CSC 423 ARTIFICIAL INTELLIGENCE

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CSC 423


ARTIFICIAL INTELLIGENCE


Introduction

Introduction
(cont)


College:


e
-
mail:
theo_christopher@hotmail.com


web address:
www.ctleuro.ac.cy



Personal:


web address:
www.theodorosch
ristophides
.
yolasite.com

Introduction
(cont)


Syllabus


Books


Library


Lab


Attendance & admittance to class


Exams and tests


Classroom behavior

4

Definition of Artificial Intelligence



Artificial

Intelligence,

or

AI

for

short,

is

a

combination

of

computer

science,

physiology,

and

philosophy
.

AI

is

a

broad

topic,

consisting

of

different

fields,

from

machine

vision

to

expert

systems
.

The

element

that

the

fields

of

AI

have

in

common

is

the

creation

of

machines

that

can

"think"
.

In

other

words

is

the

science

and

engineering

of

making

intelligent

machines,

especially

intelligent

computer

programs
.

It

is

related

to

the

similar

task

of

using

computers

to

understand

human

intelligence,

but

AI

does

not

have

to

confine

itself

to

methods

that

are

biologically

observable
.





5

An Introduction to Artificial
Intelligence (Cont)


To

what

degree

does

intelligence

consist

of,

for

example,

solving

complex

problems,

or

making

generalizations

and

relationships?

And

what

about

perception

and

comprehension
?



Research

into

the

areas

of

learning,

of

language,

and

of

sensory

perception

have

aided

scientists

in

building

intelligent

machines
.

One

of

the

most

challenging

approaches

facing

experts

is

building

systems

that

mimic

the

behavior

of

the

human

brain,

made

up

of

billions

of

neurons,

and

arguably

the

most

complex

matter

in

the

universe
.

Perhaps

the

best

way

to

gauge

the

intelligence

of

a

machine

is

British

computer

scientist

Alan

Turing’s

test
.

He

stated

that

a

computer

would

deserves

to

be

called

intelligent

if

it

could

deceive

a

human

into

believing

that

it

was

human
.

6

Turing Test


English

mathematician

Alan

Turing

proposed

in

1950

the

following

criterion

for

the

intelligence

of

a

machine
:

a

human

interrogator

cannot

differentiate

whether

s/he

is

communicating

with

another

human

or

a

computer

using

text

messages
.


An

example

of

a

test

of

acting

human
-
like


In

the

so
-
called

total

Turing

test

the

machine

also

has

to

be

able

to

observe

and

manipulate

its

physical

environment


Time
-
limited

Turing

test

competitions

are

organized

annually


The

best

performance

against

knowledgeable

organizers

is

recorded

by

programs

that

try

to

fool

the

interrogator


Human

experts

have

the

highest

probability

of

being

judged

as

non
-
humans

7

Importance of Artificial
Intelligence


Artificial

Intelligence

has

come

a

long

way

from

its

early

roots,

driven

by

dedicated

researchers
.

The

beginnings

of

AI

reach

back

before

electronics,

to

philosophers

and

mathematicians

such

as

Boole

and

others

theorizing

on

principles

that

were

used

as

the

foundation

of

AI

Logic
.

AI

really

began

to

intrigue

researchers

with

the

invention

of

the

computer

in

1943
.

The

technology

was

finally

available,

or

so

it

seemed,

to

simulate

intelligent

behavior
.

Over

the

next

four

decades,

despite

many

stumbling

blocks,

AI

has

grown

from

a

dozen

researchers,

to

thousands

of

engineers

and

specialists
;

and

from

programs

capable

of

playing

checkers,

to

systems

designed

to

diagnose

disease
.



AI

has

always

been

on

the

pioneering

end

of

computer

science
.

Advanced
-
level

computer

languages,

as

well

as

computer

interfaces

and

word
-
processors

owe

their

existence

to

the

research

into

artificial

intelligence
.

The

theory

and

insights

brought

about

by

AI

research

will

set

the

trend

in

the

future

of

computing
.

