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Artificial Intelligence in Recognition Tasks: Neural Networks and Intelligent Searching

Explore the use of neural networks in recognition tasks and intelligent searching in artificial intelligence. Learn about knowledge representation, semantic nets, decision trees, and training algorithms. Discover the capabilities and division of labor between humans and computers in AI.

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Artificial Intelligence in Recognition Tasks: Neural Networks and Intelligent Searching

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  1. Chapter 13 Artificial Intelligence

  2. Artificial Intelligence – Figure 13.1 The Turing Test

  3. Artificial Intelligence – A Division of Labor – Figure 13.2 Human and Computer Capabilities

  4. Artificial Intelligence – Knowledge Representation – Figure 13.3 – A Semantic Net Representation

  5. Artificial Intelligence – Recognition Tasks – Figure 13.4 A Neuron

  6. Artificial Intelligence – Recognition Tasks – Figure 13.5 One Neuron with Three Inputs

  7. Artificial Intelligence – Recognition Tasks – Figure 13.6 A Neural Network for Comparing Two Characters

  8. Artificial Intelligence – Recognition Tasks – Figure 13.7 The Truth Table for XOR

  9. Artificial Intelligence – Recognition Tasks - Figure 13.8 An Attempt at an XOR Perception

  10. Artificial Intelligence – Recognition Tasks – Figure 13.9 Neural Net for XOR

  11. Recognition Tasks – The Optical Character System Used By Banks Requires A Special Set of Characters. These Characters Allow for Exact Pattern Matching

  12. Artificial Intelligence – Recognition Tasks – Training Data – Machine Recognition of Handwritten Characters

  13. Artificial Intelligence – Recognition Tasks – Practice Problem –If Input Line 1 is Stimulated in the Above Neural Network (and Line 2 is Not Stimulated), Will the Output Line fire?

  14. Reasoning Tasks – Intelligent Searching – Figure 13.10 Decision Tree for Sequential Search

  15. Reasoning Tasks – Intelligent Searching – Figure 13.11 – Decision Tree for Binary Search

  16. Reasoning Tasks – Intelligent Searching – Figure 13.12 A Decision Tree with Exponential Growth

  17. Conclusion – Exercises – Use An Englishlike Formal Language to Represent the Knowledge Explicitly Contained in the Above Semantic Net

  18. Conclusion – Exercises – In the Above Neural Network, Which Event or Events Will Cause Node N3 To Fire?

  19. Conclusion – Exercises – Challenge Work – Figure 13.13 The AND Truth Table

  20. Conclusion – Challenge Works – Figure 13.14 A Skeleton for the AND Perceptron

  21. Conclusion – Challenge Work – Figure 13.15 A General Perceptron for a Training Algorithm

  22. Conclusion – Challenge Work – Figure 13.16 Initial Configuration of Perceptron to be Trained

  23. Conclusion – Challenge Work – Figure 13.17 Configuration of the Perceptron After One Adjustment

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