Can Machines Think?

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8 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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Can Machines Think?


Peter Bock




Professor of Machine Intelligence and Cognition

Director of Project ALISA

Department of Computer Science

The George Washington University



1

Background Issues

Assumption:

... the question of whether
Machines Can Think

... is about as relevant


as the question of whether
Submarines Can Swim
. [Dijkstra 1984]

Axiom:

The whole is greater than the sum of its parts.

Axiom:

The whole is
exactly equal

to the sum of its parts; if it seems otherwise,
at least one of its parts has been overlooked. [Bock 2005]

Definition:

A set may be arbitrarily large and complex. [Cantor 1874]

[??????????]

Definition:

A
part

of an entity consists exclusively of matter and/or energy. [Bock 2005]

2

Fundamental Propositions

Definition:

Intelligence

is the ability of an entity to synthesize responses that are
significantly correlated with its stimuli. [Bock 1993]

Postulate:

Intelligence capacity

is a measure of the amount of
information

that can be stored in the memory of an entity. [Bock 1993]

Background Issues

Definition:

The standard unit of
information

is the
bit
, which is the base
-
2 logarithm of the
number of unique states an entity can be in. [Shannon & Weaver, 1949]

Assumption:

... the question of whether
Machines Can Think

... is about as relevant

as the question of whether
Submarines Can Swim
. [Dijkstra 1984]

Axiom:

The whole is greater than the sum of its parts.

Axiom:

The whole is
exactly equal

to the sum of its parts; if it seems otherwise,
at least one of its parts has been overlooked. [Bock 2002]

Definition:

A set may be arbitrarily large and complex. [Cantor 1874]

[??????????]

Definition:

A
part

of an entity consists exclusively of matter and/or energy.

[Bock 2002]

3

1,000,000,000,000,000,000,000,


000,000,000,000,000,000,000,


000,000,000,000,000,000,000,


000,000,000,000,000,000,000
(number of baryons)

toggle switch

10
0

=

1

worm

10
4

=

10,000

sea slug

10
7

=

10,000,000

tiny lizard

10
8

=

100,000,000 = 10
MB

desktop computer

10
10

=

10,000,000,000 = 1
GB

DNA molecule

10
10

=

10,000,000,000 = 1
GB

frog

10
11

=

100,000,000,000 = 10
GB

mainframe computer

10
12

=

1,000,000,000,000 = 100
GB

dog

10
14

=

100,000,000,000,000 = 10,000
GB
= 10
TB

human being

10
15

=

1,000,000,000,000,000 = 100
TB

human species

10
25

=

10,000,000,000,000,000,000,000,000 = 1
YB

universe

10
84

=


Entity

Intelligence Capacity (bits)

Examples of Intelligence Capacity

4




RAM capacity (bytes)

generation

period

technology



mainframe

PC

% human


1

1952
-

1958

vacuum tube

0.1 KB


2

1958
-

1964

transistor

1 KB




3

1964
-

1970

SSI

10 KB




4

1970
-

1976

MSI

100 KB




5

1976
-

1982

LSI

1 MB

100 KB

0.000001


6

1982
-

1988

VLSI

10 MB

1 MB

0.00001


7

1988
-

1994

CISC

100 MB

10 MB

0.0001


8

1994
-

2000

RISC

1 GB

100 MB

0.001

Frog

NOW


9

2000
-

2006

MP RISC

10 GB

1 GB

0.01

Growth of Computer Memory Capacity

5

1

2

3

4

5

6

7

8

1 Megabyte

1 Kilobyte

1 Gigabyte

1 Terabyte

1 Petabyte

9

Memory

Capacity

Time Period

1952

1958

1964

1970

1976

1982

1988

1994

2000

2006

Growth of Computer Memory Capacity


NOW

Generation

Mainframe RAM

PC RAM


human brain

6

1

2

3

4

5

6

7

8

Generation

9

Memory

Capacity

1 Megabyte

1 Kilobyte

1 Gigabyte

1 Terabyte

1 Petabyte

Time Period

1952

1958

1964

1970

1976

1982

1988

1994

2000

2006


NOW

Mainframe RAM

PC RAM

my PC
disk

capacities


human brain

my PC
RAM

capacities

Growth of Computer Memory Capacity

7

1

2

3

4

5

6

7

8

9

10

11

12

13

1952

1958

1964

1970

1976

1982

1988

1994

2000

2012

2018

2024

2030

1 Megabyte

1 Kilobyte

1 Gigabyte

1 Terabyte

1 Petabyte

Memory

Capacity

technology change

2036


human brain

14

PC RAM

Mainframe RAM

Growth of Computer Memory Capacity

Generation

Time Period


NOW

2006

8

Knowledge Acquisition

Definition:

Knowledge

is the instantiation of intelligence.

Definition:

Cognition

(
Thinking
)

is the mental process of acquiring, representing,
processing, and applying knowledge.

9

Knowledge Acquisition


10% capacity of the brain



10
14

bits



1 line of code (rule)



1000 bits



100 billion rules


software production rate



10 lines of code per person
-
hour


software production time



10
10

person
-
hours





10,000,000 person
-
years !!!

Definition:

Knowledge

is the instantiation of intelligence.

Definition:

Cognition

(
Thinking
) is the mental process of acquiring, representing,
processing, and applying knowledge.

IMPOSSIBLE !!!

Fact:

This approach for achieving robust AI was abandoned in the mid
-
1980’s.

Programming

Fact:

CYC:
rule
-
based system funded by DARPA and directed by Douglas Lenat





under construction for more than 20 years at MCC in Texas





objective is to include 1 billion “common sense” rules





no significant successes and many, many failures

NONETHELESS...

10


10% capacity of the brain



10
14

bits


data transfer rate



10
8

bits per second


data transfer time



10
6

seconds





12 days

HOW ???

GREAT !!!

Direct Transfer

Knowledge Acquisition

11

THAT’S BETTER !!!


10% capacity of the brain



10
14

bits


average rate of sensory input



500,000 bits per second


knowledge acquisition time



200,000,000 seconds





3500 days (16 hours per day)





10 years

Learning

Collective Learning Systems (CLS) [Bock 1976]

Definition:

Project ALISA

is an adaptive non
-
parametric parallel
-
processing
statistical knowledge acquisition and classification system based on
CLS theory. [Bock,
et al
. 1992]


Practical applications are illustrated on my website.

Knowledge Acquisition

Definition:
Learning

is the dynamic acquisition and application of knowledge
based on unsupervised and supervised training.

12

Edvard Munch

(10 images)

Training Style

mimicry = 25%

brush size = thick

influence = high

Derived Art

Source Image

photograph

Courtesy of Ben Rubinger

13

Training Style

Monet

(39 images)

mimicry = 28%

brush size = large

influence = high

Derived Art

Source Image

photograph

Courtesy of Ben Rubinger

14

Source Image

photograph

mimicry = 28%

brush size = medium

influence = medium

Derived Art

Training Style

Sam Brown

(171 images)

Courtesy of Ben Rubinger

15

brick walls

(6 images)

Source Image

Training Style

mimicry = 24%

brush size = medium

influence = high

Derived Art

photograph

Courtesy of Ben Rubinger

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