Approaches to A. I. Thinking like humans Cognitive science Neuron level Neuroanatomical level Mind level Thinking rationally Aristotle, syllogisms Logic.

Slides:



Advertisements
Similar presentations
1 Connectionist Modeling Jenny Hayes. 2 Overview What are connectionist models? How do they work? How are they used in psychology?
Advertisements

Neural Network I Week 7 1. Team Homework Assignment #9 Read pp. 327 – 334 and the Week 7 slide. Design a neural network for XOR (Exclusive OR) Explore.
Computer Science Department FMIPA IPB 2003 Neural Computing Yeni Herdiyeni Computer Science Dept. FMIPA IPB.
Artificial Intelligence ICS 61 February, 2015
Tuomas Sandholm Carnegie Mellon University Computer Science Department
Artificial Neural Networks
Neural NetworksNN 11 Neural Networks Teacher: Elena Marchiori R4.47 Assistant: Kees Jong S2.22
Connectionist models. Connectionist Models Motivated by Brain rather than Mind –A large number of very simple processing elements –A large number of weighted.
Carla P. Gomes CS4700 CS 4700: Foundations of Artificial Intelligence Prof. Carla P. Gomes Module: Neural Networks: Concepts (Reading:
1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.
Rutgers CS440, Fall 2003 Neural networks Reading: Ch. 20, Sec. 5, AIMA 2 nd Ed.
Artificial Neural Networks Artificial Neural Networks are (among other things) another technique for supervised learning k-Nearest Neighbor Decision Tree.
Introduction to Neural Network Justin Jansen December 9 th 2002.
(Page 554 – 564) Ping Perez CS 147 Summer 2001 Alternative Parallel Architectures  Dataflow  Systolic arrays  Neural networks.
Introduction to Neural Networks John Paxton Montana State University Summer 2003.
Introduction to Neural Networks CMSC475/675
1 Introduction to Artificial Neural Networks Andrew L. Nelson Visiting Research Faculty University of South Florida.
Chapter 14: Artificial Intelligence Invitation to Computer Science, C++ Version, Third Edition.
Neural Networks AI – Week 21 Sub-symbolic AI One: Neural Networks Lee McCluskey, room 3/10
Computer Science and Engineering
Artificial Neural Nets and AI Connectionism Sub symbolic reasoning.
Artificial Neural Networks An Overview and Analysis.
Introduction to Neural Networks Debrup Chakraborty Pattern Recognition and Machine Learning 2006.
Explorations in Neural Networks Tianhui Cai Period 3.
Artificial Neural Network Yalong Li Some slides are from _24_2011_ann.pdf.
Machine Learning Dr. Shazzad Hosain Department of EECS North South Universtiy
1 Machine Learning The Perceptron. 2 Heuristic Search Knowledge Based Systems (KBS) Genetic Algorithms (GAs)
Artificial Neural Networks. The Brain How do brains work? How do human brains differ from that of other animals? Can we base models of artificial intelligence.
1 Chapter 11 Neural Networks. 2 Chapter 11 Contents (1) l Biological Neurons l Artificial Neurons l Perceptrons l Multilayer Neural Networks l Backpropagation.
Artificial Neural Networks An Introduction. What is a Neural Network? A human Brain A porpoise brain The brain in a living creature A computer program.
Computer Science 101 Computer Systems Organization.
Neural Networks in Computer Science n CS/PY 231 Lab Presentation # 1 n January 14, 2005 n Mount Union College.
CS 478 – Tools for Machine Learning and Data Mining Perceptron.
PARALLELIZATION OF ARTIFICIAL NEURAL NETWORKS Joe Bradish CS5802 Fall 2015.
Lecture 5 Neural Control
Artificial Neural Networks Chapter 4 Perceptron Gradient Descent Multilayer Networks Backpropagation Algorithm 1.
A Perspective on the Future of Massively Parallel Computing Presented by: Cerise Wuthrich June 23, 2005.
COSC 4426 AJ Boulay Julia Johnson Artificial Neural Networks: Introduction to Soft Computing (Textbook)
Introduction to Neural Networks Freek Stulp. 2 Overview Biological Background Artificial Neuron Classes of Neural Networks 1. Perceptrons 2. Multi-Layered.
COMP53311 Other Classification Models: Neural Network Prepared by Raymond Wong Some of the notes about Neural Network are borrowed from LW Chan’s notes.
Perceptrons Michael J. Watts
Artificial Intelligence CIS 342 The College of Saint Rose David Goldschmidt, Ph.D.
Minds and Computers Discovering the nature of intelligence by studying intelligence in all its forms: human and machine Artificial intelligence (A.I.)
Neural Networks. Background - Neural Networks can be : Biological - Biological models Artificial - Artificial models - Desire to produce artificial systems.
Where are we? What’s left? HW 7 due on Wednesday Finish learning this week. Exam #4 next Monday Final Exam is a take-home handed out next Friday in class.
1 Azhari, Dr Computer Science UGM. Human brain is a densely interconnected network of approximately neurons, each connected to, on average, 10 4.
NEURONAL NETWORKS AND CONNECTIONIST (PDP) MODELS Thorndike’s “Law of Effect” (1920’s) –Reward strengthens connections for operant response Hebb’s “reverberatory.
1 Neural Networks MUMT 611 Philippe Zaborowski April 2005.
INTRODUCTION TO NEURAL NETWORKS 2 A new sort of computer What are (everyday) computer systems good at... and not so good at? Good at..Not so good at..
Business Intelligence and Decision Support Systems (9 th Ed., Prentice Hall) Chapter 6: Artificial Neural Networks for Data Mining.
Information Processing
Introduction to Neural Networks
Neural Networks.
Introduction to Artificial Neural Network Session 1
Fall 2004 Perceptron CS478 - Machine Learning.
Other Classification Models: Neural Network
Other Classification Models: Neural Network
Joost N. Kok Universiteit Leiden
Neural Networks Dr. Peter Phillips.
Dr. Unnikrishnan P.C. Professor, EEE
CSSE463: Image Recognition Day 17
XOR problem Input 2 Input 1
Intelligent Leaning -- A Brief Introduction to Artificial Neural Networks Chiung-Yao Fang.
CSSE463: Image Recognition Day 17
CS 621 Artificial Intelligence Lecture /10/05 Prof
CSSE463: Image Recognition Day 17
The Network Approach: Mind as a Web
Introduction to Neural Network
David Kauchak CS158 – Spring 2019

Presentation transcript:

Approaches to A. I. Thinking like humans Cognitive science Neuron level Neuroanatomical level Mind level Thinking rationally Aristotle, syllogisms Logic “Laws of thought” Acting like humans Understand language Play games Control the body The Turing Test Acting rationally Business approach Results oriented 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 10 billion neurons (in humans) Each one has an electro-chemical state

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

Modeling the Neural Network

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

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

The Percepton

The Perceptron

Perceptrons can be combined to make a network

How to “program” a Perceptron?

Perceptron Learning Rule InputOutput x1x2x3 1 if avg(x1, x2)>x3, 0 otherwise Training data: Valid weights: Perceptron function:

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

Feed-forward network

Recurrent Neural Networks

Hopfield Network

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

On Learning the Past Tense of English Verbs

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