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Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2006

Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2006. Lecture 1 September 18, 2006. Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall06/ann/. Japan's humanoid robots Better than people

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Artificial Neural Networks ECE.09.454/ECE.09.560 Fall 2006

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  1. Artificial Neural NetworksECE.09.454/ECE.09.560Fall 2006 Lecture 1September 18, 2006 Shreekanth Mandayam ECE Department Rowan University http://engineering.rowan.edu/~shreek/fall06/ann/

  2. Japan's humanoid robots Better than people Dec 20th 2005 | TOKYOFrom The Economist print edition Why the Japanese want their robots to act more like humans

  3. Why the Japanese want their robots to act more like humans HER name is MARIE, and her impressive set of skills comes in handy in a nursing home. MARIE can walk around under her own power. She can distinguish among similar-looking objects, such as different bottles of medicine, and has a delicate enough touch to work with frail patients. MARIE can interpret a range of facial expressions and gestures, and respond in ways that suggest compassion. Although her language skills are not ideal, she can recognise speech and respond clearly. Above all, she is inexpensive. Unfortunately for MARIE, however, she has one glaring trait that makes it hard for Japanese patients to accept her:

  4. Why the Japanese want their robots to act more like humans ………………….she is a flesh-and-blood human being from the Philippines. If only she were a robot instead.

  5. Harveian Oration In celebration of cerebration by Professor Colin Blakemore, presented at the Royal College of Physicians, London, UK, on Oct 18, 2005 www.thelancet.com Vol 366 Dec 10, 2005

  6. Plan • What is artificial intelligence? • Course introduction • Historical development – the neuron model • The artificial neural network paradigm • What is knowledge? What is learning? • The Perceptron • Widrow-Hoff Learning Rule • The “Future”….?

  7. Systems that think rationally • Logic • Systems that think like humans • Cognitive modeling • Systems that act rationally • Decision theoretic agents • Systems that act like humans • Natural language processing • Knowledge representation • Machine learning Artificial Intelligence

  8. Course Introduction • Why should we take this course? • PR, Applications • What are we studying in this course? • Course objectives/deliverables • How are we conducting this course? • Course logistics • http://engineering.rowan.edu/shreek/fall06/ann/

  9. Course Objectives • At the conclusion of this course the student will be able to: • Identify and describe engineering paradigms for knowledge and learning • Identify, describe and design artificial neural network architectures for simple cognitive tasks

  10. Biological Origins

  11. Biological Origins

  12. History/People

  13. Indicate Desired Outputs Determine Synaptic Weights Predicted Outputs Neural Network Paradigm Stage 1: Network Training Artificial Neural Network Present Examples “knowledge” Stage 2: Network Testing Artificial Neural Network New Data

  14. ANN Model x Input Vector y Output Vector Artificial Neural Network f Complex Nonlinear Function f(x) = y “knowledge”

  15. Single output ANN x y 1-out-of-c selector Coder Associator ANN ANN x x yc yc y2 y2 y1 y1 ANN x y Popular I/O Mappings

  16. The Perceptron Activation/ squashing function wk1 Bias, bk x1 wk2 x2 S S j(.) Output, yk Inputs uk Induced field, vk wkm xm Synaptic weights

  17. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  18. “Learning” Mathematical Model of the Learning Process Intitialize: Iteration (0) ANN [w]0 x y(0) [w] x y Iteration (1) [w]1 x y(1) desired o/p Iteration (n) [w]n x y(n) = d

  19. Error-Correction Learning Desired Output, dk (n) wk1(n) Activation/ squashing function x1 (n) Bias, bk wk2(n) x2 + Output, yk (n) S S j(.) Inputs Synaptic weights - Induced field, vk(n) wkm(n) Error Signal ek (n) xm

  20. Pattern Association Pattern Recognition Function Approximation Filtering x2 x2 2 2 DB 1 1 DB x1 x1 Learning Tasks Classification

  21. Perceptron Training Widrow-Hoff Rule (LMS Algorithm) w(0) = 0 n = 0 y(n) = sgn [wT(n) x(n)] w(n+1) = w(n) + h[d(n) – y(n)]x(n) n = n+1 Matlab Demo

  22. The Age of Spiritual MachinesWhen Computers Exceed Human Intelligenceby Ray Kurzweil | Penguin paperback | 0-14-028202-5 |

  23. Summary

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