Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Unit 1: Introduction to Artificial Intelligence
Miguel A. Gutiérrez Naranjo
Departamento de Ciencias de la Computación e Inteligencia ArtificialUniversidad de Sevilla
Inteligencia Artificial
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Índice
1 What is Artificial Intelligence?Preliminary notionsLooking for a definitionAn example: Learning
2 Some History notesOriginEarly yearsWeak methodsDiversification
3 Artificial Intelligence today
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Preliminary notions I
Artificial Intelligence (2001) 2001 A Space Odyssey (1968)Steven Spielberg Arthur C. Clarke
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Preliminary notions II
Star Wars (1977) Knight Rider (1982)George Lucas Glen A. Larson
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Merriam-Webster Dictionary
Intelligence
1 the ability to learn or understand or to deal with new ortrying situations
2 the ability to apply knowledge to manipulate one’senvironment or to think abstractly as measured byobjective criteria (as tests)
3 the ability to perform computer functions
4 . . .
Artificial
1 humanly contrived often on a natural model :man-made
2 lacking in natural or spontaneous quality
3 . . .
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Merriam-Webster Dictionary
Intelligence
1 the ability to learn or understand or to deal with new ortrying situations
2 the ability to apply knowledge to manipulate one’senvironment or to think abstractly as measured byobjective criteria (as tests)
3 the ability to perform computer functions
4 . . .
Artificial
1 humanly contrived often on a natural model :man-made
2 lacking in natural or spontaneous quality
3 . . .
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial
CoffeeA beverage made by percolation, infusion, or decoction fromthe roasted and ground seeds of a coffee plant. Is coffeenatural?
StoneA stone used to break open coconuts. Is it a natural or anartificial tool? What if the stone is used by a scavenger birdto break eggs?
DNA computerEhud Shapiro presented in 2004 a microscopic computer(molecular dimension) built out of synthetic DNA andenzimes, and proved to be able to effectively detectchemical signals which precede certain types of cancer(Nature, 2004) Is this natural or artificial?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial
CoffeeA beverage made by percolation, infusion, or decoction fromthe roasted and ground seeds of a coffee plant. Is coffeenatural?
StoneA stone used to break open coconuts. Is it a natural or anartificial tool? What if the stone is used by a scavenger birdto break eggs?
DNA computerEhud Shapiro presented in 2004 a microscopic computer(molecular dimension) built out of synthetic DNA andenzimes, and proved to be able to effectively detectchemical signals which precede certain types of cancer(Nature, 2004) Is this natural or artificial?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial
CoffeeA beverage made by percolation, infusion, or decoction fromthe roasted and ground seeds of a coffee plant. Is coffeenatural?
StoneA stone used to break open coconuts. Is it a natural or anartificial tool? What if the stone is used by a scavenger birdto break eggs?
DNA computerEhud Shapiro presented in 2004 a microscopic computer(molecular dimension) built out of synthetic DNA andenzimes, and proved to be able to effectively detectchemical signals which precede certain types of cancer(Nature, 2004) Is this natural or artificial?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Intelligence
Our nephew
• We teach our nephew to play chess. After some time,on a new game, he is able to defeat us.
• We claim his intelligence made him win.
Our computer
• Our computer, on a new game, is able to defeat us.
• Is it because of his intelligence?
Intelligent machinesWhat should they do so that we can say they are intelligent?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Intelligence
Our nephew
• We teach our nephew to play chess. After some time,on a new game, he is able to defeat us.
• We claim his intelligence made him win.
Our computer
• Our computer, on a new game, is able to defeat us.
• Is it because of his intelligence?
Intelligent machinesWhat should they do so that we can say they are intelligent?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Intelligence
Our nephew
• We teach our nephew to play chess. After some time,on a new game, he is able to defeat us.
• We claim his intelligence made him win.
Our computer
• Our computer, on a new game, is able to defeat us.
• Is it because of his intelligence?
