Can Machines Think?Can Machines Think?

 Peter Bock  

Professor of Machine Intelligence and Cognition

Director of Project ALISA

Department of Computer Science

The George Washington University

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Background IssuesBackground 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]

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Fundamental PropositionsFundamental PropositionsDefinition: 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 IssuesBackground 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]

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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 100 = 1

worm 104 = 10,000

sea slug 107 = 10,000,000

tiny lizard 108 = 100,000,000 = 10 MB

desktop computer 1010 = 10,000,000,000 = 1 GB

DNA molecule 1010 = 10,000,000,000 = 1 GB

frog 1011 = 100,000,000,000 = 10 GB

mainframe computer 1012 = 1,000,000,000,000 = 100 GB

dog 1014 = 100,000,000,000,000 = 10,000 GB = 10 TB

human being 1015 = 1,000,000,000,000,000 = 100 TB

human species 1025 = 10,000,000,000,000,000,000,000,000 = 1 YB

universe 1084 =

Entity Intelligence Capacity (bits)

Examples of Intelligence CapacityExamples of Intelligence Capacity

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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

FrogNOW9 2000 - 2006 MP RISC 10 GB 1 GB 0.01

Growth of Computer Memory CapacityGrowth of Computer Memory Capacity

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1 2 3 4 5 6 7 8

1 Megabyte

1 Kilobyte

1 Gigabyte

1 Terabyte

1 Petabyte9

MemoryCapacity

Time Period

1952 1958 1964 1970 1976 1982 1988 1994 2000 2006

Growth of Computer Memory CapacityGrowth of Computer Memory Capacity

NOW

Generation

Mainframe RAM

PC RAM

human brain

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1 2 3 4 5 6 7 8

Generation9

MemoryCapacity

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 CapacityGrowth of Computer Memory Capacity

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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

MemoryCapacity

tech

no

log

y ch

ang

e

2036

human brain

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PC RAM

Mainframe RAM

Growth of Computer Memory CapacityGrowth of Computer Memory Capacity

Generation

Time Period

NOW

2006

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Knowledge AcquisitionKnowledge Acquisition

Definition: Knowledge is the instantiation of intelligence.

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

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Knowledge AcquisitionKnowledge Acquisition

10% capacity of the brain ≈ 1014 bits1 line of code (rule) ≈ 1000 bits ≈ 100 billion rules

software production rate ≈ 10 lines of code per person-hoursoftware production time ≈ 1010 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.

ProgrammingProgramming

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...

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10% capacity of the brain ≈ 1014 bitsdata transfer rate ≈ 108 bits per second

data transfer time ≈ 106 seconds≈ 12 days

HOW ???

GREAT !!!

Direct TransferDirect Transfer

Knowledge AcquisitionKnowledge Acquisition

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THAT’S BETTER !!!

10% capacity of the brain ≈ 1014 bitsaverage rate of sensory input ≈ 500,000 bits per second

knowledge acquisition time ≈ 200,000,000 seconds≈ 3500 days (16 hours per day)≈ 10 years

LearningLearning

Collective Learning Systems (CLS) [Bock 1976]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 AcquisitionKnowledge Acquisition

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

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Edvard Munch(10 images)

Training Style

mimicry = 25%brush size = thick

influence = high

Derived Art

Source Image

photograph Courtesy of Ben Rubinger

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Training Style

Monet(39 images)

mimicry = 28%brush size = large

influence = high

Derived Art

Source Image

photograph Courtesy of Ben Rubinger

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Source Image

photograph

mimicry = 28%brush size = medium

influence = medium

Derived ArtTraining Style

Sam Brown(171 images)

Courtesy of Ben Rubinger

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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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