The

products

available

today

are

only

bits

and

pieces

of

what

are

soon

to

follow,

but

they

are

a

movement

towards

the

future

of

artificial

intelligence
.

The

advancements

in

the

quest

for

artificial

intelligence

have,

and

will

continue

to

affect

our

jobs,

our

education,

and

our

lives
.

8

The History of AI


One

can

consider

McCulloch

ja

Pitts

(
1943
)

to

be

the

first

AI

publication


It

demonstrates

how

a

network

of

simple

computation

units,

neurons
,

can

be

used

to

compute

the

logical

connectives

(
and
,

or
,

not
,

etc
.
)


It

is

shown

that

all

computable

functions

can

be

computed

using

a

neural

network


It

is

suggested

that

these

networks

may

be

able

to

learn


Hebb

(
1949
)

gives

a

simple

updating

rule

for

teaching

neural

networks


Turing

(
1950
)

introduces

his

test,

machine

learning,

genetic

algorithms,

and

reinforcement

learning


In

1956

John

McCarthy

organized

a

meeting

of

researchers

interested

in

the

field,

the

name

AI

was

invented

9

The History of AI (Cont)


From

the

very

beginning

central

universities

have

been

CMU,

MIT,

and

Stanford

which

are

top

universities

in

the

field

of

AI

even

today


McCarthy

(
1958
)

programming

language

Lisp


In

the

1950
’s

and

1960
’s

huge

leaps

forward

were

made

in

operating

within

microworlds

(e
.
g
.
,

the

blocks

world)


Also

robotics

went

forward
:

e
.
g
.

Shakey

from

SRI

(
1969
)


As

well

research

on

neural

networks

(Widrow

&

Hoff,

Rosenblatt’s

perceptron)


Eventually

it

however

became

evident

that

the

success

within

microworlds

does

not

scale

up

as

such


It

had

been

obtained

without

a

deeper

understanding

of

the

target

problem

and

by

using

computationally

intensive

methods

10

The History of AI (Cont)


Neural

networks

were

wiped

out

of

computer

science

research

for

over

a

decade

by

Minsky

and

Papert’s

proof

of

the

poor

expressive

power

of

the

perceptron

(
xor

function)


In

1970
’s

expert

systems

were

being

developed,

they

gather

the

deep

knowledge

of

one

application

field


Expert

systems

gained

a

better

expertise

than

human

experts

in

many

fields

and

they

became

the

first

commercial

success

story

of

AI


Developing

expert

systems

however

turned

out

to

be

meticulous

work

that

cannot

really

be

made

automatic


Logic

programming

had

its

brightest

time

in

the

mid

1980
’s


Study

of

neural

networks

returned

back

to

computer

science

research

in

the

mid

1980
’s

11

The History of AI (Cont)


Also

the

raise

of

machine

learning

research

dates

back

to

the

1980
’s


The

research

of

Bayesian

networks

also

started

at

that

time


Maybe

the

second

important

commercial

success

due

to

the

heavy

influence

of

Microsoft


Later

on

these

topics

have

been

studied

under

the

label

of

data

mining

and

knowledge

discovery


Agents

are

an

important

technology

in

many

fields

of

computing


A

recent

trend

is

also

direction

towards

analytic

research

instead

of

using

just

ad

hoc

techniques


Theoretical

models

of

machine

learning


Well
-
founded

methods

of

planning


The

new

raise

of

game

theory

12

The History of Artificial
Intelligence

(More Details)













Timeline of major AI events



Evidence

of

Artificial

Intelligence

folklore

can

be

traced

back

to

ancient

Egypt,

but

with

the

development

of

the

electronic

computer

in

1941
,

the

technology

finally

became

available

to

create

machine

intelligence
.

The

term

artificial

intelligence

was

first

coined

in

1956
,

at

the

Dartmouth

conference,

and

since

then

Artificial

Intelligence

has

expanded

because

of

the

theories

and

principles

developed

by

its

dedicated

researchers
.

Through

its

short

modern

history,

advancement

in

the

fields

of

AI

have

been

slower

than

first

estimated,

progress

continues

to

be

made
.