Intelligent machinesWhat should they do so that we can say they are intelligent?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningARCHES - P. Winston 1975
Examples
≡
Positiveexamples
≡
Negativeexamples
Learning
?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningARCHES - P. Winston 1975
Examples
≡
Positiveexamples
≡
Negativeexamples
Learning
?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningKepler’s third law
Kepler’s third law (1618)
The square of the orbital periodof a planet (time needed to makea complete tour around the Sun)is directly proportional to the cu-be of the average distance to theSun.
BACONBACON automatic learning system(P. Langley, 1987) redescovered Kepler’s third law.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningKepler’s third law
Kepler’s third law (1618)
The square of the orbital periodof a planet (time needed to makea complete tour around the Sun)is directly proportional to the cu-be of the average distance to theSun.
BACONBACON automatic learning system(P. Langley, 1987) redescovered Kepler’s third law.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningProteins
Secondary structure of proteins
• The GOLEM system (Muggleton y Feng, 1992) wasused for predicting the secundary structure of proteins.
• Its accuracy over an independient test was 82 %, whilethe best conventional method got a 73 % accuracy.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
LearningProteins
Secondary structure of proteins
• The GOLEM system (Muggleton y Feng, 1992) wasused for predicting the secundary structure of proteins.
• Its accuracy over an independient test was 82 %, whilethe best conventional method got a 73 % accuracy.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Origin of aviation
• In 1903 the brothers Wilbur and Orville Wright becomethe first men to fly on a biplane with an engine; theirfirst short flight takes place on December 17th in USA,Kitty Hawk (North Carolina), and is considered as theorigin of the aviation. Prior to that only animals wereable to fly by using their wings.
• Do planes actually fly?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Origin of aviation
• In 1903 the brothers Wilbur and Orville Wright becomethe first men to fly on a biplane with an engine; theirfirst short flight takes place on December 17th in USA,Kitty Hawk (North Carolina), and is considered as theorigin of the aviation. Prior to that only animals wereable to fly by using their wings.
• Do planes actually fly?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Alan Turing
Alan M. Turing, (1950). Computing machinery and intelligence.Mind, 59, 433-460.
I propose to consider the question, Can machines think?
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Turing Test
AI SYSTEM
HUMAN
? HUMANINTERROGATOR
Turing test is not reproducible, constructive, and it cannot besubject of mathematical analysis.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Turing Test
• In 1990 the Loebner prize was created for the firstmachine able to pass the Turing Test.
• No machine has won the prize yet.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Turing Test
• In 1990 the Loebner prize was created for the firstmachine able to pass the Turing Test.
• No machine has won the prize yet.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Roots
• Philosophy: Logic, reasoning systems
• Mathematics: Formal representation, algorithms,decidability, tractability, provability, . . .
• Linguistics: Formal languages, study of grammars, . . .
• Psicology: Adaptation, perception,. . .
• . . .
• Associated to the technological development of thephisical support.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Roots
• Philosophy: Logic, reasoning systems
• Mathematics: Formal representation, algorithms,decidability, tractability, provability, . . .
• Linguistics: Formal languages, study of grammars, . . .
• Psicology: Adaptation, perception,. . .
• . . .
• Associated to the technological development of thephisical support.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Roots
• Philosophy: Logic, reasoning systems
• Mathematics: Formal representation, algorithms,decidability, tractability, provability, . . .
• Linguistics: Formal languages, study of grammars, . . .
• Psicology: Adaptation, perception,. . .
• . . .
• Associated to the technological development of thephisical support.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Roots
• Philosophy: Logic, reasoning systems
• Mathematics: Formal representation, algorithms,decidability, tractability, provability, . . .
• Linguistics: Formal languages, study of grammars, . . .
• Psicology: Adaptation, perception,. . .
• . . .
• Associated to the technological development of thephisical support.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Roots
• Philosophy: Logic, reasoning systems
• Mathematics: Formal representation, algorithms,decidability, tractability, provability, . . .
• Linguistics: Formal languages, study of grammars, . . .
• Psicology: Adaptation, perception,. . .
• . . .
