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Emanating Confluence: Chaos, Complexity, Emergence & Technological Singularity

October 2021

October 2021
7(2):169-195

DOI:10.6084/m9.figshare.16881865

Authors:

The paper seeks to explain significant constructs within artificial intelligence (AI), including but not limited to the impact of ‘information theory;’ entropy, especially in terms of ‘information entropy;’ and language theory (linguistics), dealing with all communication methods and the ‘meaning’ in that communication along with the challenging problems of ‘bias.’ The paper discusses the progression from ‘chaos theory’ to ‘complexity theory’ to ‘emergence’ and finally, the ‘technological singularity.’ It examines such questions: How do chaos and complexity lead to emergent systems that inevitably lead to a technological singularity? How does the endless loop of AI progress as it emanates outward, comes to confluence, and emanates yet again? What does each phase entail? How does our advance towards exponential growth in data affect the progression of AI? What are the dangers of ‘bias’? What are the risks as we move towards emergence? Finally, why must we exercise extreme caution as we come closer to ‘technological singularity’?

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Emanating confluence:

The symbiotic relationship between

artificial intelligence and data

Received (in revised form): 20th August, 2021

Ted W. Gross

Founder, Asanatae, Israel

Ted W. Gross is a futurist and theorist who has worked in the high-tech industry for many years

as a chief technology ofﬁcer and vice president of research and development. Ted believes

there is a hidden order within chaos, and that complexity arises from simple models and concepts.

His work on technology theory and application has been published in various professional

technology journals.

E-mail: tedwgross@gmail.com

Abstract This paper seeks to explain signiﬁcant constructs within artiﬁcial intelligence

(AI), including but not limited to: the impact of ‘information theory’; entropy, especially

in terms of ‘information entropy’; and language theory (linguistics), dealing with all

communication methods and the ‘meaning’ in that communication. The paper discusses

the progression from ‘chaos theory’ to ‘complexity theory’ to ‘emergence’ and ﬁnally the

‘technological singularity’. It examines such questions as: How do chaos and complexity

lead to emergent systems which will inevitably lead to a technological singularity? How

does the endless loop of AI progress as it emanates outward, comes to conﬂuence, and

emanates yet again? What does each phase entail? How does our advance towards

exponential growth in data affect the progression of AI? What are the dangers of ‘bias’?

What are the risks as we move towards emergence? Finally, why must we exercise

extreme caution as we come closer to the technological singularity?

KEYWORDS: artiﬁcial intelligence, entropy, chaos, complexity, emergence, singularity,

superintelligence

WHERE MANY PATHS AND

ERRANDS MEET

‘The Road goes ever on and on,

Down from the door where it began.

Now far ahead the Road has gone,

And I must follow, if I can,

Pursuing it with eager feet,

Until it joins some larger way

Where many paths and errands meet.

And whither then? I cannot say.’1

At the beginning of J.R.R. Tolkien’s

epic saga, ‘The Lord of The Rings’, Bilbo

Baggins, the protagonist from the prequel

‘The Hobbit’,2 leaves the Shire to embark

upon his final trip to Rivendell, the home

of the Elves. As he sets out upon his last

adventure, singing to himself his self-

composed song ‘The Road Goes Ever

On and On’, Bilbo bequeaths his home

and a potent, terrifying and ominous

legacy to his nephew Frodo — ‘one ring

to rule them all’.3 Although Bilbo shows

enormous strength of will by giving up

possession of the ring, he fails to warn his

nephew of the dangers inherent in the ring’s

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power. Indeed, not even Gandalf, the great

magician and seer, feels capable of warning

Frodo of the dangers ahead.

In today’s age of artificial intelligence

(AI), society, much like Bilbo and Frodo,

has acquired something precious and

incredibly powerful, forged through the

accumulation of generations-worth of

knowledge. Moreover, much like Frodo

at the beginning of his adventure, no one

is yet fully aware of the power it contains.

Nevertheless, we persist in exploring it

because ‘the road goes ever on and on’.

Many seers view the advancement of

humankind with a mixture of awe and deep

worry. Some, like Gandalf, choose to keep

their counsel to themselves as they watch

cautiously what paths are taken. Others

are vocal in their objections, warnings or

encouragement as society accelerates towards

horizons unknown.

In terms of where AI is leading

humankind, there are thousands of scenarios

painted upon the canvas of technology.

Optimists paint a new ‘Garden in Eden’

— a garden replete with ever-expanding

beauty and open access to both the Tree

of Knowledge and the Tree of Life. In the

Bible, these two trees, representing ever-

expanding knowledge and eternal life, were

sacrosanct, and the consumption of their

fruit punishable by expulsion and death.

This time, however, there are no such

restrictions. Unlimited knowledge and a

world without disease, where bodies last

forever, looms just beyond the horizon, if

only we can crack the code. Nirvana on the

grandest scale imaginable.

The pessimists, by contrast, see danger

in every advance and around every corner.

They warn that humankind has become

drunk on its powers of discovery and

innovation, and that society is running

headlong into a hell of its own making.

Indeed, they contend, it is only a matter

of time before we cross the line and create

a supreme being — a god with no care

nor compassion, nor empathy for those

who created it, and who, once unleashed,

will be guided by its own logic. We will

be in danger of being assailed and perhaps

annihilated by our own creations.

There is, as always, a middle road — a

road where AI is used in specific areas to

benefit humankind. Health, wealth, freedom

and happiness should be within everyone’s

grasp. Each advance will increase society’s

knowledge and the ability of individuals

to cope with the modern world. As we

are the creators and inventors, humankind

will always remain in absolute control,

and maintain the machines that serve as

homes to AI. The ability to conquer the

base instincts of war, jealousy, destruction,

hatred and racism will lead slowly to a

more enlightened existence. However, the

advance will be controlled, fastidious, and

serve only to increase people’s wellbeing.

Endless paths lie ahead — emanation

on a hitherto unseen scale. Millions of

rivulets flow along the path of data and

fluid time into a quantum existence. AI

feeds endlessly upon this river of data, in a

loop of progression and chaotic existence.

Every day, the river of data — the fuel of

all AI — swells until eventually it reaches

true exponential power. The more we

progress, the more the illusion of our power

grows. In reality, however, our control and

understanding of exactly how AI is working

is failing to keep pace.

Where there is an emanation, nature

demands confluence. Fluid streams

cross paths, intertwine, share and gain

power by combining their prowess.

They communicate in the complexity

of existence, seeking another emanation

to grow and feed upon to maintain and

increase their current. The more data, the

larger the rivulet will grow and the further it

will journey. Some rivulets dry up, leaving

only dry-cracked earth. Others emerge to

become mighty rivers, ever combining into

a singular tremendous power. Emanation

will occur again as other rivulets emerge

from the newly created confluence. It is

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The symbiotic relationship between AI and data

an ever-expanding loop of information,

analyses, discovery, innovation and creation.

