Introduction & History Artificial Intelligence Lecture 1 Karim - - PowerPoint PPT Presentation

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Introduction & History Artificial Intelligence Lecture 1 Karim - - PowerPoint PPT Presentation

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Introduction & History Artificial Intelligence Lecture 1 Karim Bouzoubaa Content What is AI? What is Intelligence? AI and other disciplines History The state of the art Application domains Model of

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Introduction & History

Artificial Intelligence Lecture 1 Karim Bouzoubaa

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Content

¢ What is AI? ¢ What is Intelligence? ¢ AI and other disciplines ¢ History ¢ The state of the art ¢ Application domains ¢ Model of an Intelligent system ¢ The future ¢ Tools
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Cat

Exercise

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Lexicon

¢ Artificial Intelligence (AI) ¢ Science fiction ¢ AI : Computer Science Branch
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Introduction

¢ Since 20’s – 30’s ¢ Large use of computer science ¢ Reason : Fast computing ¢ Rapidity ¢ Economic gains, computers l

don’t get tired

l

don’t sleep

l

don’t strike

l

etc.

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Is the computer intelligent?

¢ Computer mainly performs instructions ¢ Computer is “ stupid “ ¢ However, the computer cannot l Decides, makes research, designs (by its
  • wn)
¢ Computer l Is not creative l Cannot have new ideas, etc. ¢ A computer is not “ intelligent ” ¢ Need: build computers with intelligence
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First definitions of AI

¢ Difference between Man and Machine:
  • 1. Fast computing for machine, don’t get tired, don’t strike, etc.
  • 2. Man creativity, can make previsions, learns, invents, etc.
  • 3. Examples
  • ‘Car’ : difference in representing information
  • ‘Horse’ : default knowledge, preferences
  • ‘Pyramid’ : default reasoning, different types of reasoning
  • 4. Synthesis
  • Intelligence characterizes Man (up to now), difficult to tackle, to
understand
  • Bring Intelligence to machines in order to help Man in his(er) everyday
tasks ¢ Definitions of AI:
  • 1. Simulate Human Intelligence using Machines
  • 2. Understand Human Intelligence (Using computer science models)
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What is Intelligence?

¢ Larousse – Mon premier dictionnaire l L’intelligence est la qualité d’une personne qui comprend vite les choses, apprend facilement et s’adapte bien aux situations nouvelles l Contraire : stupidité ¢ Wikipedia l An intelligence quotient, or IQ, is a score derived from one
  • f several different standardized tests designed to assess
intelligence l Standardized tests can't measure initiative, creativity, imagination, conceptual thinking, curiosity, effort, irony, judgment, commitment, nuance, good will, ethical reflection, or a host of other valuable dispositions and
  • attributes. What they can measure and count are isolated
skills, specific facts and function, content knowledge, the least interesting and least significant aspects of learning
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What is Intelligence/AI?

¢ Intelligence is first of all a behavior l Human beings, Animals à AI attempts to simulate this behavior l Behavior = perception, understanding, prediction, manipulation, thinking, etc. ¢ How is it possible for a slow, tiny brain, whether biological or electronic, to perceive, understand, predict, manipulate and think? l What is the impact on CS and on our every day life? ¢ It is clear that computers with human level intelligence would have a huge impact on our every day lives and on the future course of civilization (§ State
  • f the art)
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Intelligence and other disciplines

¢ Other disciplines were interested in the study of the

intelligence

¢ The study of intelligence is also one of the oldest
  • disciplines. For over 2000 years, philosophers have tried to

understand how seeing, learning, remembering, and reasoning could, or should be done

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Intelligence and other disciplines

Linguistics we have theories of the structure and meaning of the language Intelligence Economics Utility, decision theory Philosophy theories of reasoning and learning have emerged, along with the viewpoint that the mind is constituted by the operation of a physical system Mathematics we have formal theories of logic, probability, decision making and computation Computer we have the tools with which to make AI a reality Neuroscience Physical substrate of mental activity Psychology we have the tools with which to investigate the human mind, and a scientific language within which to express the resulting theories
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History of AI

¢ 1943 McCulloch & Pitts: Boolean circuit model of brain ¢ 1950 Turing's "Computing Machinery and Intelligence“ ¢ 1956 Dartmouth meeting: "Artificial Intelligence" adopted ¢ 1952—69 Big hopes!
  • Newell and Simon: GPS (General Problem Solver)
  • McCarty: LISP
  • Minsky: Micro-Worlds
¢ 1966—73 AI discovers computational complexity Neural network research almost disappears The problem is not as easy as we thought ¢ 1969—79 Early development of knowledge-based systems Expert systems Ed Feigenbaum (Stanford): Knowledge is power!
  • Dendral (inferring molecular structure from a mass spectrometer).
  • MYCIN: diagnosis of blood infections
Robotic vision applications ¢ 1980-- AI becomes an industry ¢ 1986-- Neural networks return to popularity ¢ 1987-- AI becomes a science ¢ 1995-- The emergence of intelligent agents
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Turing Test

