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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.
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The Road to Intelligence Is Paved with a Million Million Expert Systems
Christine Alvarado6 November 2002
A Dangerous Idea?
You need knowledge to be intelligent
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 worlde.g. Humans have two legs, breakfast is
served in the morning, etc.
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?
Data-Driven vs. Concept-Driven Perception
Conceptual Information
Sensory Input
The Importance of Context
The Importance of Context
The Importance of Context
The Importance of Context
Another Example
Another Example
2) 1 + 3 = 4
1) 1 + 2 = 2
3) 5 + 6 = 10
Another Example
Where’s the Knowledge?
Conceptual KnowledgeCows, sheep and pigs are
all barnyard animalsStructural Knowledge
Cows’ eyes are above their noses
Cows have spotsCows have long noses
Not included in most recognizers
Only implicitly included in most recognizers
We should represent this knowledge explicitly!
Feature-Based Models
Feature Extraction
Structural Knowledge hidden within network Which part of the
network represents “cows have legs”
Conceptual Knowledge absent
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
Including Conceptual Knowledge
Feature-based approach cannot easily handle conceptual knowledgeA picture of a cow encodes
structural knowledgeBut how do you
incorporate “cows and sheep are barnyard animals”?
Contextual Knowledge is Essential
Contextual knowledge determines the interpretation for this shape:
Once We Have Concepts, We Can Generalize
Face
eyes
nose
mouth
Eyes
Nose
Mouth
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.
Should We Get Rid of Feature-Based Recognizers?
Of course not!All these concepts have to bottom out
somewhereFeature-based recognizers very useful for
low level recognitionWe should integrate information from
feature-based recognition with explicitly represented knowledge
OK, This Sounds Good, But How?
Dynamically Constructed Object Oriented Bayesian NetworksCreate a fragment of a Bayesian Network
for each specific piece of knowledgeDynamically link them together as input
arrives
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
L1 L2 L3 S C1 C2 C7…
Description Network Fragment
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
AG INV C1 C2
And-Gate FragmentC1:C2:
Knowledge Acquisition
Start small: specific domainsSketches are a good place to begin
Simpler than vision2D with a temporal component
We can put some of the knowledge in ourselves
Conclusion
Explicit representation of knowledge……both structural and conceptual
knowledge……is a powerful way to build intelligent
systems.