From

its

birth

4

decades

ago,

there

have

been

a

variety

of

AI

programs,

and

they

have

impacted

other

technological

advancements
.


13

The

Era

of

the

Computer
:


In

1941

an

invention

revolutionized

every

aspect

of

the

storage

and

processing

of

information
.

That

invention,

developed

in

both

the

US

and

Germany

was

the

electronic

computer
.

The

first

computers

required

large,

separate

air
-
conditioned

rooms,

and

were

a

programmers

nightmare,

involving

the

separate

configuration

of

thousands

of

wires

to

even

get

a

program

running
.


The

1949

innovation,

the

stored

program

computer,

made

the

job

of

entering

a

program

easier,

and

advancements

in

computer

theory

lead

to

computer

science,

and

eventually

Artificial

intelligence
.

With

the

invention

of

an

electronic

means

of

processing

data,

came

a

medium

that

made

AI

possible
.

The History of Artificial
Intelligence

(More Details) (Cont)

14


The

Beginnings

of

AI
:


Although

the

computer

provided

the

technology

necessary

for

AI,

it

was

not

until

the

early

1950
's

that

the

link

between

human

intelligence

and

machines

was

really

observed
.

Norbert

Wiener

was

one

of

the

first

Americans

to

make

observations

on

the

principle

of

feedback

theory

feedback

theory
.

The

most

familiar

example

of

feedback

theory

is

the

thermostat
:

It

controls

the

temperature

of

an

environment

by

gathering

the

actual

temperature

of

the

house,

comparing

it

to

the

desired

temperature,

and

responding

by

turning

the

heat

up

or

down
.

What

was

so

important

about

his

research

into

feedback

loops

was

that

Wiener

theorized

that

all

intelligent

behavior

was

the

result

of

feedback

mechanisms
.

Mechanisms

that

could

possibly

be

simulated

by

machines
.

This

discovery

influenced

much

of

early

development

of

AI
.

The History of Artificial
Intelligence

(More Details) (Cont)

15


In

late

1955
,

Newell

and

Simon

developed

The

Logic

Theorist
,

considered

by

many

to

be

the

first

AI

program
.

The

program,

representing

each

problem

as

a

tree

model,

would

attempt

to

solve

it

by

selecting

the

branch

that

would

most

likely

result

in

the

correct

conclusion
.

The

impact

that

the

logic

theorist

made

on

both

the

public

and

the

field

of

AI

has

made

it

a

crucial

stepping

stone

in

developing

the

AI

field
.



In

1956

John

Mc

Carthy

regarded

as

the

father

of

AI,

organized

a

conference

to

draw

the

talent

and

expertise

of

others

interested

in

machine

intelligence

for

a

month

of

brainstorming
.

He

invited

them

to

Vermont

for

"The

Dartmouth

summer

research

project

on

artificial

intelligence
.
"

From

that

point

on,

because

of

McCarthy,

the

field

would

be

known

as

Artificial

intelligence
.

Although

not

a

huge

success,

(explain)

the

Dartmouth

conference

did

bring

together

the

founders

in

AI,

and

served

to

lay

the

groundwork

for

the

future

of

AI

research
.


The History of Artificial
Intelligence

(More Details) (Cont)

16


In

the

seven

years

after

the

conference,

AI

began

to

pick

up

momentum
.

Although

the

field

was

still

undefined,

ideas

formed

at

the

conference

were

re
-
examined,

and

built

upon
.

Centers

for

AI

research

began

forming

at

Carnegie

Mellon

and

MIT,

and

new

challenges

were

faced
:

further

research

was

placed

upon

creating

systems

that

could

efficiently

solve

problems,

by

limiting

the

search,

such

as

the

Logic

Theorist
.

And

second,

making

systems

that

could

learn

by

themselves
.


In

1957
,

the

first

version

of

a

new

program

The

General

Problem

Solver

(GPS)

was

tested
.

The

program

developed

by

the

same

pair

which

developed

the

Logic

Theorist
.

The

GPS

was

an

extension

of

Wiener's

feedback

principle,

and

was

capable

of

solving

a

greater

extent

of

common

sense

problems
.

