Chapter 11 - Artificial Intelligence
General Outcomes
- Artificial Intelligence challenges include obstacles such as processing images and language, or building autonomous agents and robots.
- Differentiate between the concepts of machine reasoning/behavior and human reasoning/behavior.
- Identify common vocabulary concerning artificial intelligence.
- Identify challenges with artificial intelligence concerning images and language processing.
- Security and ethical concerns exist concerning artificial intelligence.
- Discuss ethical and security concerns relating to artificial intelligence.
Reading Guide
In order to guide your reading we have designated each section into one of three categories:
- Essential - You should thoroughly read this material and be prepared to answer questions about this material in both discussions and on assessments.
- Important - You should read this material and be prepared to participate in potential discussions/activities about this material. While this material will not be directly assessed, your understanding of this may help you support/improve your answers to "Essential" material.
- Useful - We feel that this material is worth knowing and may supplement other material in the chapter. However, we do not plan on assessing or discussing (unless you ask questions).
| Section | Essential | Important | Useful |
|---|---|---|---|
| 11.1 Intelligence and Machines |  |
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| 11.2 Perception | |||
| Understanding Images | |
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| Language Processing | |
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| Strong AI vs. Weak AI | |
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| 11.3 Reasoning | |
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| 11.4 Additional Areas of Research | |||
| Representing and Manipulating Knowledge | |
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| Learning | |
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| Genetic Algorithms | |
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| 11.5 Artificial Neural Networks | |
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| 11.6 Robotics | |
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| 11.7 Considering the Consequences | |
Video Resources
The following videos are used in individual lessons during the study of this chapter. Their links are consolidated here for easy reference.- Crash Course Computing #: Machine Learning & Artificial Intelligence (combines aspects of Ch9 and Ch11)
- Crash Course Computing #: Computer Vision
- Crash Course Computing #: Natural Language Processing
- Crash Course Computing #: Robots
- Crash Course Computing #: The Singularity, Skynet, and the Future of Computing
- Neural Network Examples (From the Crash Course in Statistics)
- Neural Networks Explained (Goes in to the math/structure of the network in more detail)
UNI produced videos
- Introduction to Search Problems (Production Systems) (T11b)
- BFS Analysts and Hackers (T11b)
- BFS Travelling in Romania (T11b)
- DFS Analysts and Hackers (T11b)
- DFS Travelling in Romania (T11b)
- Summarizing BFS and DFS (T11b)
- Using Heuristics (T11b)
- Best-First/Best-Fit Search in Romania (T11b)
- Dr. Schafer explains Hill Climbing (T11d)
- Dr. Schafer demos Hill Climbing in action (T11d)
- Dr. Schafer explains Genetic Algorithms (T11d)
Study Guide
The competency demo for this chapter will consist of several questions from the following study guide. As you study this chapter we suggest you work through the materials in this study guide and ask questions if you need clarifications.