Common Sense Common Sense andand

Artificial IntelligenceArtificial Intelligence

Pradyumna Kumar ReddyJayanth Tadinada

Prithvi Raj KanakamSatish Kumar Guguloth

Devashish Sethia

It is way past 2001: Where the hell is HAL?

The Goals of Artificial Intelligence

• The need to reconsider the goals of AI

• Mental Amplification

• Thanks to engineering, we can travel faster and farther than our muscles can take us, see things we can’t otherwise see, talk louder than our lungs can shout.

Expert Systems• Our system: Some diagnosis expert system like MYCIN

• The Patient: An old rusted car in the back yard

• Question & Answer session with the expert system

Are there spots on the body? YES Are there more spots on the trunk than anywhere else? YESWhat color are the spots? REDDISH BROWN

• Diagnosis: The car has measles

• Degree of confidence: HIGHExample taken from Google Techtalk by Doug Lenat, may 2006

• Our system: An intelligent car loan approval system

• Question & Answer session with the expert system

Date of Birth: 1989

Time spent at current job: 19 YEARS

• Result: Loan approved

Expert Systems (cont.)

Example taken from Google Techtalk by Doug Lenat, May 2006

Expert Systems (cont.)

• So why do the “expert” systems have this problem?

• Because they don’t have common sense

• The expert systems only know equations and variables.

Search

• Is the Eiffel tower taller than the Taj Mahal?

• Cannot combine knowledge it already has access to.

• Why can’t the search engine do the simple math and give us the answer

• Lack of common sense

Natural Language Processing

• The police watched demonstrators... …because they feared violence. …because they advocated violence.

• Mary and Sue are sisters.Mary and Sue are mothers.

•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.”

Example taken from Google Techtalk by Doug Lenat, may 2006

ASSUME OUR COMPUTER NOW HAS

Common Sense

Search

Query: “someone smiling”

Caption: “A mother helping her child take her first step”

When you are happy, you smile

You become happy when someone you love accomplishes a milestone

Taking one’s first step is a milestone

Parents love their children

Search

Query: “Government buildings damaged in terrorist events in Beirut between 1990 and 2001.”

Document: “1993 pipe bombing of France’s embassy in Lebanon”

Beirut is in Lebanon

Embassies are govt. buildings

1993 is in the 1990’s

If there was a pipe bombing, then it is mostly a terrorist attack and not an accident etc.

Example taken from Google Techtalk by Doug Lenat, may 2006

Natural Language Processing

• The police watched demonstrators... …because they feared violence. …because they advocated violence.

• Mary and Sue are sisters.Mary and Sue are mothers.

•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.”

Example taken from Google Techtalk by Doug Lenat, may 2006

So How do weimplement

Common Sense?

What is this “Knowledge”?

• Millions of facts, rules of thumb etc.

• Represented as sentences in some language.

• If the language is Logic, then computers can do deductive reasoning automatically.

• This representation of a set of concepts within a domain and the relationships between those concepts is called Ontology

• The sentences are expressed in formal logic notation.

• The words and the logic sentences about them are called Formal Ontology

Hierarchy in Ontology

Predicate Calculus RepresentationParents love their children

This can be represented as

(ForAll ?P (ForAll ?C (implies

(and (isa ?P Person) (child ?P ?C))(loves ?P ?C)))))

For all P, For all C, P is a person AND C is a child of P implies P loves C

Reasoning Using Logic

Examples:

Simple: (isa Socrates Man)(ForAll ?x (implies (isa ?x Man) (isa ?x Mortal)))

(isa Socrates Mortal) =>Yes

Harder: Using general and specific knowledgeCan a can can-can? => No

Cyc

• Cyc is an AI project that attempts to assemble a comprehensive ontology and knowledge of everyday common sense knowledge.

• Its goal is to enable AI applications to perform human like reasoning.

• The project was started by CYcorp, a Texas based company.

• All the aforementioned features were incorporated in Cyc.

Cyc

• Cyc has a huge knowledge base which it uses for reasoning.

• Contains

• 15,000 predicates• 300,000 concepts• 3,200,000 assertions

• All these predicates, concepts and assertions are arranged in numerous ontologies.

Cyc: FeaturesUncertain Results

• Query: “who had the motive for the assassination of Rafik Hariri?”

• Since the case is still an unsolved political mystery, there is no way we can ever get the answer.

• In cases like these Cyc returns the various view points, quoting the sources from which it built its inferences.

• For the above query, it gives two view points • “USA and Israel” as quoted from a editorial in Al Jazeera• “Syria” as quoted from a news report from CNN

Example taken from Google Techtalk by Doug Lenat, may 2006

Cyc: Features (cont.)

• It uses Google as the search engine in the background.

• It filters results according to the context of the query.

• For example, if we search for assassination of Rafik Hariri, then it omits results which have a time stamp before that of the assassination date.

Cyc: Features (cont.)

Qualitative Queries

Query: “Was Bill Clinton a good President of the United States?”

• In cases like these, Cyc returns the results in a pros and cons type and leave it to the user to make a conclusion.

Queries With No Answer

Query: “At this instance of time, Is Alice inhaling or Exhaling?”

• The Cyc system is intelligent enough to figure out queries which can never be answered correctly.

Example taken from Google Techtalk by Doug Lenat, may 2006

The Dream• The ultimate goal is to build enough common sense into the Cyc system such that it can understand Natural Language.

• Once it understands Natural Language, all the system has to do is crawl through all the online material and learn new common sense rules and evolve.

• This two step process of building common sense and using machine learning techniques to learn new things will make the Cyc system an infinite source of knowledge.

Drawbacks• There is no single Ontology that works in all cases.

• Although Cyc is able to simulate common sense it cannot distinguish between facts and fiction.

• In Natural Language Processing there is no way the Cyc system can figure out if a particular word is used in the normal sense or in the sarcastic sense.

• Adding knowledge is a very tedious process.

References

1. Marvin Minsky, Why People Think Computers Can’t, AI Magazine, vol. 3 no. 4, Fall 1982.

2. Douglas B Lenat, Keynote address: computers vs common sense, Proceedings of the 1991 ACM SIGMOD international conference on Management of data, April 1991.

3. Douglas B Lenat, R V Guha, Karen Pittman, Dexter Pratt and Mary Shepherd, Cyc: toward programs with common sense, Communications of the ACM, 1990.

4. Douglas B Lenat, George Miller and Toshio Yokoi, CYC, WordNet, and EDR: critiques and responses, Communications of the ACM, 1995.

5. Talk by Douglas Lenat, Google techtalks, May 2006