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Tools:Computers andIT. VB, VBA,
Excel, InterDev,Etc.
Humans:DecisionMakingProcess
Algorithms:Math/Flow Chart
stuff that helps thetools help the humans
make decisions.
Artificial Intelligence and Neural Networks
DSS
Data:Facts pertinentto the decision
at hand.
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MACHINE INTELLIGENCE
Will computers become as smart as humans within the next 50
years?
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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?
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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
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Raw power
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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)
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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)
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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
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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
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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
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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
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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)
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AI software and FAQs http://www.cs.cmu.edu/Groups/AI/html/faqs/ai/(fairly techie)
American Association for Artificial Intelligence http://www.aaai.org(fairly general)
PC Artificial Intelligence magazine http://www.pcai.com/pcai(just right for OMIS 661, in my opinion)
The AI Laboratory at MIT: http://www.ai.mit.edu
Some interesting AI Web Destinations
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An Overview of
Neural 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)
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Inputdata
Dendriteinput wire
Neuron #1
Axon(output wire)
WeightW1,2
Dendrite
Neuron #2
Axon
Synapse(control of flow ofelectrochemical fluids
Neuron #3
Datasignals
FIGURE 17.3 Three Interconnected Artificial Neurons
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Learning
Three Tasks (over-simplified)
1. Compute Outputs
2. Compare Outputs with Desired Targets
3. Adjust Weights and Repeat the Process
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• 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
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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
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Limitations of
Neural 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
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Some interesting Neural Web Destinations
Brainmaker http://www.calsci.com
Neural Works Professional II Plus Neuralware, Inc