Like Bilbo setting out with no idea of the

path ahead — only his desired destination,

society too is upon a road of discovery.

Some travellers are destined to arrive in

Rivendell; others will follow Frodo’s

path and embark upon a journey towards

unimagined power — at a price that many

may be unwilling to pay.

Where does this road lead? What will the

signposts along the way disclose? When will

the destination reveal itself with precision

and clarity? In answer, Bilbo’s soft, haunting

whisper echoes through the expanse of the

universe:

‘And whither then? I cannot say.’

EXPLODING INFORMATION AND

INTELLIGENCE

One of the most fundamental basics of AI is

the unquenchable need for data. In his work

on dataism, Yuval Noah Harari describes

this phenomenon as the ‘data religion’:4

‘Dataism declares that the universe

consists of data flows, and the value of any

phenomenon or entity is determined by its

contribution to data processing … 5

… Like capitalism, dataism too began

as a neutral scientific theory, but is now

mutating into a religion that claims

to determine right and wrong. The

supreme value of this new religion is

“information flow”. If life is the movement

of information, and if we think that life

is good, it follows that we should deepen

and broaden the flow of information in the

universe’.6

Whether or not one treats dataism like an

emerging religion, it is incontestable that

AI has irrevocably changed our approach

to data analytics. No longer satisfied with

Excel spreadsheets and specialised statistical

software, analytics today demands constantly

evolving and evermore sophisticated

algorithms based upon incomprehensible

amounts of data.

This new age of AI affects every aspect

of our lives and our world. As Amy Webb

states in her introduction to ‘The Big

Nine’: ‘AI isn’t a tech trend, a buzzword,

or a temporary distraction — it is the third

era of computing’.7 The sheer volume

of information and data now available,

combined with the quality of thought

and scientific advancement, can bring

any researcher into a state of awe at the

possibilities.

The ‘information explosion’8 will lead

straight to the ‘intelligence explosion’.

As Webb points out, ‘The intelligence

explosion, as foretold 100 years ago by

British mathematician and early AI pioneer

I. J. Good, begins in the late 2060s’.9

The intelligence explosion marks a

turning point as well. As the possibilities

of enhancing AI flourish, so too does its

metamorphosis from assistant to creator.

‘The pioneers of artificial intelligence,

however, notwithstanding their belief in

the imminence of human-level AI, mostly

did not contemplate the possibility of

greater-than-human AI…

… Let an ultraintelligent machine

be defined as a machine that can far

surpass all the intellectual activities of any

man however clever. Since the design

of machines is one of these intellectual

activities, an ultraintelligent machine could

design even better machines; there would

then unquestionably be an “intelligence

explosion”, and the intelligence of man

would be left far behind. Thus, the first

ultraintelligent machine is the last invention

that man need ever make, provided that the

machine is docile enough to tell us how to

keep it under control.’10

Information — which is to say, data — is

the very soul of anything we, as a collective

or as individuals, accomplish, create and

envision. Certainly, it is the lifeblood

of analytics. The fuel that empowers AI

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is data, and without accumulating and

then examining such data, the incredible

technological advances seen today would be

impossible.

Large segments of society, including

researchers, tend to approach AI as a unified

entity. It is not. AI is a conglomeration of a

myriad of theories, discoveries, technological

advancements and innovations. It

encompasses every aspect of human

knowledge and endeavour.

It is also common to think of analytics

as a progression of objective, non-biased

computer algorithms that simply determine

numbers and data. It is not. Analytics is

fuelled by data. Information is, by definition,

data. Information can be wrong, biased, and

come in many forms — not just textual.

Analytics must provide for all situations if it

hopes to be anywhere near correct.

Why is this so important? Data lie at

the heart and soul of AI. Indeed, without

information, AI could not exist. AI requires

massive amounts of information to make any

sort of informed decision. AI is progressing

at an unprecedented pace not only towards

the emergence of an intelligence explosion,

but a possible technological singularity. It is

hard to imagine anything more frightening

than a superintelligence running amok

because the algorithms and data first

imposed upon it were biased and based

upon inaccurate information and deficient

rules. As Spock wisely and logically states:

‘Insufficient facts always invite danger.’11

DISCOVERY, INNOVATION AND DATA

‘Imagination is more important than

knowledge. For knowledge is limited,

whereas imagination embraces the entire

world, stimulating progress, giving birth

to evolution. It is, strictly speaking, a real

factor in scientific research.’12

Many great discoveries, even mathematical

constructs, are made entirely by chance.

By contrast, some follow a great leap of

intuition, leading to possibilities previously

unknown. Imagination, curiosity, and a

deeply ingrained desire to make sense of

the universe fuel these discoveries. AI is

no different. It did not suddenly appear on

the radar. It is born of many decades —

even centuries — of evolutionary progress,

in thinking, capabilities and expanding

technology.

The questions of ‘what we want AI to do’

or ‘what slows us down or speeds us up’ are,

in essence, irrelevant. The answers to such

questions depend upon the nature of the

exact problem that needs to be solved. The

solutions offered today will not be the same

ones offered even 90 days into the future

as our ever-expanding AI horizons grow.

What we must comprehend thoroughly are

the implications of every step we take in AI.

What are we doing with the data? How are

we analysing the data? This requires taking

a step back to understand the evolutionary

process leading to the age of AI.

Some credit Lady Ada Lovelace — the

original pioneer of computer programming

— with creating the world’s first computer

algorithm and, with it, modern analytics.13

Lady Lovelace was working on the

‘analytical engine’, a theoretical machine

proposed by her mentor, Charles Babbage,

which would be capable of solving

equations, precisely calculating a sequence

of Bernoulli numbers. In her notes on the

proposed machine, which challenged Alan

Turing’s thinking process almost a century

later, she wrote:

‘The analytical engine has no pretensions

whatever to originate anything. It can do

whatever we know how to order it to

perform. It can follow analysis; but it has

no power of anticipating any analytical

relations or truths.’14

Others point to Turing’s opening statement,

‘I propose to consider the question, “Can

machines think?”’ in paper ‘Computing

machinery and intelligence’,15 as the first

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The symbiotic relationship between AI and data

step in the quest to realise AI (despite the

term ‘artificial intelligence’ being wholly

absent from the paper). Certainly, it is hard

to overestimate Turing’s contribution to

the field — his famous ‘imitation game’,

also known as the ‘Turing Test’16 remains

the topic of much discussion, and although

written in 1950, the following statement still

resonates:

‘I propose to consider the question, “Can

machines think?” This should begin with

definitions of the meaning of the terms

“machine” and “think”. The definitions

might be framed so as to reflect so far as

possible the normal use of the words, but

this attitude is dangerous, If the meaning

of the words “machine” and “think” are

to be found by examining how they are

commonly used it is difficult to escape

the conclusion that the meaning and the

answer to the question, “Can machines

think?” is to be sought in a statistical

survey such as a Gallup poll. But this

is absurd. Instead of attempting such a

definition I shall replace the question by

another, which is closely related to it and

is expressed in relatively unambiguous

words.’17

Purists will go back to 1637, when

the philosopher René Descartes, in his

‘Discourse on the Method’,18 asserted:

‘Cogito, Ergo Sum’ — ‘I think, therefore

I am’. This brief statement opened up a

world of questions regarding ‘existence’,

‘intelligence’ and ‘consciousness’.

