Artificial Intelligence, Expert Systems, and Neural Networks

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?