A

couple

of

years

after

the

GPS,

IBM

contracted

a

team

to

research

artificial

intelligence
.

Herbert

Gelerneter

spent

3

years

working

on

a

program

for

solving

geometry

theorems
.


While

more

programs

were

being

produced,

McCarthy

was

busy

developing

a

major

breakthrough

in

AI

history
.

In

1958

McCarthy

announced

his

new

development
;

the

LISP

language,

which

is

still

used

today
.

LISP

stands

for

LISt

Processing,

and

was

soon

adopted

as

the

language

of

choice

among

most

AI

developers
.

The History of Artificial
Intelligence

(More Details) (Cont)

17


In

1963

MIT

received

a

2
.
2

million

dollar

grant

from

the

United

States

government

to

be

used

in

researching

Machine
-
Aided

Cognition

(artificial

intelligence)
.

The

grant

by

the

Department

of

Defense's

Advanced

research

projects

Agency

(ARPA),

to

ensure

that

the

US

would

stay

ahead

of

the

Soviet

Union

in

technological

advancements
.

The

project

served

to

increase

the

pace

of

development

in

AI

research,

by

drawing

computer

scientists

from

around

the

world,

and

continues

funding
.

The History of Artificial
Intelligence

(More Details) (Cont)

18


The

Multitude

of

programs


The

next

few

years

showed

a

multitude

of

programs,

one

notably

was

SHRDLU
.

SHRDLU

was

part

of

the

microworlds

project,

which

consisted

of

research

and

programming

in

small

worlds

(such

as

with

a

limited

number

of

geometric

shapes)
.

The

MIT

researchers

headed

by

Marvin

Minsky,

demonstrated

that

when

confined

to

a

small

subject

matter,

computer

programs

could

solve

spatial

problems

and

logic

problems
.

Other

programs

which

appeared

during

the

late

1960
's

were

STUDENT,

which

could

solve

algebra

story

problems,

and

SIR

which

could

understand

simple

English

sentences
.

The

result

of

these

programs

was

a

refinement

in

language

comprehension

and

logic
.

The History of Artificial
Intelligence

(More Details) (Cont)

19


Another

advancement

in

the

1970
's

was

the

advent

of

the

expert

system
.

Expert

systems

predict

the

probability

of

a

solution

under

set

conditions
.

For

example
:


Because

of

the

large

storage

capacity

of

computers

at

the

time,

expert

systems

had

the

potential

to

interpret

statistics,

to

formulate

rules
.

And

the

applications

in

the

market

place

were

extensive,

and

over

the

course

of

ten

years,

expert

systems

had

been

introduced

to

forecast

the

stock

market,

aiding

doctors

with

the

ability

to

diagnose

disease,

and

instruct

miners

to

promising

mineral

locations
.

This

was

made

possible

because

of

the

systems

ability

to

store

conditional

rules,

and

a

storage

of

information
.



During

the

1970
's

Many

new

methods

in

the

development

of

AI

were

tested,

notably

Minsky's

frames

theory
.

Also

David

Marr

proposed

new

theories

about

machine

vision,

for

example,

how

it

would

be

possible

to

distinguish

an

image

based

on

the

shading

of

an

image,

basic

information

on

shapes,

color,

edges,

and

texture
.

With

analysis

of

this

information,

frames

of

what

an

image

might

be

could

then

be

referenced
.

another

development

during

this

time

was

the

PROLOGUE

language
.

The

language

was

proposed

for

In

1972
,


The History of Artificial
Intelligence

(More Details) (Cont)

20


During

the

1980
's

AI

was

moving

at

a

faster

pace,

and

further

into

the

corporate

sector
.

In

1986
,

US

sales

of

AI
-
related

hardware

and

software

surged

to

$
425

million
.

Expert

systems

in

particular

demand

because

of

their

efficiency
.

Companies

such

as

Digital

Electronics

were

using

XCON,

an

expert

system

designed

to

program

the

large

VAX

computers
.

DuPont,

General

Motors,

and

Boeing

relied

heavily

on

expert

systems

Indeed

to

keep

up

with

the

demand

for

the

computer

experts,

companies

such

as

Teknowledge

and

Intellicorp

specializing

in

creating

software

to

aid

in

producing

expert

systems

formed
.

