1. Artificial Intelligence, Expert Systems, and Neural Networks Group 10 Cameron Kinard Leaundre Zeno Heath Carley Megan Wiedmaier

2. Introduction Artificial Intelligence Expert Systems Neural Networks Business Use Real World Application

3. What is Artificial Intelligence? A branch of science dealing with behavior, learning, and adaptation in machines. Two Categories  Conventional  Computational The two most common types of AI are expert systems and neural networks.

4. Conventional Artificial Intelligence A method involving the use of structured formulas and statistical analysis. Methods include  Expert systems  Case based reasoning  Bayesian networks  Behavior based AI.

5. Computational Artificial Intelligence The method of analyzing existing information and recognizing patterns. Simply put, it has the ability to learn from existing information. Methods include  Neural networks  Evolutionary computation  Fuzzy systems.

6. What is an Expert System? A program structured by a set of rules and procedures that take the knowledge, supplied by an expert, and recommend a course of action in order to solve specific problems. They use reasoning to work through problems and offer recommendations that address these problems. They are ideal for diagnostic and prescriptive problems. They are usually built for specific applications called domains.

7. Expert System Use Field use  Accounting  Financial management  Production  Process control  Medication prescription  In many other domains

8. Expert Systems - Advantages Its gathering and use of expertise They can perform many functions that will benefit organizations  Reduction in training costs  Decrease human error  Providing consistent answers to repetitive tasks  Safeguard sensitive company information

9. Expert Systems - Disadvantages Its inability to solve problems for which it was not designed Its inability to use common sense and judgment to solve newly encountered problems

10. What is a Neural Network? Artificial intelligence systems that can be trained to recognize patterns and adapt to new concepts and knowledge. They are not bound by a set of rules designed for a specific application. They are able to imitate the human ability to process information without following a set of rules.

11. What are Neural Networks? They use interconnecting neurons to produce an output. A neural network uses its neurons collectively to execute its functions.  A neuron is the basic functioning element in a neural network that takes inputs and produces outputs. This allows the neural network to continue performing even if some of its neurons are not functioning

12. Neural Network Use They are useful for identification, classification, and forecasting when dealing with a large amount of information. They are used in speech and visual recognition. Field use  Engineering  Drilling  Meteorology  Medical  Insurance industries  Military.

13. Neural Networks - Advantages They can adjust to new information on their own. They are able to function without structured information. They are able to process large volumes of data.

14. Neural Networks - Disadvantage The neural networks have hidden layers. The fact that these layers are hidden prohibits users from adjusting the connections reducing control of the system.

15. Overall Business Use The systems increase completion rates and decrease error by reducing human interaction. These systems protect information and utilize knowledge more efficiently to make intelligent decisions. Companies can gain an edge over their competitors by implementing these systems.

16. Real World Applications Banks Hospitals Credit Card Companies Manufacturers Robotics  Medical Fields

17. Conclusion Artificial Intelligence Expert Systems Neural Networks Business Uses Real World Applications

18. Any questions?