• Associated to the technological development of thephisical support.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
McCulloch y PittsMcCulloch, W. S. and Pitts, W. H. (1943).A logical calculus of the ideas immanent in nervous activity.Bulletin of Mathematical Biophysics, 5:115-133.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Santiago Ramón y Cajal
Santiago Ramón y Cajal (1852 - 1934) Drawing of a neuron by Ramón y Cajal (1899)
Nobel Prize in Medicine in 1906
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Neuron
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
The gestation of AI
McCulloch and Pitts (1943)A logical calculus of the ideas immanent in nervous activity
Alan M. Turing (1950)Computing Machinery and Intelligence
Dartmouth AI Conference (1956)
John McCarthy Marvin MinskyClaude Shannon Ray SolomonoffAlan Newell Herbert SimonArthur Samuel Oliver SelfridgeNathaniel Rochester Trenchard More
John McCarthy proposes the term Artificial Intelligence
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
The gestation of AI
McCulloch and Pitts (1943)A logical calculus of the ideas immanent in nervous activity
Alan M. Turing (1950)Computing Machinery and Intelligence
Dartmouth AI Conference (1956)
John McCarthy Marvin MinskyClaude Shannon Ray SolomonoffAlan Newell Herbert SimonArthur Samuel Oliver SelfridgeNathaniel Rochester Trenchard More
John McCarthy proposes the term Artificial Intelligence
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
The gestation of AI
McCulloch and Pitts (1943)A logical calculus of the ideas immanent in nervous activity
Alan M. Turing (1950)Computing Machinery and Intelligence
Dartmouth AI Conference (1956)
John McCarthy Marvin MinskyClaude Shannon Ray SolomonoffAlan Newell Herbert SimonArthur Samuel Oliver SelfridgeNathaniel Rochester Trenchard More
John McCarthy proposes the term Artificial Intelligence
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Initial Enthusiasm (1952-1969)
Herbert Simon (1957)It is not my aim to surprise or shock you – but the simplest way Ican summarize is to say that there are now in the world machinesthat think, that learn and that create. Moreover, their ability to dothese things is going to increase rapidly until – in a visible future –the range of problems they can handle will be coextensive withthe range to which the human mind has been applied.
General Problem Solver (1959)Newell, A.; Shaw, J.C.; Simon, H.A. (1959). Report on ageneral problem-solving program. Proc. of the Int.Conference on Information Processing. pp. 256-264.
John McCarthyLook, Ma, no hands!
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Initial Enthusiasm (1952-1969)
Herbert Simon (1957)It is not my aim to surprise or shock you – but the simplest way Ican summarize is to say that there are now in the world machinesthat think, that learn and that create. Moreover, their ability to dothese things is going to increase rapidly until – in a visible future –the range of problems they can handle will be coextensive withthe range to which the human mind has been applied.
General Problem Solver (1959)Newell, A.; Shaw, J.C.; Simon, H.A. (1959). Report on ageneral problem-solving program. Proc. of the Int.Conference on Information Processing. pp. 256-264.
John McCarthyLook, Ma, no hands!
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Initial Enthusiasm (1952-1969)
Herbert Simon (1957)It is not my aim to surprise or shock you – but the simplest way Ican summarize is to say that there are now in the world machinesthat think, that learn and that create. Moreover, their ability to dothese things is going to increase rapidly until – in a visible future –the range of problems they can handle will be coextensive withthe range to which the human mind has been applied.
General Problem Solver (1959)Newell, A.; Shaw, J.C.; Simon, H.A. (1959). Report on ageneral problem-solving program. Proc. of the Int.Conference on Information Processing. pp. 256-264.
John McCarthyLook, Ma, no hands!
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
ELIZA. Weizenbaum (1965)
Weizenbaum, J. (1966). ELIZA A Computer Program for theStudy of Natural Language Communication between Manand Machine. Communications of the ACM 9(1), 36-45.
SessionI am the psychotherapist. Please, describe your problems (. . . ).- I am scared of flyingWhy do you say you are afraid of flying?- Because I have anxiety attacks when I need totravel by planeIs it because you have anxiety attacks when you need to travel byplane that you came to me?- Yes, I need help because the fear does not allow meto enjoy the journeyDoes it bother you that the fear does not allow you to enjoy thejourney?...- Bye
My secretary will send you a bill.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
ELIZA. Weizenbaum (1965)
Weizenbaum, J. (1966). ELIZA A Computer Program for theStudy of Natural Language Communication between Manand Machine. Communications of the ACM 9(1), 36-45.