Other

expert

systems

were

designed

to

find

and

correct

flaws

in

existing

expert

systems
.


The History of Artificial
Intelligence

(More Details) (Cont)

21


The

Transition

from

Lab

to

Life


The

impact

of

the

computer

technology,

AI

included

was

felt
.

No

longer

was

the

computer

technology

just

part

of

a

select

few

researchers

in

laboratories
.

The

personal

computer

made

its

debut

along

with

many

technological

magazines
.

Such

foundations

as

the

American

Association

for

Artificial

Intelligence

also

started
.

There

was

also,

with

the

demand

for

AI

development,

a

push

for

researchers

to

join

private

companies
.

150

companies

such

as

DEC

which

employed

its

AI

research

group

of

700

personnel,

spend

$
1

billion

on

internal

AI

groups
.


Other

fields

of

AI

also

made

there

way

into

the

marketplace

during

the

1980
's
.

One

in

particular

was

the

machine

vision

field
.

The

work

by

Minsky

and

Marr

were

now

the

foundation

for

the

cameras

and

computers

on

assembly

lines,

performing

quality

control
.

Although

crude,

these

systems

could

distinguish

differences

shapes

in

objects

using

black

and

white

differences
.

By

1985

over

a

hundred

companies

offered

machine

vision

systems

in

the

US,

and

sales

totaled

$
80

million
.

The History of Artificial
Intelligence

(More Details) (Cont)

22


The

Transition

from

Lab

to

Life

(Cont)



The

1980
's

were

not

totally

good

for

the

AI

industry
.

In

1986
-
87

the

demand

in

AI

systems

decreased,

and

the

industry

lost

almost

a

half

of

a

billion

dollars
.

Companies

such

as

Teknowledge

and

Intellicorp

together

lost

more

than

$
6

million,

about

a

third

of

there

total

earnings
.

The

large

losses

convinced

many

research

leaders

to

cut

back

funding
.

Another

disappointment

was

the

so

called

"smart

truck"

financed

by

the

Defense

Advanced

Research

Projects

Agency
.

The

projects

goal

was

to

develop

a

robot

that

could

perform

many

battlefield

tasks
.

In

1989
,

due

to

project

setbacks

and

unlikely

success,

the

Pentagon

cut

funding

for

the

project
.


Despite

these

discouraging

events,

AI

slowly

recovered
.

New

technology

in

Japan

was

being

developed
.

Fuzzy

logic,

first

pioneered

in

the

US

has

the

unique

ability

to

make

decisions

under

uncertain

conditions
.

Also

neural

networks

were

being

reconsidered

as

possible

ways

of

achieving

Artificial

Intelligence
.

The

1980
's

introduced

to

its

place

in

the

corporate

marketplace,

and

showed

the

technology

had

real

life

uses,

ensuring

it

would

be

a

key

in

the

21
st

century
.


The History of Artificial
Intelligence

(More Details) (Cont)

23


AI

put

to

the

Test


The

military

put

AI

based

hardware

to

the

test

of

war

during

Desert

Storm
.

AI
-
based

technologies

were

used

in

missile

systems,

heads
-
up
-
displays,

and

other

advancements
.

AI

has

also

made

the

transition

to

the

home
.

With

the

popularity

of

the

AI

computer

growing,

the

interest

of

the

public

has

also

grown
.

Applications

for

the

Apple

Macintosh

and

IBM

compatible

computer,

such

as

voice

and

character

recognition

have

become

available
.

Also

AI

technology

has

made

steadying

camcorders

simple

using

fuzzy

logic
.

With

a

greater

demand

for

AI
-
related

technology,

new

advancements

are

becoming

available
.

Inevitably

Artificial

Intelligence

has,

and

will

continue

to

affecting

our

lives
.

The History of Artificial
Intelligence

(More Details) (Cont)

24

The State of the Art


Different

activities

in

many

subfields
:


Robotic

vehicles
:

Driverless

robotic

cars

are

being

developed

in

closed

environments

and

more

and

more

in

daily

traffic
.

