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.

MACHINE INTELLIGENCE

Will computers become as smart as humans within the next 50

years?

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?

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

Raw power

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)

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)

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

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

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

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

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)

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

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)

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

Learning

Three Tasks (over-simplified)

1. Compute Outputs

2. Compare Outputs with Desired Targets

3. Adjust Weights and Repeat the Process

• 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

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

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

Some interesting Neural Web Destinations

Brainmaker   http://www.calsci.com

Neural Works Professional II Plus   Neuralware, Inc