Given the utility of AI approaches for knowledge representation and inference, a data scientist should be aware of their range and history. A data scientist should develop a good sense of existing work in order to know where to look for possible solutions to the full range of possible problems one might encounter.
Knowledge
Tier 1:
- History of AI
- Reality of AI (what it is, what it does) versus perception
- Major subfields of AI: knowledge representation, logical and probabilistic reasoning, planning, perception, natural language processing, learning, robotics (both physical and virtual)
Skills
Tier 1:
- Explain how the origins of AI have led to the current status of AI
- Describe major branches of AI in order to recognize useful concepts and methods when needed in Data Science
Tier 2:
- State what AI systems are and that they both collect and use data to implement AI as well as collect and generate data that can be used by data scientists.
- Describe qualitatively how robots (physical or virtual), agents, and multi-agent systems collect and use data to embed, deliver, or implement artificial intelligence.
- Describe data collected and produced by AI systems that can be useful for data science applications.
Dispositions
Tier 1:
- Astute to, and respectful of, the fact that AI is not a new field, but rather one with a long and rich history.
Suggestions Accepted for consideration for the next Edition:
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