SessionI am the psychotherapist. Please, describe your problems (. . . ).- I am scared of flyingWhy do you say you are afraid of flying?- Because I have anxiety attacks when I need totravel by planeIs it because you have anxiety attacks when you need to travel byplane that you came to me?- Yes, I need help because the fear does not allow meto enjoy the journeyDoes it bother you that the fear does not allow you to enjoy thejourney?...- Bye
My secretary will send you a bill.
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Teleology
Teleology (Merriam-Webster)a doctrine explaining phenomena by final causes
The chinese room (Searle, 1980)Searle J. (1980) Minds, Brains and Programs. TheBehavioral and Brain Sciences 3, 417-424.
Strong and weak AI
• Strong AI - Computers are intelligent
• Weak AI - Computers seem to be intelligent
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Teleology
Teleology (Merriam-Webster)a doctrine explaining phenomena by final causes
The chinese room (Searle, 1980)Searle J. (1980) Minds, Brains and Programs. TheBehavioral and Brain Sciences 3, 417-424.
Strong and weak AI
• Strong AI - Computers are intelligent
• Weak AI - Computers seem to be intelligent
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Teleology
Teleology (Merriam-Webster)a doctrine explaining phenomena by final causes
The chinese room (Searle, 1980)Searle J. (1980) Minds, Brains and Programs. TheBehavioral and Brain Sciences 3, 417-424.
Strong and weak AI
• Strong AI - Computers are intelligent
• Weak AI - Computers seem to be intelligent
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Weak methods (1969-1993)
• Knowledge based systems• Expert systems
• Dendral (Feigenbaum, 1975). Inference of molecularstructures.
• XCON (McDermott, 1978) Selection of components forthe VAX computer systems.
• Mycin (ShortLiffe, ∼1970) Diagnosis of infectious blooddiseases.
• CADUCEUS (Pople, ∼1970) Extension of Mycin.• . . .
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
KBS
Basic structure of KBS
KBS = Knowledge + Reasoning
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Expert Systems
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Diversification (1993-)
• Genetic algorithms
• Artificial life
• Learning
• Robotics
• Agent theory
• . . .
• Man-machine interaction
• Access to a huge amount of data
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Relationship with other sciences
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayProblems
• Knowledge representation
• Deduction, reasoning, and problem solving
• Planing
• Machine Learning
• Natural language processing
• Movement and manipulation
• Perception
• Social Intelligence
• Creativity
• Intelligence in general
• . . .
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Artificial Intelligence todayStuart Rusell. AIMA
• Jugar una partida de tenis de mesa
• Conducir por una carretera con curvas
• Conducir por una avenida con tráfico
• Hacer la compra por internet
• Comprar en un mercado de abastos
• Realizar una operación quirúrgica
• Inventar un chiste
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Bibliography I
S. Russell y P. Norvig.Inteligencia artificial: Un enfoque moderno.Segunda ediciónPrentice Hall, 2004.
D. Poole, A. Mackworth, R. Goebel.Computational Intelligence. A Logical ApproachOxford University Press 1998
P. Langley.Elements of Machine LearningMorgan Kaufmann 1996
Unit 1:Introduction to
ArtificialIntelligence
Miguel A.GutiérrezNaranjo
What isArtificialIntelligence?Preliminary notions
Looking for adefinition
An example:Learning
Some HistorynotesOrigin
Early years
Weak methods
Diversification
ArtificialIntelligencetoday
Bibliography II
Alan M. Turing (1950). Computing machinery andintelligence. Mind, 59, 433-460.
W.S. McCulloch y W.H. Pitts, (1943). A logical calculusof the ideas immanent in nervous activity. Bulletin ofMathematical Biophysics, 5:115-133.
J. Searle (1980) Minds, Brains and Programs. TheBehavioral and Brain Sciences 3, 417-424.
P.F. MartÃnez-Freire (1994) Inteligencia natural einteligencia artificial Actas del X Congreso deLenguajes Naturales y Lenguajes Formales. CarlosMartÃn-Vide (Ed.) PPU, 1994.