IntroAI

8/6/2019 IntroAI

1/27

Introduction to

IntelligentComputing

8/6/2019 IntroAI

2/27

Text Book:

ArtificialIntelligenceA Modern Approach

Stuart Russell & PeterNorvig

8/6/2019 IntroAI

3/27

Intelligence

Apple Newton Gravity Force No one things like him, why should not

apple go towards sky which one is pullingtowards earth Earth rotates - force forms

gravity force. Twin tower attack - secret data Hidden

in image. Birbol without modification make it as

small line A long line drawn next to theexisting line. If a machine has brain how it will

encounter in new situation.

8/6/2019 IntroAI

4/27

Approaches to AI

Systems that act like humans The study of how to obtain that computers perform tasks

at which, at the moment, people are better (Rich andKnight, 1991)

Systems that think like humans The effort to make computers think... machines with

minds in the full and literal sense (Haugeland, 1985)

Systems that think rationally The study of the mental faculties through the study of

computational models (Charniak and McDermott, 1985)

Systems that act rationally The effort to explain and emulate the intelligent behavior

in terms of computational processes (Shalkoff, 1990)

8/6/2019 IntroAI

5/27

5

Definitions (Russell + Norvig)

Like humans Not necessarily like humans

Systems that think likehumans

Systems that think rationally

Systems that act likehumans

Systems that act rationally

Thi

nk

A

ct

8/6/2019 IntroAI

6/27

6

Human intelligence

One distinction between these definitions iswhether or not imitating humans is the goal.

One advantage of imitating humans is thathumans are an easily acknowledged exemplar of

intelligence.The Turing Test:

A human interrogator. Communicates with a hiddensubject that is either a computer system or a human. Ifthe human interrogator cannot reliably decide whetheron not the subject is a computer, the computer is saidto have passed the Turing test.

8/6/2019 IntroAI

7/27

7

Turing Test?

In 1950 Turing proposed anoperational definition ofintelligence by using a Testcomposed of :

An interrogator (aperson who will askquestions)

a computer (intelligent

machine !!) A person who willanswer to questions

A curtain (separator)

A. Turing

8/6/2019 IntroAI

8/27

8

What is ArtificialIntelligence?

The computer passes the test of intelligence if a human,after posing some written questions, cannot tell whetherthe responses were from a person or not.

8/6/2019 IntroAI

9/27

Turing test

8/6/2019 IntroAI

10/27

10

Human intelligenceTuring provided some very persuasive

arguments that a system passing theTuring test is intelligent.

However, the test does not provide

much traction on the question of howto actually build an intelligent system.

i.e., Turing test is not enough to see

how a machine acts;We also need to know what internalmental [rational] states it has.

8/6/2019 IntroAI

11/27

11

Human intelligence

In general there are various reasonswhy trying to mimic humans mightnot be the best approach to AI.

Computer Human BrainComputational Units 1 CPU, 108 gates 1011 neurons

Storage Units 1010 bits RAM

1011 bits disk

1011 neurons

1014 synapses

Cycle time 10-9 sec 10-3 secBandwidth 1010 bits/sec 1014 bits/sec

Memory updates/sec 109 1014

8/6/2019 IntroAI

12/27

Eliza (1966)Conversation with ELIZA,

E> Hello. I am ELIZA. How can I help you?P> I am feeling sad.E> I am sorry to hear you are sad.

P> I had some nightmares tonight.E> You say you had some nightmares tonight?P> Yes, my dreams are worrying me.E> What does that dream suggest to you?P> Thrill, scare.E> I am not sure I understand you fully.P> My dreams annoy me.E> Do you dream often?P> Yes, daily.

8/6/2019 IntroAI

13/27

The Chinese room (JohnSearle,1980)

8/6/2019 IntroAI

14/27

Does not necessarily generateUnderstanding

Human who understands English, equipped

with a rule book [written in English], variousstacks of paper, some blank, some withindecipherable[hard to read] inscriptions[Words].

Input to room human finds matchingsymbols in the rule book follows theinstruction.

From the outside of room system takes

input in chinese language and generateanswers in chinese language conventionallyintelligent turing

8/6/2019 IntroAI

15/27

Searle argue as follows,

The person in the room does not

understand Chinese(given), the rule book stacks of paper does not understandChinese.