Modern

cars

recognize

speed

limits,

adapt

to

the

traffic,

take

care

of

pedestrian

safety,

can

park

themselves,

have

intelligent

light

systems,

wake

up

the

driver,




Speech

recognition
:

Many

devices

and

services

nowadays

understand

spoken

words

(even

dialogs)


Autonomous

planning

and

scheduling
:

E
.
g
.

space

missions

are

tomorrow

planned

autonomously


Game

playing
:

Computers

defeat

human

world

champions

in

chess

systematically

and

convincingly


Spam

fighting
:

Learning

algorithms

reliably

filter

away

80
%

or

90
%

of

all

messages

saving

us

time

for

more

important

tasks

25

The State of the Art (Cont)


Different

activities

in

many

subfields

(Cont)
:


Logistics

planning
:

E
.
g
.

military

operations

are

helped

by

automated

logistics

planning

and

scheduling

for

transportation


Robotics
:

Autonomous

vacuum

cleaners,

lawn

movers,

toys,

and

special

(hazardous)

environment

robots

are

common

these

days


Machine

translation
:

Translation

programs

based

on

statistics

and

machine

learning

are

in

ever

increasing

demand

(in

particular

in

EU)

26

Approaches


In

the

quest

to

create

intelligent

machines,

the

field

of

Artificial

Intelligence

has

split

into

several

different

approaches

based

on

the

opinions

about

the

most

promising

methods

and

theories
.

These

rivaling

theories

have

lead

researchers

in

one

of

two

basic

approaches
;

bottom
-
up

and

top
-
down
.

Bottom
-
up

theorists

believe

the

best

way

to

achieve

artificial

intelligence

is

to

build

electronic

replicas

of

the

human

brain's

complex

network

of

neurons,

while

the

top
-
down

approach

attempts

to

mimic

the

brain's

behavior

with

computer

programs
.


27

Neural Networks and Parallel
Computation



The

human

brain

is

made

up

of

a

web

of

billions

of

cells

called

neurons,

and

understanding

its

complexities

is

seen

as

one

of

the

last

frontiers

in

scientific

research
.

It

is

the

aim

of

AI

researchers

who

prefer

this

bottom
-
up

approach

to

construct

electronic

circuits

that

act

as

neurons

do

in

the

human

brain
.

Although

much

of

the

working

of

the

brain

remains

unknown,

the

complex

network

of

neurons

is

what

gives

humans

intelligent

characteristics
.

By

itself,

a

neuron

is

not

intelligent,

but

when

grouped

together,

neurons

are

able

to

pass

electrical

signals

through

networks
.

The neuron "firing", passing a signal
to the next in the chain


28

Neural Networks and Parallel
Computation (Cont)



Research

has

shown

that

a

signal

received

by

a

neuron

travels

through

the

dendrite

region,

and

down

the

axon
.

Separating

nerve

cells

is

a

gap

called

the

synapse
.

In

order

for

the

signal

to

be

transferred

to

the

next

neuron,

the

signal

must

be

converted

from

electrical

to

chemical

energy
.

The

signal

can

then

be

received

by

the

next

neuron

and

processed
.


Warren

McCulloch

after

completing

medical

school

at

Yale,

along

with

Walter

Pitts

a

mathematician

proposed

a

hypothesis

to

explain

the

fundamentals

of

how

neural

networks

made

the

brain

work
.

Based

on

experiments

with

neurons,

McCulloch

and

Pitts

showed

that

neurons

might

be

considered

devices

for

processing

binary

numbers
.

An

important

back

of

mathematic

logic,

binary

numbers

(represented

as

1
's

and

0
's

or

true

and

false)

were

also

the

basis

of

the

electronic

computer
.

This

link

is

the

basis

of

computer
-
simulated

neural

networks,

also

know

as

Parallel

computing
.

29

Neural Networks and Parallel
Computation (Cont)



A

century

earlier

the

true

/

false

nature

of

binary

numbers

was

theorized

in

1854

by

George

Boole

in

his

postulates

concerning

the

Laws

of

Thought
.