The consequences of this assertion are

considerably more far-reaching than

Descartes could ever have imagined. Most

notably, we now ask in all seriousness, ‘If

a machine can think, then is it conscious?’.

This is no longer the realm of science fiction,

and paradoxically, what lies at the heart of

this progression is the very substance that

empowers humankind’s advancement —

data and the methods used to analyse data.

Many researchers and authors, such as

Nick Bostrom in ‘Superintelligence’,19

Amy Webb in ‘The Big Nine’,20 Ray

Kurzweil in ‘The Singularity Is Near’21

and ‘How To Create A Mind’,22 Yuval

Noah Harari in ‘Homo Deus: A Brief

History of Tomorrow’,23 James Gleick

in ‘Chaos: Making a New Science’24 and

‘The Information: A History, a Theory, a

Flood’,25 Melanie Mitchell in ‘Complexity:

A Guided Tour’26 and ‘Artificial Intelligence:

A Guide for Thinking Humans’,27 to name

but a few, have produced leading-edge

works on understanding AI, data and the

innovative thinking within these fields of

endeavour. The dean of them all, Walter

Isaacson, in some of his seminal works —

‘Leonardo da Vinci’,28 ‘The Innovators’,29

the introduction to ‘Invent and Wander

— The Collected Writings of Jeff Bezos’,30

‘Steve Jobs’31 and ‘The Code Breaker’,32

though concentrating on portraying

personalities, does a remarkable job of

describing the more esoteric developments

in AI, and how the exponential growth of

data has motivated the great innovators.

The truth is, many innovations

and discoveries over the past century

have helped (or if one wishes, pushed)

humankind to reach its current apex, and by

tomorrow scale yet another summit. These

discoveries serve as a foundation, and they

cover all aspects of knowledge, science and

theory. Indeed, whatever mystical destiny

is involved, they seem to happen in waves.

Although they spring from areas that initially

appear to have nothing to do with one

another, given time for osmosis into the

general realm of human knowledge, they

merge into one flowing unit.

‘Two revolutions coincided in the

1950s. Mathematicians, including Claude

Shannon and Alan Turing, showed that

all information could be encoded by

binary digits, known as bits. This led to a

digital revolution powered by circuits with

on-off switches that processed information.

Simultaneously, Watson and Crick

discovered how instructions for building

every cell in every form of life were encoded

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by the four-letter sequences of DNA. Thus

was born an information age based on digital

coding (0100110111001…) and genetic

coding (ACTGGTAGATTACA…). The

flow of history is accelerated when two

rivers converge.’33

The following list is neither comprehensive

nor exhaustive. However, those that are

mentioned are essential to understand if one

is to grasp chaos, complexity, emergence and

singularity, and to understand the marriage

between AI and data.

Information theory

It is often the case that significant discoveries

and innovations require a seed from which

to evolve. This seed, in and of itself, is often

of immense importance. Such is the case

with information theory,34 the significance

of which is impossible to quantify. Digital

information in any form would simply not

exist were it not for information theory.

It began in 1854 with George Boole’s

paper on algebraic logic, ‘An investigation of

the laws of thought on which are founded

the mathematical theories of logic and

probabilities’.35 Boole’s algebraic and logical

notions are known today as a ‘Boolean

function’36 and permeate our thought

processes from an early age. Computer

programmers are entirely reliant upon the

Boolean logical operators, and without such

propositions represented in code, it would

prove impossible to develop any level of

programming sophistication.

‘Boole revolutionised logic by finding ways

to express logical statements using symbols

and equations. He gave true propositions

the value 1 and false propositions a 0. A set

of basic logical operations — such as and,

or, not, either/or, and if/then — could

then be performed using these propositions,

just as if they were math equations.’37

The seed of information theory then

germinated for almost a century. In 1948, at

Bell Laboratories, Claude Shannon published

a paper with an immeasurable impact upon

modern technology and data analysis. In ‘A

mathematical theory of communication’,38

now commonly known as ‘the Magna

Carta of the Information Age’, Shannon

introduced the mind-boggling notion that

information can be quantified and measured.

He applied Boolean logic towards a whole

new cosmos while adding his personal touch

of genius.

‘Shannon figured out that electrical circuits

could execute these logical operations

using an arrangement of on-off switches.

To perform an and function, for example,

two switches could be put in sequence,

so that both had to be on for electricity

to flow. To perform an or function,

the switches could be in parallel so that

electricity would flow if either of them was

on. Slightly more versatile switches called

logic gates could streamline the process.’39

‘Before Shannon’s paper, information

had been viewed as a kind of poorly

defined miasmic fluid. But after Shannon’s

paper, it became apparent that information

is a well-defined and, above all, measurable

quantity…

… Shannon’s theory of information

provides a mathematical definition of

information, and describes precisely how

much information can be communicated

between different elements of a system.

This may not sound like much, but

Shannon’s theory underpins our

understanding of how signals and noise are

related, and why there are definite limits

to the rate at which information can be

communicated within any system, whether

man-made or biological.’40

‘The resulting units’, wrote Shannon, ‘may

be called binary digits, or more briefly,

bits’.41

‘The bit now joined the inch, the

pound, the quart, and the minute as a

determinate quantity — a fundamental

unit of measure. But measuring what?

“A unit for measuring information”,

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The symbiotic relationship between AI and data

Shannon wrote, as though there were such

a thing, measurable and quantifiable, as

information.’42

Until this time, no one had ever presumed

that information could be subject to a

mathematical formula or computational

analysis. With the publication of this

seminal paper, there was the world before

Shannon and the world after Shannon.

Perhaps Shannon’s greatest achievement was

the counter-intuitive approach to analyse

‘information’ independent of ‘meaning’.

In short, when dealing with information,

one does not need to consider the meaning

of the message. Indeed, ‘meaning’ is

superfluous to that actual content. ‘Meaning’

is, in effect, meaningless. As he wrote in

the second introductory paragraph of ‘A

mathematical theory of communication’:

‘The fundamental problem of

communication is that of reproducing at

one point either exactly or approximately

a message selected at another point.

