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 George Burns: “My aunt is in the

hospital. I went to see her today,

and took her flowers.”



 Gracie Allen: “George, that’s terrible!

You should have brought her

flowers!”



Steve O’Hara, Cyc, 9/26/07

CYC:

Toward Programs with

Common Sense



CACM Aug 1990







Steve O’Hara

UTSA CS 7123





Steve O’Hara, Cyc, 9/26/07

Motivation

Software is “brittle”

Examples:

 A skin disease diagnosis program that is told

about a rusty old car and decides it has

measles

 A car loan authorization system approves a

loan from somebody whose years at the

same job exceeds their age

 A digitalis dosage system that does not

notice when the patients age (49) and

weight (102) are switched





Steve O’Hara, Cyc, 9/26/07

Words are Deceptive

 MicrowaveOven isa KitchenAppliance

 Dishwasher isa KitchenAppliance

 Ahdfdiwhefh isa KitchenAppliance



 MicrowaveOven requires Electricity

 Dishwasher requires Electricity and Water



 Water is a Liquid



Steve O’Hara, Cyc, 9/26/07

Artificial Intelligence

To achieve an AI requires:

 Language / Logic



• How to express knowledge

 Manipulating Knowledge

• Rules of many flavors

 The Knowledge Base

• Nobody is really working on this

• Not Cyc, not google, not wikipedia



Steve O’Hara, Cyc, 9/26/07

Expert System Rules

IF frog(x)

THEN amphibian(x)

IF amphibian(x)

THEN laysEggsInWater(x)

IF laysEggsInWater(x)

THEN livesNearLotsOf(x, Water)

If livesNearLotsOf(x, Water)

THEN NOT livesInDesert(x)



Looks Intelligent, Right?



Steve O’Hara, Cyc, 9/26/07

However …

 Does Freda lay eggs?

 Is Freda sometimes in water?



 Is Freda a person?



 Is Freda larger or smaller than the



Pacific Ocean?

 Does Freda live on the Sun?









Steve O’Hara, Cyc, 9/26/07

Overcoming Brittleness

 Cyc Project started in 1984

 Led by Dr Douglas Lenat







 Funded by Bill, Steve, you and me



 Now Cycorp, www.cyc.com



 Only “true” AI project left



Steve O’Hara, Cyc, 9/26/07

Hmmm ….

 Bogosity: The Lenat



 The unit of bogosity, derived from the fictional

field of Quantum Bogodynamics, the lenat is

seldom used, as it is understood that it is too

large for normal conversation. Its most common

form is the microlenat. [The Original Hacker's

Dictionary. Retrieved on 2006-11-08]



 http://en.wikipedia.org/wiki/List_of_humorous_u

nits_of_measurement#Bogosity:_The_Lenat



Steve O’Hara, Cyc, 9/26/07

The Cyc Project



 Representation Language

 Inference Engine



 Ontology of the KB



 The KB







 Applications





Steve O’Hara, Cyc, 9/26/07

CycL - Representation

 Clean and simple semantics

 Yet speedy inference







 Epistemological level

• First order predicate calculus (good)

 Heuristic level

• Special purpose logic (custom)





Steve O’Hara, Cyc, 9/26/07

CycL Example

 transfersThrough(owns, physicalParts)

 owns(Guha, Toyota0093)

 physicalParts(Toyota0003, WheelRR0093)







Can infer owns(Guha, WheelRR0093)

without any special logic





Steve O’Hara, Cyc, 9/26/07

Cyc Ontology

 Thing

• InternalMachineThing

 The number 5

 The string “foo”



• RepresentedThing

 table

 TheWhiteHouse



 TheFourthOfJuly1990









Steve O’Hara, Cyc, 9/26/07

High Level Classes

 IndividualObject –vs– Set

 Substance –vs– Individual



 Process –vs– Event







 Walking –vs– WalkingToTheMailbox









Steve O’Hara, Cyc, 9/26/07

What Cyc Knows -- Buying

 Payments made through the mail are not

normally done in cash

 Candy costs double at a movie theater

 Agents need to own items they consume

 If you buy something, a sub-event

involves paying the seller

 Agents own items that are of some use to

them

 Etc.



Steve O’Hara, Cyc, 9/26/07

Machine Learning

 Roams the KB (at night) looking for

unexpected symmetries

 Surprisingly, the size of the KB has



fluctuated over the years as patterns

have emerged and more general

rules have replaced groups of

specialized rules





Steve O’Hara, Cyc, 9/26/07

Applications (in 1990)

 Large Engineering Knowledge bases

 Axiomatizing Human Emotions



 Machine Learning by Analogy



 Qualitative Physics Reasoning



 Natural Language Understanding









Steve O’Hara, Cyc, 9/26/07

Applications (now)

 From www.cyc.com

 Intelligent Search



 Excel spreadsheet logic



 Machine Translation



 Text understanding



 Picture Categorization









Steve O’Hara, Cyc, 9/26/07

Language Understanding

 The pen is in the box.

 The box is in the pen.



 The police watched the demonstrators …

• because they feared violence.

• because they advocated violence.





 Mary and Sue are sisters.

 Mary and Sue are mothers.

Steve O’Hara, Cyc, 9/26/07

Conclusion

 One of the most ambitious software

projects ever undertaken

 Best Marketing ever



 Love it or hate it



 Fantastic potential









Steve O’Hara, Cyc, 9/26/07


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