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Artificial Intelligence

1. Introduction

a. Artificial Intelligence (coined in 1956, John McCarthy)

i. apply to the use of coputers for studying and odeling

pro!le"sol#ing s$ills once thought only to used !y huans

ii. play gaes, proof theores, translated natural language,

learned fro their e%periences

!. &uring's 195 paper Coputing Machinery and Intelligence*

i. first suggest coputers !e used to siulate huan

intelligence

ii. Is there that uch siilarities !et+een the !rain and a

coputer

1. storage eory

-. a!ility to follo+ steps. input/output

0. process sensory perceptions

5. etc.

6.

. Can coputers thin$2

c. 3A4 9 3al*

i. s$ills and attri!utes that allo+ hi to confor to any def of

AI

-. Intelligent Autoataa. automaton(def.) used to descri!e anything that acts on its o+n.

(r. lit.self-moving)

i. re -th century siply iic$ed huans otions and

actions

1. clay figures

-. great cloc$s +ith o#ing figures

. &he &ur$ a chess playing achine 19thcent.

ii. -thcentury (electronics and circuits) thin$ing achine2

1. still only sall ad#ances ore of an electronic

iic$ingiii. 195 the coputer

1. Alan &uring Can achines thin$2*

a. a test7 the imitation game(Turing Test)

i. - huans and a coputer

ii. interrogator and responser (huan and

coputer)

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iii. coputer is said to ha#e superior

intelligence if the interrogator is fooled

i#. If the coputer acts intelligently then it

is intelligent

iv. Modern

1. no consensus as to +hat is AI

-. 8efinitions7

a. Mins$y AI is the science of a$ing

achines do things that +ould reuire

intelligence if done !y an.*

i. &uring it does a$e a difference ho+ a

achine is intelligent

ii. focus on algoriths and prograing

techniues

!. 3ayes the study of intelligence ascoputation*

i. :%tent to +hich a huans can !e

considered coputers*

1. they are interested in ho+ huans

sol#e the pro!les and use sensory

input

c. &essler +hate#er hasn't !een done yet*

i. ephasi;es the elusi#e natures of !oth

intelligence and coputation.

ii. as soon as it can !e coputeri;ed then

!oo* not really intelligent.

iii. &rue AI " o#ing target

. eople and Machines (

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c. =ur natural a!ility to deal +ith #ision and

natural language and etc +e are +ired to

adept at these tas$s*

d. =ne area of research is gi#ing coputer the

facilities to learn and gro+.

e. od oent (+e are created in the iage of

od) not e#olution oent

-. +al$ing, tal$ing, pattern recognition, sound

processing, spea$ing

!. &hin$ing 8eeply

i. analogy and etaphor

1. +e li#e our li#es !ased on etaphors this situation

or thought or ne+ thing is li$e >>>>>>>>>>>>>>>

-. a huan !eing, e#en a child, ?$no+s' #astly ore

than any coputer yet !uilt.*. :%aples of analogy7

a. &ie change and going to church* story

!. learning a ne+ gae

i.

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i. special languagesPROO!(also 4I@)

1. designed around logic

-. designed especially for AI

. saple progra "change([H,Q,D,N,P]) :-

member(H,[0,1,2]), /* Half-dollar */member(Q,[0,1,2,!,"]), /* #$ar%er */member(D,[0,1,2,!,",&,',,,,10]) , /* d */

member(N,[0,1,2,!,",&,',,,,10, /* n+*/ 11,12,1!,1",1&,1',1,1,1,20]),

+ &0*H 2&*Q 10*D &*N, . 100,P + 100-

. chess, chec$ers, !ac$gaon, =thello0. scheduling, edical diagnostic $no+ledge , etc.

d. &hin$ing a!out Coputers

i. dra+!ac$s7 lac$ of $no+ledge a!out huan intellect

ii. instead of trying to produce intelligent achine understand

intelligence

iii. use coputers to understand intelligence (e%periental

cognition)

iv. the coputers !ecoes the test !ed

1. you can't dissect the huan !rain

e. Comparison7 !rain and coputer

i. storage !rain (5 trillion) #s. coputer (1 trillion)

ii. cople%ity

1. parallel processing

a. !rain each neuron connected to 5, others

i. illions of processors each connected

to 1's

ii. transfer of data slo+er due to cheical

transfer 1 ft/s

!. coputeri. 1's of processors each connected to

1's

ii. transfer data illion ties faster

iii. cycle tie (s+itch to change) illion

%'s faster

i#. lue ene

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iii. speed

a. !rain

i. transfer of data slo+er due to cheical

transfer 1 ft/s

!. coputer

i. transfer data illion ties faster

ii. cycle tie (s+itch to change) illion

%'s faster

iv. conclusion

1. coputers are uch faster for doing siple,

repetiti#e, serial tas$s (crunching ra+ data)

-. huans are faster doing cople%, high le#el, and

parallel tas$s.

0. Artificial @$ills

a. Intro!. 4anguage rocessing

i. 4anguage distinguishes huans fro other species

1. fruitful insight into huan intellect

-. since 195s translating one language into another

a. solution to +orld+ide language pro!le

!. large !ilingual dictionaries

c. ore difficult then first thought

i. :nglish to Bussian

1. :nglish the spirit is +illing is

+illing, !ut the flesh is +ea$*

-. Bussian the #od$a is accepta!le,

!ut the eat has spoiled*

. @hifted to+ard language understanding

a. +or$ing $no+ledge of eleents of the language

graatical structure

i. contri!uted to the de#elopent of high"

le#el languages

!. incorporates

i. parsing1. different parts and graatical

correct

ii. sense of +ord !ased on surrounding

+ords

iii. an e%tensi#e and shared $no+ledge a!out

the real +orld transcends language

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i#. 8ifficult &ie flies li$e an arro+D fruit

flies li$e a !anana*

0. Chatter!ots " http7//+++.!otspot.co/search/s"

chat.ht

a. :4IEA using scripted dialogue

i. re"phrasing to get uestion and using

neutral responses

c. @peech Becognition

i. isolated +ord detectors

ii. @pecific conte%t speech recognition "

http7//shop.#oicerecognition.co/ites.asp2

CcF8BA=GHsourceFgoogle

iii. Many difficulties !oth technical and theoretical

d. no+ledge processing

i. :arly gae"playing and pro!le sol#ing1. state"space description

e. isual processing

i. iage enhanceent

ii. edge detection

iii. =CB (=ptical Character Becognition)

f. 4earning

i. neural net+or$s

ii. training

http://www.botspot.com/search/s-chat.htmhttp://www.botspot.com/search/s-chat.htmhttp://www.botspot.com/search/s-chat.htmhttp://www.botspot.com/search/s-chat.htm