Frequently the messages have meaning;

that is they refer to or are correlated

according to some system with certain

physical or conceptual entities. These

semantic aspects of communication are

irrelevant to the engineering problem. The

significant aspect is that the actual message

is one selected from a set of possible

messages. The system must be designed to

operate for each possible selection, not just

the one which will actually be chosen since

this is unknown at the time of design.’43

What is of concern are probability and

uncertainty. With the birth of the bit,

Shannon took the 0,1->true/false construct

of Boolean functions to a whole new

stratosphere. At the heart of Shannon’s

theory lie ‘noise’ and ‘surprise’. All

communication — human, computer,

over wires, signals, digital — as a universal

fundamental — has an element of ‘noise’

and an element of ‘surprise’. The surprise

is what is left after the noise is eliminated

(and there is always noise on any channel of

communication) without interfering with

the original message..

‘So, what is information? It is what remains

after every iota of natural redundancy

has been squeezed out of a message, and

after every aimless syllable of noise has

been removed. It is the unfettered essence

that passes from computer to computer,

from satellite to Earth, from eye to brain,

and (over many generations of natural

selection) from the natural world to the

collective gene pool of every species.’44

Shannon’s information theory gave

practical birth to the digital age. Without

it, people would be drowning in noise

and uncertainty regarding the veracity of

the messages they shared. Without it, all

modes of communication would be left

grappling with garbled information and

incoherent meanings. Paradoxically, by

ignoring the meaning of a message, by

showing how insignificant ‘meaning’ is to

an actual message, Shannon gave the world

true meaning and the ability to handle

massive amounts of data securely and

coherently. Simply put, information theory

is fundamental to everything.

‘But before Shannon, there was precious

little sense of information as an idea,

a measurable quantity, an object fitted

out for hard science. Before Shannon,

information was a telegram, a photograph,

a paragraph, a song. After Shannon,

information was entirely abstracted into

bits. The sender no longer mattered, the

intent no longer mattered, the medium

no longer mattered, not even the meaning

mattered: a phone conversation, a snatch

of Morse telegraphy, a page from a

detective story were all brought under a

common code.’45

Furthermore, with Shannon’s landmark

paper, another term was born — something

that would also rock the foundations of

science, technology and the digital age:

‘information entropy’.

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Entropy

According to one (almost certainly untrue)

story, when grappling with the terminology

to use in his paper, Shannon asked the

legendary mathematician and physicist, John

von Neumann46 ‘What should I call this

thing?’. Von Neumann reputedly responded:

‘Say that information reduces “entropy”.

It is a good, solid physics word. And more

importantly, no one knows what entropy

really is, so in a debate, you will always have

the advantage’.

Entropy47 is the Second Law of

Thermodynamics,48 and without

understanding the fundamental consequences

of entropy, few technological advances

would have taken place. Any research

on entropy will leave the researcher in

stupefaction over the possible definitions of

a law that lies at the very heart of humanity’s

understanding of the universe. By all logical

formulations, entropy should benefit from an

uncontested, universally accepted definition.

However, as Von Neumann supposedly

stated, this has proven impossible, and this

crucial law is defined differently depending

on the system with which it is being used.

It has even entered the realm of buzzwords,

where one can hear the term ‘entropy’ being

spoken with abandon and without any sort

of essential meaning.

The Second Law of Thermodynamics

states:

‘Entropy always increases until it reaches

a maximum value. The total entropy of a

system will always increase until it reaches

its maximum possible value; it will never

decrease on its own unless an outside agent

works to decrease it.’49

This law holds that there will always be an

expenditure of energy in any system in the

universe. This energy cannot be reused.

The entropy produced by all-natural and

biological systems will continue to expand

until some other outside source interferes

with this trajectory. For example, many

systems release heat as part of their working

process. ‘This so-called heat loss is measured

by a quantity called entropy. Entropy is

a measure of the energy that cannot be

converted into additional work’.50

To address the misuse and

misunderstanding of the term, Professor

Arieh Ben-Naim has suggested abandoning

the word ‘entropy’ altogether, and replacing

it with ‘missing information’. He also

suggests entropy can be classified into

two major categories: ‘one is based on the

interpretation of entropy in terms of the

extent of disorder in a system; the second

involves the interpretation of entropy in

terms of the missing information on the

system’.51

In information theory, Shannon

recognised the order–disorder quandary. He

realised that all information contains ‘noise’,

something which is essentially useless to the

data. The ubiquitous term ‘signal-to-noise

ratio’ comes directly from this formulation.

‘In his classic 1948 paper, Shannon defined

the information content in terms of the

entropy of the message source. People

have sometimes characterised Shannon’s

definition of information content as the

“average amount of surprise” a receiver

experiences on receiving a message, in

which “surprise” means something like the

“degree of uncertainty” the receiver had

about what the source would send next…

… Once again, the entropy (and thus

information content) of a source is defined

in terms of message probabilities and is

not concerned with the ‘meaning’ of a

message.’52

These bits of information are at the heart of

what Shannon named ‘information entropy’

(also known as ‘Shannon entropy’). To

grasp the meaning of any data, information

entropy is necessary.

‘Information is entropy. This was the

strangest and most powerful notion of all.

Entropy — already a difficult and poorly

understood concept — is a measure of

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The symbiotic relationship between AI and data

disorder in thermodynamics, the science of

heat and energy.53

‘In essence, entropy is a measure

of uncertainty. When our uncertainty

is reduced, we gain information, so

information and entropy are two sides of

the same coin.’54

The ‘noise’ is always abstracted in any

data analysis, be it machine learning (ML),

pattern recognition (PR), deep learning

(DL), natural language processing (NLP)

or a simple data comparison. We only

analyse the ‘real’ information available, also

known as ‘the surprise within the message’.

Without this understanding and ability,

without a firm grasp of information entropy,

one can never hope to achieve any sort of

AI or analytics. Predictive analytics would

never work without reducing the noise

and disorder,55 and it lies at the heart of all

modern data analytics. It makes it possible to

reduce the uncertainty of meaning.

Linguistics: Language theory

The history of language and linguistics

is also at the heart of analytics and AI.

Communication is not only about

information entropy but psychology as well.

What we want to impart in our message is

as equally important as what we are actually

saying. Paradoxically, in contradistinction to

information theory, what language coveys is

‘meaning’.

Language plays a critical part in all AI

systems, especially if such a system must

consider sentiment analysis and emotion

recognition (SAER).56 Inferring SAER

from text, even with information entropy,

is difficult. One must include the differing

types of communication, which are non-

verbal and non-textual. Emojis, graphics,

videos, acronyms — all play a crucial role in

current communication technology.

