Artificial Intelligence (AI) includes the methodologies for modelling and simulating several human abilities that are widely accepted as representing intelligence. Perceiving, representing, learning, planning, and reasoning with knowledge and evidence are key themes.
Concepts and methods developed for building AI systems are useful in Data Science. For example, knowledge graphs such as semantic ontologies are both used and generated by data scientists. Computer vision algorithms can be used in analysis of image data; speech and natural language processing algorithms can be applied in analysis of speech or text data. Machine learning algorithms are applied extensively to extract patterns from data. Thus, a student who is well versed in AI will be able to apply those techniques in a Data Science context.
Conversely, Data Science methods are applied extensively in AI systems. Data Science students should have an understanding of AI systems and the way they work, if they plan to apply their work to AI.
Due to their centrality in Data Science, AI competencies related to images, text, and machine learning are highlighted elsewhere. Working with images and text is in the Data Acquisition, Management and Governance KA; Machine Learning is its own KA but is also referenced extensively in the Data Mining KA. This knowledge area addresses knowledge representation, reasoning, and planning.
Scope
- Major subfields of AI
- Representation and reasoning
- Planning and problem solving
- Ethical considerations
Competencies
- Describe major areas of AI as well as contexts in which AI methods may be applied.
- Represent information in a logic formalism and apply relevant reasoning methods.
- Represent information in a probabilistic formalism and apply relevant reasoning methods.
- Be aware of the wide range of ethical considerations around AI systems, as well as mechanisms to mitigate problems.
Subdomains
- AI-General – Tier 1, Tier 2
- AI-Knowledge Representation and Reasoning (Logic-based models) – Tier 2, Elective
- AI-Knowledge Representation and Reasoning (Probability-based models) – Tier 1, Tier 2, Elective
- AI-Planning and Search Strategies – Tier2, Elective
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