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Approaches to A. I.

Approaches to A. I. Human Rational. Thinking Acting. (Artificial) Neural Networks. Biological inspiration Synthetic networks non-Von Neumann Machine learning Perceptrons – MATH Perceptron learning Varieties of Artificial Neural Networks. Brain - Neurons.

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Approaches to A. I.

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  1. Approaches to A. I. Human Rational Thinking Acting

  2. (Artificial) Neural Networks • Biological inspiration • Synthetic networks • non-Von Neumann • Machine learning • Perceptrons – MATH • Perceptron learning • Varieties of Artificial Neural Networks

  3. Brain - Neurons 10 billion neurons (in humans) Each one has an electro-chemical state

  4. Brain – Network of Neurons Each neuron has on average 7,000 synaptic connections with other neurons. A neuron “fires” to communicate with neighbors.

  5. Modeling the Neural Network

  6. von Neumann Architecture Separation of processor and memory. One instruction executed at a time.

  7. Animal Neural Architecture von Neumann Birds and bees (and us) Each neuron has state and processing Massively parallel, massively interconnected. • Separate processor and memory • Sequential instructions

  8. The Percepton • A simple computational model of a single neuron. • Frank Rosenblatt, 1957 • The entries in are usually real-valued (not limited to 0 and 1)

  9. The Perceptron

  10. Perceptrons can be combined to make a network

  11. How to “program” a Perceptron? • Programming a Perceptron means determining the values in . • That’s worse than C or Fortran! • Back to induction: Ideally, we can find from a set of classified inputs.

  12. Perceptron Learning Rule Training data: Valid weights: Perceptron function:

  13. Varieties of Artificial Neural Networks • Neurons that are not Perceptrons. • Multiple neurons, often organized in layers.

  14. Feed-forward network

  15. Recurrent Neural Networks

  16. Hopfield Network

  17. On Learning the Past Tense of English Verbs • Rumelhart and McClelland, 1980s

  18. On Learning the Past Tense of English Verbs

  19. On Learning the Past Tense of English Verbs

  20. Neural Networks • Alluring because of their biological inspiration • degrade gracefully • handle noisy inputs well • good for classification • model human learning (to some extent) • don’t need to be programmed • Limited • hard to understand, impossible to debug • not appropriate for symbolic information processing

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