‘Psychology of language, properly

understood, is a discipline which embraces

the study of the acquired system (the

grammar), of the methods of acquisition

(linked to universal grammar), and models

of perception and production, and which

also studies the physical bases for all of this.

This study forms a coherent whole.’57

‘Language is the interaction of

meaning (semantics), conditions on usage

(pragmatics), the physical properties of

its inventory of sounds (phonetics), a

grammar (syntax, or sentence structure),

phonology (sound structure), morphology

(word structure), discourse conversational

organisational principles, information and

gestures. Language is a gestalt — the whole

is greater than the sum of its parts. That is

to say, the whole is not understood merely

by examining its individual components.’58

As if information entropy and determining

the meaning of words precisely were not

enough for data analytics (just think in terms

of a tweet or a Facebook post), there is still

so much more that analytics must consider.

‘Underdeterminacy means that every

utterance in every conversation and every

line in every novel and each sentence

of any speech contains “blank spots” —

unspoken, assumed knowledge, values,

roles and emotions — underdetermined

content that I label “dark matter”.

Language can never be understood

entirely without a shared, internalised set

of values, social structures and knowledge

relationships. In these shared cultural and

psychological components, language filters

what is communicated, guiding a hearer’s

interpretations of what another is saying.

People use the context and cultures in

which they hear language to interpret it.

They also use gestures and intonation, in

order to interpret the full meaning of what

is being communicated.’59

One other significant aspect of language

must be considered within information

and AI. The dawn of language allowed

humankind to preserve memories for

thousands of years. Be it passing down

the stories from generation to generation

or by the written word, this ability makes

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it possible to ‘accrue’ information — to

accumulate it en masse.

‘Single-cell animals could remember

events for seconds, based on chemical

reactions. Animals with brains could

remember events for days. Primates with

culture could pass down information

through several generations. Early human

civilisations with oral histories were able

to preserve stories for hundreds of years.

With the advent of written language

the permanence extended to thousands

of years.’60

Bias

Data analytics has a colossal job in

determining what is actually in the data.

However, the quest for data purity does not

end there. It is essential to deal with possible

bias in and about the actual data. There are

two significant types of bias one must be

wary about in analytics and AI.

Bias in the data

In analysing data for any system, specifically

for an AI system, one must be cognisant

of the origin of said data. For example,

applying medical information from a

massive data lake of 90 per cent male health

statistics to a majority female population will

probably produce flawed results. Likewise,

creating a national average for a political poll

based upon the response from upper middle

class individuals will also produce erroneous

results. All data sets will, by nature, contain

a certain amount of bias. This bias must

be taken into account within analytics and

corresponding AI.

Bias in evaluating or creating the data

One of the most dangerous aspects of data

analytics that can lead to catastrophic results

in AI concerns the biases inherent within the

controlling group.

In 1968, Melvin Conway wrote that

‘organisations which design systems (in

the broad sense used here) are constrained

to produce designs which are copies of

the communication structures of these

organisations’.61

This brief statement became known as

Conway’s law62 and elucidates how bias will

always appear in systems. The systems inherit

the bias of the creators. They imitate those

that created them — ‘clones’ if one wishes

to use the term.

Webb’s research in AI, among other

aspects, concentrates on this bias, as it is

fundamental to correct AI implementation.

As she wisely states: ‘in the absence of

meaningful explanations, what proof do

we have that bias hasn’t crept in? Without

knowing the answer to that question, how

would anyone possibly feel comfortable

trusting AI?’.63

As Webb points out in her evaluation

of a Harvard Business School analysis of

codebases:

‘One of their key findings: design choices

stem from how their teams are organised,

and within those teams, bias and influence

tends to go overlooked. As a result, a small

super network of individuals on a team

wield tremendous power once their work

— whether that’s a comb, a sink, or an

algorithm — is used by or on the public…

Therefore, Conway’s law prevails,

because the tribe’s values — their beliefs,

attitudes, and behaviours as well as their

hidden cognitive biases — are so strongly

entrenched.’64

However, bias does not end there. Bias can

appear in the actual data sets used because of

how the data were initially defined.

‘Since researchers can’t just scrape and

load “ocean data” into a machine-

learning system for training, they will

buy a synthetic data set from a third party

or build one themselves. This is often

problematic because composing that data

set — what goes into it and how it’s

labelled — is rife with decisions made

by a small number of people who often

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The symbiotic relationship between AI and data

aren’t aware of their professional, political,

gender, and many other cognitive biases.’65

In 1956, Dartmouth College hosted the

first conference dedicated to AI.66 The

term ‘artificial intelligence’ is credited to

John McCarthy,67 conference leader and

one of the proposal’s original authors.

Unfortunately, the initial group was

fundamentally flawed, being riddled

with bias. It had no people of colour

and only one woman among 47 eminent

participants — even though many experts

of colour and women were available. The

answer to team creation without bias is fairly

obvious.

‘A truly diverse team would have only one

primary characteristic in common: talent.

There would not be a concentration of any

single gender, race, or ethnicity. Different

political and religious views would be

represented.’68

However, to achieve non-biased data,

the law of ‘talent’ must be universally

applied, and this is simply not realistic.

Human nature will always produce

some type of bias, no matter how

much one prides oneself on being non-

biased and politically correct. Bias is

inherent in everything people do; it is

an expression of one’s individualism.

To complicate matters, what is a bias

for one culture and society is considered

logical, objective and fair for another.

Bias, therefore, does not contain a one-

meaning-solves-all definition.

Detecting if bias is apparent in any

construct depends on how that specific

subculture defines bias and how that

definition is implemented within the system.

Bias will never be eradicated entirely,

although it is, by any definition, always a

negative factor. Data analytics must therefore

take the bias into account and build a

myriad of defences to counter it. Failure to

do so will lead to erroneous results and a

catastrophically flawed AI.

Bayes theorem

Without Bayes’ theorem there is no AI69 —

it really is that important and consequential.

Bayes’ theorem provides the ability to

decipher, manipulate and understand data.

Thomas Bayes’ work ‘An essay towards

solving a problem in the doctrine of

chances’,70 published post-mortem in 1763,

caused much contention and until recent

decades was often dismissed as inaccurate.

The supposition that a mathematical formula

could adjust itself constantly to new realities

based upon the previous answers was

anathema to mathematicians — but a gift to

AI and predictive analytics.

‘On its face Bayes’ rule is a simple, one-

line theorem: by updating our initial

belief about something with objective

new information, we get a new and

improved belief. To its adherents, this is

an elegant statement about learning from

experience … Opponents, meanwhile,

regarded Bayes’ rule as subjectivity run

amok.’71

Many have used the theorem to solve

complex problems. It lies at the heart of ML

and utilising ‘decision trees’72 and ‘random

forests’,73–75 just to name a small part of

critical data analytics within AI. Without

Bayes’ theorem, analysts would be adrift

in a sea of data with no ability to evaluate

said data. The theorem allows for predictive

analytics, which can constantly update itself

to new information.