No understanding of Chinese is going on

Hence, according to Searle, running theright program does not necessarilygenerate understanding.

8/6/2019 IntroAI

16/27

Lost in translationEnglish Russian (or Spanish) English

The spirit is willing, but the flesh is weak

,

The vodka is good, but the meat is rotten

(Actually: Spirit is willingly ready, but flesh is weak

orThe alcohol is arranged, but the meat is weak)

Translation requires general knowledge oflanguage remove ambiguity.

8/6/2019 IntroAI

17/27

Autonomous robots

8/6/2019 IntroAI

18/27

18

AI inherited many ideas, viewpoints and techniquesfrom other disciplines.AI inherited many ideas, viewpoints and techniquesfrom other disciplines.

AI Foundations?

AIAI

Psychology

Linguistic

CS

Philos

ophy

Mathematics

Theories ofreasoning andlearning

Theories of logicprobability, decisionmaking andcomputation

Make AI a

reality

The meaning andstructure oflanguage

To

investigatehumanmind

8/6/2019 IntroAI

19/27

19

What is Artificial Intelligence

To give an answer, the computer would need topossess some capabilities:

Natural language processing: To communicatesuccessfully.

Knowledge representation: To store what it knowsor hears.

Automated reasoning: to answer questions anddraw conclusions using stored information.

Machine learning: To adapt to new circumstances

and to detect and extrapolate patterns. Computer vision: To perceive objects.

Robotics to manipulate objects and move.

8/6/2019 IntroAI

20/27

20

Goals of AI

AI began as an attempt to understandthe nature of intelligence,but it has grown into a scientific andtechnological field affecting many aspectsof commerce and society.Solve real-world problems usingknowledge and reasoning.

Creating new opportunities inbusiness, engineering, and manyother application areas

8/6/2019 IntroAI

21/27

21

Examples of AI Applicationsystems

Game Playing

TDGammon, the worldchampion backgammonplayer, built by GerryTesauro of IBM research

Deep Blue chess program

beat world champion GaryKasparov

Chinook checkers program

8/6/2019 IntroAI

22/27

The decisive game of the match was Game2we saw something that went beyondout wildest [Natural] expectationsThemachine refused to move to a positionthat had a decisive[crucial] short-termadvantage - showing a very human senseof danger.

Garry Kasparov 1997

The decisive game.

8/6/2019 IntroAI

23/27

23

Artificial Intelligence History

Early AI: (The gestation of Artificial Intelligence)

1943 McCulloch & Pitts: Boolean circuit model of brain1950 Turing's ``Computing Machinery and Intelligence''1950s Early AI programs, including Samuel's checkers

program,Newell & Simon's Logic Theorist, Gelernter'sGeometry Engine

The birth of Artificial Intelligence (1956)

1956 McCarthy organizes Dartmouth meeting and includesMinsky, Shannon, Newell, Samuel, Simon

Name ``Artificial Intelligence'' adopted

8/6/2019 IntroAI

24/27

24


Early enthusiam, great expectations (1952-1969):

1957 General Problem Solver [Newell, Simon, Shaw @ CMU]1958 Creation of the MIT AI Lab by Minsky and McCarthy1958 LISP, [McCarthy], second high level language (MIT AI

Memo 1)1963 Creation of the Stanford AI Lab by McCarthy1965 Robinson's complete algorithm for logical reasoning

A dose of reality (1966-1973):

1966-74 AI discovers computational complexity

1966-72 Shakey, SRIs Mobile Robot [Fikes, Nilson]

8/6/2019 IntroAI

25/27

8/6/2019 IntroAI

26/27

26


The return of neural networks (1986 - present)

1988-93 Expert systems industry busts: ``AI Winter''1985-95 Neural networks return to popularity

AI becomes a science (1987 present)

1988- Resurgence of probabilistic and decision-theoretic methods

Computational learning theory

``Nouvelle AI'': ALife, GAs, soft computing, emergent

computing

Complex Systems or the Science of complexity

8/6/2019 IntroAI

27/27

Rationally wisely, logically

Omniscience knowing everything [om-ni-science]

Dichotomy between two things thedichotomy between peace and war [di-cho-tomy]

Interrogator inquiring

Exemplar - example Computational calculation

Wildest - natural

Mental rational - Internal workings as well as

external behavior Conscious aware - mindful

Syntactic - syntax

Documents

IntroAI