1. The Road to Intelligence Is Paved with a Million Million Expert Systems Christine Alvarado 6 November 2002

2. A Dangerous Idea? You need knowledge to be intelligent

3. OK, So Why Is this Dangerous? What is intelligence? Many ideas: passing the Turing Test, displaying common sense, etc. Perception What is knowledge? Specific facts about the world e.g. Humans have two legs, breakfast is served in the morning, etc.

4. A More Dangerous Idea We should explicitly represent specific knowledge within perceptual computer systems Questions I will address: What does this mean? Why should we do this? How can we do this?

5. Data-Driven vs. Concept-Driven Perception Conceptual Information Sensory Input

6. The Importance of Context

10. Another Example

11. 2) 1 + 3 = 4 1) 1 + 2 = 2 3) 5 + 6 = 10

12. Another Example

13. Where’s the Knowledge? Conceptual Knowledge Cows, sheep and pigs are all barnyard animals Structural Knowledge Cows’ eyes are above their noses Cows have spots Cows have long noses Not included in most recognizers Only implicitly included in most recognizers We should represent this knowledge explicitly!

14. Feature-Based Models Feature Extraction Structural Knowledge hidden within network Which part of the network represents “cows have legs” Conceptual Knowledge absent

16. Why Make Knowledge Explicit? To include conceptual knowledge in recognition To understand why the computer system is making a mistake To allow humans to construct recognizers

17. Including Conceptual Knowledge Feature-based approach cannot easily handle conceptual knowledge A picture of a cow encodes structural knowledge But how do you incorporate “cows and sheep are barnyard animals”?

18. Contextual Knowledge is Essential Contextual knowledge determines the interpretation for this shape:

19. Once We Have Concepts, We Can Generalize Face eyes nose mouth Eyes Nose Mouth

20. More Human Understanding The system can explain the reasons for its beliefs I think the shape is an arrow even though it only sort of looks like one because this is a finite state machine.

21. Should We Get Rid of Feature- Based Recognizers? Of course not! All these concepts have to bottom out somewhere Feature-based recognizers very useful for low level recognition We should integrate information from feature-based recognition with explicitly represented knowledge

22. OK, This Sounds Good, But How? Dynamically Constructed Object Oriented Bayesian Networks Create a fragment of a Bayesian Network for each specific piece of knowledge Dynamically link them together as input arrives

23. Representing Knowledge (Define Shape And-Gate (components (Line L1) (Line L2) (Line L3) (Semi-Circle S)) (constraints (parallel L1 L2) (same-horiz-pos L1 L2) (same-length L1 L2) (connected S.p1 L3.p1) (connected S.p2 L3.p2) (meets L1.p2 L3) (meets L2.p2 L3))) L1: L2: L3: S: C1: C2: C3: C4: C5: C6: C7: And-Gate L1L2L3SC1C2C7 … DescriptionNetwork Fragment

24. Hierarchical Representation To encode: “Often, an inverter precedes an and-gate” (Define Shape-Composition Inv-Before-And (components (And-Gate ag) (Inverter inv)) (constraints (connects inv ag) (precedes inv ag))) AG: INV: Inv-Before-And AGINVC1C2 And-Gate Fragment C1: C2:

25. Knowledge Acquisition Start small: specific domains Sketches are a good place to begin Simpler than vision 2D with a temporal component We can put some of the knowledge in ourselves

26. Conclusion Explicit representation of knowledge… …both structural and conceptual knowledge… …is a powerful way to build intelligent systems.