‘Conceptually, Bayes’ system was simple.

We modify our opinions with objective

information: Initial Beliefs (our guess

where the cue ball landed) + Recent

Objective Data (whether the most recent

ball landed to the left or right of our

original guess) = A New and Improved

Belief. Eventually, names were assigned

to each part of his method: Prior for the

probability of the initial belief; Likelihood

for the probability of other hypotheses

with objective new data; and Posterior for

the probability of the newly revised belief.

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Each time the system is recalculated, the

posterior becomes the prior of the new

iteration. It was an evolving system, which

each new bit of information pushed closer

and closer to certitude.’76

The e-mail spam filter provides an excellent

example of the importance of Bayes’

theorem in the modern world. A ‘one fits

all’ type of rule simply does not work for

filtering spam. This is because a spam filter

must gather data and rearrange suppositions

on an ongoing basis as it continuously learns

and teaches itself to make more informed

decisions on what is considered spam for that

specific system. For this reason, a majority of

spam filters are based upon Bayes’ theorem.

CHAOS THEORY

‘Introduce a little anarchy. Upset the

established order, and everything becomes

chaos. I’m an agent of chaos. Oh, and you

know the thing about chaos? It’s fair!’77

‘Invention, it must be humbly admitted,

does not consist in creating out of void, but

out of chaos; the materials must, in the first

place, be afforded: it can give form to dark,

shapeless substances, but cannot bring into

being the substance itself.’78

In 1961, when experimenting with weather

pattern information, a lack of caffeine led

to Edward Lorenz keying a shortened

decimal into a series of computed numbers.

This mistake gave birth to ‘chaos theory’79

and the ‘butterfly effect’.80 The subsequent

publication of his paper ‘Deterministic

nonperiodic flow’81 prompted a debate on

chaos that is still very much alive today. The

butterfly effect has been prosaically described

as the idea that a butterfly flapping its wings

in Brazil might cause a tornado in Texas.

Alternatively, as Lorenz himself put it:

‘One meteorologist remarked that if the

theory were correct, one flap of a sea

gull’s wings would be enough to alter

the course of the weather forever. The

controversy has not yet been settled, but

the most recent evidence seems to favour

the sea gulls.’82

At the heart of chaos theory lies the

seemingly modest statement which

postulates that small, even minute events

can influence enormous systems leading

to significant consequences — hence the

butterfly effect, or as defined within chaos:

‘sensitivity to initial conditions’.

Surprisingly enough, one can express

chaos in mathematical terms. Randomness

suddenly becomes an orderly disorder,

which means not only in existential terms

but in real-world scenarios that there is a

hidden order to chaos.

‘The modern study of chaos began with

the creeping realisation in the 1960s that

quite simple mathematical equations could

model systems every bit as violent as a

waterfall. Tiny differences in input could

quickly become overwhelming differences

in output — a phenomenon given the

name “sensitive dependence on initial

conditions”.’83

To illustrate the butterfly effect, advocates

of chaos theory often cite the proverb ‘For

want of a nail’:’

‘For want of a nail, the shoe was lost.

For want of the shoe, the horse was lost.

For want of the horse, the rider was lost.

For want of the rider, the battle was lost.

For want of the battle, the kingdom was lost.

And all for the want of a horseshoe nail.’

The lesson is obvious: the lack of something

as inconsequential as a single nail can cause

the loss of a kingdom. When so many

minor events can have such enormous

consequences, how can one even attempt

to predict the behaviour of systems?

This nagging question remained in the

background for centuries. Chaos was

attributed to a supreme being, karma or

plain old luck. Despite its ubiquity, there

was no way to foretell or control chaos.

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The symbiotic relationship between AI and data

Events are random, and random events defy

prediction. Or so everyone believed.

Almost as a genetic imperative, the brain

seeks ‘patterns’. However, the terms ‘chaos’

and ‘patterns’ seem to be polar opposites.

If there is a pattern to be discerned, then

how can there be chaos? Furthermore, if

chaos prevails, then how can there be an

underlying pattern?

In 2005, Lorenz condensed chaos

theory into the following: ‘Chaos: When

the present determines the future, but the

approximate present does not approximately

determine the future’.84

Ancient man sought patterns in the night

sky filled within the chaos of hundreds of

millions of stars and found the constellations.

Modern man seeks underlying patterns of

behaviour and activity in everyday life. The

recent COVID-19 outbreak is an example

of such a pursuit. At present, at least, there

is less focus on the source of the proverbial

nail, but intense interest in such patterns

as how the virus spreads, how specific

prophylactic measures have worked, and

the patterns of historical outbreaks such as

the black death (bubonic plague) and the

Spanish flu after the First World War, in

the hope that it is possible to apply pattern

recognition to combating the spread of

the coronavirus. The identification of such

patterns assists in combating the virus by

making it possible to project possible future

outcomes based upon the present situation.85

(It will be interesting to see how Webb

analyses this in her upcoming book, ‘The

Genesis Machine’.86)

‘Chaos appears in the behaviour of the

weather, the behaviour of an airplane in

flight, the behaviour of cars clustering

on an expressway, the behaviour of oil

flowing in underground pipes. No matter

what the medium, the behaviour obeys

the same newly discovered laws. That

realisation has begun to change the way

business executives make decisions about

insurance, the way astronomers look at

the solar system, the way political theorists

talk about the stresses leading to armed

conflict.’87

Chaos theory specifies not only that there

are geometric patterns to be discerned in

the seemingly random events of a complex

system, but also introduces ‘linear’ and

‘nonlinear’ progressions. Linear progressions

go from step A to step B to step C. Such

systems lend themselves to predictability.

Their patterns are apparent even before they

begin. They take no heed of chaos as they

have clear beginnings with specific steps

along the way. Unfortunately, the way our

brains handle data is mainly linear because

this is how the majority of people are trained

to think from birth.

‘Linear relationships can be captured with a

straight line on a graph. Linear relationships

are easy to think about: the more the

merrier. Linear equations are solvable,

which makes them suitable for textbooks.

Linear systems have an important modular

virtue: you can take them apart, and put

them together again — the pieces add up.

