Core AI is supposed to be the most basic most fundamental areas of AI. This is not quite the same as the most important areas of AI. It probably is what should be in any good AI textbook. Certainly I would include in core AI:
1. search
2. learning
3. memory
as well as something many other researchers would omit:
4. values/utility/fitness
Most people would also throw in:
5. logic(s)
6. representation
I think a good case can be made for adding:
7. pattern recognition
8. complexity
9. heuristics
10. compression
11. statistics
Depending on how broad you want your coverage to be one would also consider (in no special order):
12. natural language
13. neural networks
14. planning
15. consciousness
16. theorem proving
17. parallel computing
18. feature extraction
19. classifiers
20 modularity
21. architectures
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