Boole's

principles

make

up

what

is

known

as

Boolean

algebra,

the

collection

of

logic

concerning

AND,

OR,

NOT

operands
.

For

example

according

to

the

Laws

of

thought

the

statement
:

(for

this

example

consider

all

apples

red)



-

Apples

are

red
--

is

True


-

Apples

are

red

AND

oranges

are

purple
--

is

False


-

Apples

are

red

OR

oranges

are

purple
--

is

True


-
Apples

are

red

AND

oranges

are

NOT

purple
--

is

also

True




Boole

also

assumed

that

the

human

mind

works

according

to

these

laws,

it

performs

logical

operations

that

could

be

reasoned
.

Ninety

years

later,

Claude

Shannon

applied

Boole's

principles

in

circuits,

the

blueprint

for

electronic

computers
.

Boole's

contribution

to

the

future

of

computing

and

Artificial

Intelligence

was

immeasurable,

and

his

logic

is

the

basis

of

neural

networks
.

30

Neural Networks and Parallel
Computation (Cont)



McCulloch

and

Pitts,

using

Boole's

principles,

wrote

a

paper

on

neural

network

theory
.

The

thesis

dealt

with

how

the

networks

of

connected

neurons

could

perform

logical

operations
.

It

also

stated

that,

one

the

level

of

a

single

neuron,

the

release

or

failure

to

release

an

impulse

was

the

basis

by

which

the

brain

makes

true

/

false

decisions
.

Using

the

idea

of

feedback

theory,

they

described

the

loop

which

existed

between

the

senses

---
>

brain

---
>

muscles,

and

likewise

concluded

that

Memory

could

be

defined

as

the

signals

in

a

closed

loop

of

neurons
.

Although

we

now

know

that

logic

in

the

brain

occurs

at

a

level

higher

then

McCulloch

and

Pitts

theorized,

their

contributions

were

important

to

AI

because

they

showed

how

the

firing

of

signals

between

connected

neurons

could

cause

the

brains

to

make

decisions
.

McCulloch

and

Pitt's

theory

is

the

basis

of

the

artificial

neural

network

theory
.

31

Neural Networks and Parallel
Computation (Cont)



Using

this

theory,

McCulloch

and

Pitts

then

designed

electronic

replicas

of

neural

networks,

to

show

how

electronic

networks

could

generate

logical

processes
.

They

also

stated

that

neural

networks

may,

in

the

future,

be

able

to

learn,

and

recognize

patterns
.

The

results

of

their

research

and

two

of

Weiner's

books

served

to

increase

enthusiasm,

and

laboratories

of

computer

simulated

neurons

were

set

up

across

the

country
.


Two

major

factors

have

inhibited

the

development

of

full

scale

neural

networks
.

Because

of

the

expense

of

constructing

a

machine

to

simulate

neurons,

it

was

expensive

even

to

construct

neural

networks

with

the

number

of

neurons

in

an

ant
.

Although

the

cost

of

components

have

decreased,

the

computer

would

have

to

grow

thousands

of

times

larger

to

be

on

the

scale

of

the

human

brain
.

The

second

factor

is

current

computer

architecture
.

The

standard

Von

Neuman

computer,

the

architecture

of

nearly

all

computers,

lacks

an

adequate

number

of

pathways

between

components
.

Researchers

are

now

developing

alternate

architectures

for

use

with

neural

networks
.


32

Neural Networks and Parallel
Computation (Cont)



Even

with

these

inhibiting

factors,

artificial

neural

networks

have

presented

some

impressive

results
.

Frank

Rosenblatt,

experimenting

with

computer

simulated

networks,

was

able

to

create

a

machine

that

could

mimic

the

human

thinking

process,

and

recognize

letters
.

But,

with

new

top
-
down

methods

becoming

popular,

parallel

computing

was

put

on

hold
.

Now

neural

networks

are

making

a

return,

and

some

researchers

believe

that

with

new

computer

architectures,

parallel

computing

and

the

bottom
-
up

theory

will

be

a

driving

factor

in

creating

artificial

intelligence
.