Competences Covered In the Course

 

1.  (Recognizing AI)  Distinguish between technological artifacts that use and do not use AI. 

2. (Understanding Intelligence) Critically analyze and discuss features that make an entity “intelligent”, including discussing differences between human, animal, and machine intelligence. 

3. (Interdisciplinarity) Recognize that there are many ways to think about and develop “intelligent” machines. Identify a variety of technologies that use AI, including technology spanning cognitive systems, robotics, and ML. 

4. (General vs. Narrow) Distinguish between general and narrow AI. 

5. (AI’s Strengths & Weaknesses) Identify problem types that AI excels at and problems that are more challenging for AI. Use this information to determine when it is appropriate to use AI and when to leverage human skills. 

6. (Imagine Future AI) Imagine possible future applications of AI and consider the effects of such applications on the world. 

7. (Representations) Understand what a knowledge representation is and describe some examples of knowledge representations. 

8. (Decision-Making) Recognize and describe examples of how computers reason and make decisions.

9. (ML Steps) Understand the steps involved in machine learning and the practices and challenges that each step entails. 

10. (Human Role in AI) Recognize that humans play an important role in programming, choosing models, and fine-tuning AI systems. 

11. (Data Literacy) Understand basic data literacy concepts 

12. (Learning from Data) Recognize that computers often learn from data (including one’s own data). 

13. (Critically Interpreting Data) Understand that data cannot be taken at face-value and requires interpretation. Describe how the training examples provided in an initial dataset can affect the results of an algorithm. 

14. (Action & Reaction) Understand that some AI systems have the ability to physically act on the world. This action can be directed by higher-level reasoning (e.g. walking along a planned path) or it can be reactive (e.g. jumping backwards to avoid a sensed obstacle). 

15. (Sensors) Understand what sensors are, recognize that computers perceive the world using sensors, and identify sensors on a variety of devices. Recognize that different sensors support different types of representation and reasoning about the world. 

16. (Ethics) Identify and describe different perspectives on the key ethical issues surrounding AI (i.e. privacy, employment, misinformation, the singularity, ethical decision making, diversity, bias, transparency, accountability). 

17. (Programmability) Understand that agents are programmable.