Artificial Intelligence 1... · goals communi- cative goals received COs + positionings COs to be...

Introduction & History

Artificial Intelligence

Lecture 1 Karim Bouzoubaa

Karim Bouzoubaa Artificial Intelligence 2

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


Cat Exercise


Lexicon

¢ Artificial Intelligence (AI)

¢ Science fiction

¢ AI : Computer Science Branch


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.


Is the computer intelligent?

¢ Computer mainly performs instructions ¢ Computer is “ stupid “

¢ However, the computer cannot l Decides, makes research, designs (by its

own) ¢ Computer

l  Is not creative l Cannot have new ideas, etc.

¢ A computer is not “ intelligent ” ¢ Need: build computers with intelligence


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)


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

of 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


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 of the art)


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


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


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


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, reason ing , language understanding, learning

Human Interrogator

Human AI system


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 opposed to solving the grand AI problem.


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 …


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.


State of the art – Equational prover


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


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 schedul ing program that involved up to 50,000 vehicles, cargo, and people


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


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 of 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


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


Application domains

¢ Games, Theorem prover, Problem resolution ¢ Medical science ¢ Transport ¢ Management ¢ Army ¢ Chemical science ¢  etc.


General and

Specific Knowledge

Learn Reason

Planning

Perception Communication Acts

Communication Acts Actions

Environment

Actions

Other entities

General AI Model


Perceive

Reason about the

task

Interpret communicative

acts

Plan the interactions

Plan actions

Choose conversa-

tional objects

Perform actions

Take the turn and perform

communicative acts

perceivedbeliefs

communicative act

communicative act

Model of the task

Model of social structure

communication plans

Agent's mental states Domain

knowledge

Knowledge about other agents

actions to be performed

goals

communicative goals

received COs + positioningsCOs to be

transmitted

External world

actions percepts

Figure 1: A simplified agent's model

Other agents

1

89

10

7

6

5

43

2

action plan schemas

interaction plan schemas

Legend cognitive process

components of conversational context

agent's environment

Detailed AI Model


Future

¢ The big Question:

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


Robotics


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


Robotics

n NASA Robots Spirit & Opportunity on Mars planet


Robotics

n Robots P3 of Honda


Future ¢  Usual objects (appliances, tools, wears, glasses, etc.) will be augmented with sensors, microprocessors , and cor respond ing embedded systems.

l  Mobile or not l  Communicating (Wifi, BlueTooth) l  (Semi) autonomous l  Advances UI (speech, gestures, etc.)


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


Tools ¢ Programming languages

l Lisp, Prolog

¢ Expert system shells

¢ NLP tools

¢ Agent and Multi-Agent Platforms

¢ Machine Learning, deep learning, ...

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