeural Networks • Do not do well at tasks that are not done well by people • Lack explanation capabilities • Limitations and expense of hardware technology restrict most applications to software simulations • Training times can be excessive and tedious • Usually requires large amounts of training and test data

  19. Some interesting Neural Web Destinations Brainmakerhttp://www.calsci.com Neural Works Professional II PlusNeuralware, Inc

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