Artificial Intelligence: Difference between revisions
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Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"<ref>https://en.wikipedia.org/wiki/Artificial_intelligence</ref> | Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"<ref>https://en.wikipedia.org/wiki/Artificial_intelligence</ref> | ||
Revision as of 14:46, 31 May 2023
Artificial Intelligence[1]
Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, the field of AI research defines itself as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving"[2]
For now (January 2018), this page is a collection of my notes from my graduate class, which I will build upon and share with my students. The structure of this information is "note-taking, not ready for structured learning by students".
The big ideas in AI[edit]
- Natural Language Processing
- Problems in AI
- Characteristics of AI agents
- Three fundamentals of knowledge-based AI
- Four schools of AI
- Semantic relationships
- Means-Ends Analysis
- Problem Reduction
- Production System
- Frames
- Learning by recording cases
- Case-based reasoning
- Incremental concept learning
- Logic
- Planning
- Primitive action