AI-Introduction

AI-Introduction: Fundamental Issues

CS Core:

1. Overview of AI problems, Examples of successful recent AI applications
2. Definitions of agents with examples (e.g., reactive, deliberative)
3. What is intelligent behavior?
   1. The Turing test and its flaws
   2. Multimodal input and output
   3. Simulation of intelligent behavior
   4. Rational versus non-rational reasoning
4. Problem characteristics
   1. Fully versus partially observable
   2. Single versus multi-agent
   3. Deterministic versus stochastic
   4. Static versus dynamic
   5. Discrete versus continuous
5. Nature of agents
   1. Autonomous, semi-autonomous, mixed-initiative autonomy
   2. Reflexive, goal-based, and utility-based
   3. Decision making under uncertainty and with incomplete information
   4. The importance of perception and environmental interactions
   5. Learning-based agents
   6. Embodied agents
   7.         sensors, dynamics, effectors
6. Overview of AI Applications, growth, and impact (economic, societal, ethics)

KA Core:

7. Practice identifying problem characteristics in example environments
8. Additional depth on nature of agents with examples
9. Additional depth on AI Applications, Growth, and Impact (economic, societal, ethics, security)

Non-core:

10. Philosophical issues
11. History of AI

Illustrative Learning Outcomes:

1. Describe the Turing test and the “Chinese Room” thought experiment.
2. Differentiate between optimal reasoning/behavior and human-like reasoning/behavior.
3. Differentiate the terms: AI, machine learning, and deep learning.
4. Enumerate the characteristics of a specific problem.

Suggestions Accepted for consideration for the next Edition:

1. The role of GenAI in Society

Please provide your suggestions about this knowledge unit. All submitted comments will be reviewed at the end of the month. Comments accepted for inclusion will be listed above.