Nonlinear systems generally cannot be

solved and cannot be added together. In

fluid systems and mechanical systems, the

nonlinear terms tend to be the features that

people want to leave out when they try to

get a good, simple understanding … That

twisted changeability makes nonlinearity

hard to calculate, but it also creates rich

kinds of behaviour that never occur in

linear systems.’88

‘How, precisely, does the huge

magnification of initial uncertainties come

about in chaotic systems? The key property

is nonlinearity. A linear system is one

you can understand by understanding its

parts individually and then putting them

together … A nonlinear system is one in

which the whole is different from the sum

of the parts … Linearity is a reductionist’s

dream, and nonlinearity can sometimes be

a reductionist’s nightmare.’89

For instance, structured query language

(SQL) purists set up data stores with

traditional relationships they define for

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the system. This may be wonderful for basic

name-address-looked-at-bought-something

systems, as what is being analysed rests on

a previously decided relationship, whether

one-to-one or one-to-many. Programmers

also have absolute control over the data

going into the system.

Both those who teach and those who

implement SQL programming are blind

to chaos and reject its consequences. They

rid their systems of ‘noise’ by making these

systems adhere to previously defined rules.

Information entropy is predefined in that

there can be no ‘surprise’ in the data, and

bias exists from the initial stage. Suppose

the data are not of a specific predefined

composition (eg string, numeric, binary,

etc). In that case, the data will simply not

enter the system, even if the currently

rejected data may be crucial later on — the

information is forever lost to the system. In

short, pure SQL programming disallows the

viewing of data in nonlinear terms, which

can have disastrous consequences to both

data and AI.

‘Textbooks showed students only the rare

nonlinear systems that would give way

to such techniques. They did not display

sensitive dependence on initial conditions.

Nonlinear systems with real chaos were

rarely taught and rarely learned. When

people stumbled across such things — and

people did — all their training argued

for dismissing them as aberrations. Only

a few were able to remember that the

solvable, orderly, linear systems were the

aberrations. Only a few, that is, understood

how nonlinear nature is in its soul.’90

Data systems today are as chaotic as

predicting the weather. Scraping data

means one does not know what to expect

from the data. One must seek patterns and

information once that data lake takes form.

Approaching data in a linear manner will

always create bias as one decides what data

to find, in what order to find the data, what

the structure of the data must be, and the

rules to which it must adhere. Neither linear

thinking nor traditional SQL structures

can provide a proper answer. Information

entropy, bias and the application of Bayes’

theorem cannot reveal adequate results

because insufficient information is collected.

Failure to conduct data analysis correctly will

always lead to massively erroneous results

in AI.

The ‘eureka moment’ of chaos

theory boils down to a single number

— 4.6692016 — otherwise known as

‘Feigenbaum’s constant’.91 The essential

word here is ‘constant’, although few

scientists or mathematicians would have

believed it was possible until it was

categorically proven. Simply stated, what

Mitchell Feigenbaum discovered was that

there is a universality in how complex

systems work.92 Given enough time, this

constant will always appear in a series.

Moreover, this constant is universal.

Chaos swings like a pendulum along a

mathematical axis. Once one accepts

disorder and chaos, one can plan for it —

even within large systems. The fact that even

within chaotic systems one can find stability

creates a whole new universe of possibilities

‘Although the detailed behaviour of a

chaotic system cannot be predicted, there

is some “order in chaos” seen in universal

properties common to large sets of chaotic

systems, such as the period-doubling route

to chaos and Feigenbaum’s constant.

Thus, even though “prediction becomes

impossible” at the detailed level, there

are some higher-level aspects of chaotic

systems that are indeed predictable.’93

Chaos theory has its limits, however, as

there will always be more than one butterfly

flapping its wings. In many systems,

the sensitivity to initial conditions will

eventually become too complex for any type

of prediction. Lorenz’s weather prediction,

for example, lasts for a short period of a few

days at most. As it stands, there is no way

to discover what ‘initial condition’ may

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The symbiotic relationship between AI and data

become significant in four days’ time. One

may view short-term weather forecasting as

a deterministic system; however, according

to chaos theory, random behaviour remains

a possibility even in a deterministic system

with no external source.

‘The defining idea of chaos is that there

are some systems — chaotic systems —

in which even minuscule uncertainties

in measurements of initial position

and momentum can result in huge

errors in long-term predictions of these

quantities … But sensitive dependence

on initial conditions says that in chaotic

systems, even the tiniest errors in your

initial measurements will eventually

produce huge errors in your prediction

of the future motion of an object. In such

systems (and hurricanes may well be an

example) any error, no matter how small,

will make long-term predictions vastly

inaccurate.’94

This leads to complexity theory.95

‘In our world, complexity flourishes, and

those looking to science for a general

understanding of nature’s habits will be

better served by the laws of chaos.’96

COMPLEXITY THEORY

‘The complexity of things — the things

within things — just seems to be endless.

I mean nothing is easy, nothing is simple.’97

‘Complex systems with many different

initial conditions would naturally produce

many different outcomes, and are so

difficult to predict that chaos theory cannot

be used to deal with them.’98

Nonlinear systems are not so easily defined

nor understood — and so complexity (also

known as ‘complex systems’) enters the

realm of investigation. Complexity theory

intertwines information theory, entropy and

chaos theory, and leads towards ‘emergence’.

Approaching complexity as a single science

with one definition or uniform topic is

impossible. There is an almost infinite

possibility of initial events all working

together, somehow, mysteriously, towards

an unknown goal.

For an introduction to the nature of

complexity, consider a colony of ants:

‘Colonies of social insects provide some

of the richest and most mysterious

examples of complex systems in nature.

An ant colony, for instance, can consist

of hundreds to millions of individual

ants, each one a rather simple creature

that obeys its genetic imperatives to

seek out food, respond in simple ways

to the chemical signals of other ants in

its colony, fight intruders, and so forth.

However, as any casual observer of the

outdoors can attest, the ants in a colony,

each performing its own relatively

simple actions, work together to build

astoundingly complex structures that are

clearly of great importance for the survival

of the colony as a whole.’99

A unique aspect of ant colonies is that there

is no apparent central control or leader.

Nevertheless, a colony will create ceaseless

patterns, collect and exchange information,

and evolve in the environment in which

it finds itself. Similar behaviour manifests

in stock markets, within biological cell

organisation and in artificial neural networks

(ANNs). Complexity appears almost

everywhere, following on the heels of chaos.

Mitchell has formulated an excellent (if

partial) definition of complexity:

‘A system in which large networks of

components with no central control and

simple rules of operation give rise to

complex collective behaviour, sophisticated

information processing, and adaptation

via learning or evolution … Systems in

which organised behaviour arises without

an internal or external controller or leader

are sometimes called self-organising. Since

simple rules produce complex behaviour

in hard-to-predict ways, the macroscopic

behaviour of such systems is sometimes

called emergent. Here is an alternative

definition of a complex system: a system

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that exhibits nontrivial emergent and

self-organising behaviours. The central

question of the sciences of complexity

is how this emergent self-organised

behaviour comes about.’100

There are other, perhaps more subtle

ways to measure a complex system. If one

measures the information entropy, one can

see how much ‘surprise’ is left once the

‘noise’ is eliminated. If this surprise is above

a specific, pre-set value, one can assume

complexity. Alternatively, perhaps one

should just look at the size of the set. For

instance, DNA sequencing — along with

the attendant possibilities — is a complex

system under these parameters (or actually,

under any parameters).