¢ Turing (1950) "Computing machinery and intelligence": ¢ "Can machines think?" à "Can machines behave intelligently?" ¢ Operational test for intelligent behavior: the Imitation Game ¢ Predicted that by 2000, a machine might have a 30% chance of fooling a lay person for 5 minutes ¢ Suggested major components of AI: knowledge, reasoning, language understanding, learning Human Interrogator Human AI system
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More recently

¢ AI turns more scientific, relies on more

mathematically sophisticated tools:

l Markov models (for speech recognition) l Belief networks (see Office 97) ¢ Focus turns to building useful artifacts as
  • pposed to solving the grand AI problem.
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State of the art

¢ Deep Blue defeated the reigning world chess champion Garry Kasparov in 1997 ¢ Proved a mathematical conjecture (Robbins conjecture) unsolved for decades ¢ No hands across America (driving autonomously 98% of the time from Pittsburgh to San Diego) ¢ During the 1991 Gulf War, US forces deployed an AI logistics planning and scheduling program that involved up to 50,000 vehicles, cargo, and people ¢ NASA's on-board autonomous planning program controlled the scheduling of operations for a spacecraft ¢ Proverb solves crossword puzzles better than most humans ¢ And many more …
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State of the art - Deep Blue

NEW YORK (CNN) -- He had never lost a chess match. But that all changed after 19 moves Sunday against the Deep Blue IBM computer.
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State of the art – Equational prover

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State of the art – ALVINN

¢ Autonomous Land Vehicle In a Neural Network ¢ No hands across America (driving autonomously 98% of the time from coast to coast) ¢ 5487 km
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State of the art – 1991 Gulf War

¢ US forces deployed an AI

l o g i s t i c s p l a n n i n g a n d scheduling program that involved up to 50,000 vehicles, cargo, and people

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State of the art – NASA

¢ NASA's on-board autonomous

planning program controlled the scheduling of operations for a spacecraft

¢ Mission Deep Space 1 (1998) l Agent-based system l Capable to autonomously make

decisions

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State of the art – Proverb

¢ Proverb (The Probabilistic Cruciverbalist) is a computerized crossword puzzle solver ¢ Proverb solves crossword puzzles better than most humans ¢ It builds on recent advances in computer science on efficient probabilistic reasoning, information retrieval, data mining, and constraint satisfaction to use a variety
  • f online databases to solve puzzles.
¢ An extensive series of tests indicates that Proverb fills in approximately 90% of the words correctly on an average New York Times crossword puzzle
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AI Fields

¢ K representation: neural nets, semantic nets,

etc.

¢ Reasoning: NLP, ≠ kinds of reasoning (case-

based, logic, deductive, ...)

¢ Planning (get the robot to find the telephone

in the other room)

¢ M a c h i n e L e a r n i n g ( a d a p t t o n e w

circumstances)

¢ Machine vision, speech recognition, finding

data on the web, robotics, and much more

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Application domains

¢ Games, Theorem prover, Problem resolution ¢ Medical science ¢ Transport ¢ Management ¢ Army ¢ Chemical science ¢ etc.
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SLIDE 25 Karim Bouzoubaa Artificial Intelligence 25 General and Specific Knowledge Learn Reason Planning Perception Communication Acts Communication Acts Actions Environment Actions Other entities

General AI Model

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SLIDE 26 Karim Bouzoubaa Artificial Intelligence 26 Perceive Reason about the task Interpret communicative acts Plan the interactions Plan actions Choose conversa- tional
  • bjects
Perform actions Take the turn and perform communicative acts perceived beliefs communicative act communicative act Model of the task Model of social structure communica- tion plans Agent's mental states Domain knowledge Knowledge about
  • ther agents
actions to be performed goals communi- cative goals received COs + positionings COs to be transmitted External world actions percepts Figure 1: A simplified agent's model Other agents 1 8 9 10 7 6 5 4 3 2 action plan schemas interaction plan schemas Legend cognitive process components of conversational context agent's environment

Detailed AI Model

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Future

¢ The big Question:

Will some day the machine be more intelligent than a human being?

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Robotics

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Robotics

By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. ¢ Major Applications l Search and

assistance in disaster cases

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Robotics

n NASA Robots

Spirit & Opportunity on Mars planet

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Robotics

n Robots P3

  • f Honda
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Future

¢ Usual objects (appliances, tools, wears, glasses, etc.) will be augmented with sensors, microprocessors, and corresponding embedded systems. l Mobile or not l Communicating (Wifi, BlueTooth) l (Semi) autonomous l Advances UI (speech, gestures, etc.)
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Limits / Future

¢ Theoretical limits l Learning l Approximate reasoning l Large amount of knowledge ¢ Difference Generalist/Specific approach (closed worlds) ¢ Structure l Mind: massive parallelism l Computer: sequential ¢ Law: computer/human society
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Tools

¢ Programming languages l Lisp, Prolog ¢ Expert system shells ¢ NLP tools ¢ Agent and Multi-Agent Platforms ¢ Machine Learning, deep learning, ...