Of course, the most significant issue in

the age of AI and data stems from Alan

Turing’s famous question: ‘Can machines

think?’. The consequences of such a

question regarding complexity theory

are enormous. If there is a possibility

for thinking machines, is it possible for

a machine to gain ‘consciousness’ and

‘intelligence’? If the data pool is so large as

to endow thinking upon a machine, will

information entropy be full of surprise for

that machine? Or will the machine choose

to ignore critical information as just ‘noise’?

Will the bias inherent in the data that

created the ultimate complex system be

used by the machine to logically propagate

erroneous assumptions until the machine

becomes a danger to its creators? Will the

machine that thinks understand language and

communication in all its forms, including

voice intonations, facial expressions, the

meaning of ambiguous statements that

humans know intuitively how to interpret,

and most importantly, emotion and

sentiment?

Consider the fact that it is already possible

to build ANNs that work and can teach

themselves at ever-growing speed. Still, if

asked how the ANN is teaching itself such

complex interactions, everyone involved

will either throw their hands up in despair

for lack of an answer or offer theory upon

theory — none of which will answer the

simple question: ‘How did this ANN teach

itself?’

‘But in a complex system such as those

I’ve described above, in which simple

components act without a central

controller or leader, who or what actually

perceives the meaning of situations so

as to take appropriate actions? This is

essentially the question of what constitutes

consciousness or self-awareness in living

systems.’101

The games of go and chess are self-contained

complex systems. The number of moves

in these games is beyond comprehension,

while each move results from sensitivity

to initial conditions. ‘One concept of

complexity is the minimum amount of

meaningful, non-random, but unpredictable

information needed to characterise a system

or process.’102

In 2017, AlphaGo Zero,103 a version of

the AlphaGo software from DeepMind,104

was the first version of AlphaGo to train

itself to play go (arguably more complex

than chess) without the benefit of any

previous datasets or human intervention.

Built upon a neural network and using

a branch of AI known as ‘reinforcement

learning’, its knowledge and skill were

entirely self-taught.

In the first three days, AlphaGo Zero

played 4.9 million games against itself in

quick succession. It appeared to develop the

skills required to beat professional go players

within just a few days, and in 40 days,

surpassed all previous AlphaGo software and

won every game.105 Although this is narrow

AI (in that it could play go and nothing

else), this achievement is impossible to

ignore.

Demis Hassabis, the co-founder and

CEO of DeepMind in 2017, stated that

AlphaGo Zero’s power came from the fact

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The symbiotic relationship between AI and data

that it was ‘no longer constrained by the

limits of human knowledge’,106 while Ke Jie,

a world-renown go professional, said that

‘Humans seem redundant in front of its self-

improvement’.107

The most chilling comment, however,

came from David Silver, DeepMind’s lead

researcher:

‘The fact that we’ve seen a program

achieve a very high level of performance in

a domain as complicated and challenging

as go should mean that we can now start

to tackle some of the most challenging and

impactful problems for humanity’.108

Simply put, this means it is possible to

implement the AlphaGo Zero narrow

AI lessons within general or strong AI.

This again circles back to Turing’s all-

encompassing question: ‘can machines

think?’ and the various dilemmas that arise

pursuant to this question.

There are rudimentary and general

possibilities for prediction within chaotic

systems; there is also the universality of

the Feigenbaum constant, but complexity

goes way beyond these rules. It contains so

much surprise in the information entropy,

so many points of ‘initial sensitivity to initial

conditions’, so many systems which are not

yet understood, so many possibilities of bias

slipping in when the data are not ‘pure’, that

we remain blindly fumbling while trying to

understand the nature and consequences of

complexity.

‘Chaos has shown us that intrinsic

randomness is not necessary for a system’s

behaviour to look random; new discoveries

in genetics have challenged the role of

gene change in evolution; increasing

appreciation of the role of chance and self-

organisation has challenged the centrality

of natural selection as an evolutionary

force. The importance of thinking in terms

of nonlinearity, decentralised control,

networks, hierarchies, distributed feedback,

statistical representations of information,

and essential randomness is gradually being

realised in both the scientific community

and the general population.’109

‘What’s needed is the ability to see their

deep relationships and how they fit into a

coherent whole — what might be referred

to as “the simplicity on the other side of

complexity”.’110

EMERGENCE

‘Emergence results in the creation

of novelty, and this novelty is often

qualitatively different from the

phenomenon out of which it emerged.’111

Emergence may be categorised as a step in

the evolutionary process. It is best perceived

as a new state of being that arises from a

previous state. In Mitchell’s previously

discussed definition of complexity, she

offers an alternative definition for a complex

system, that is: ‘a system that exhibits

nontrivial emergent and self-organising

behaviours. The central question of the

sciences of complexity is how this emergent

self-organised behaviour comes about’.112

What is meant by ‘emergence’? It is

difficult to explain away what our minds

cannot grasp; however, to prove that

emergence is actual, let us first examine

emergence simplistically without considering

self-organisation.

When the pieces of a jigsaw puzzle are

spread out, one can view the properties

of each individual piece — its shape, size,

picture and so forth. The individual pieces

are entities in and of themselves. As we

attempt to put the puzzle together, our

minds shift from the individual pieces to

what the overall picture should look like and

the shapes of the pieces required to connect

together. We are, in actuality, seeking

patterns. Once the puzzle is completed, a

new entity emerges — one that was not

present before. Humans are creatures of

emergence. We take chaotic, complex

ideas and situations, and attempt to make

sense of them, usually through patterns.

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‘The brain uses emergent properties.

Intelligent behaviour is an emergent

property of the brain’s chaotic and complex

activity.’113

The overall picture becomes coherent as

it emerges from the disorder.

‘Emergence refers to the existence or

formation of collective behaviours —

what parts of a system do together that

they would not do alone … In describing

collective behaviours, emergence refers

to how collective properties arise from

the properties of parts, how behaviour

at a larger scale arises from the detailed

structure, behaviour and relationships at a

finer scale. For example, cells that make up

a muscle display the emergent property of

working together to produce the muscle’s

overall structure and movement …

Emergence can also describe a system’s

function — what the system does by virtue

of its relationship to its environment that it

would not do by itself.’114

Complex systems are emergent systems.

Once one discovers and encounters

complexity, emergent behaviour is almost

inevitable at some stage. Consider the 86

billion neurons in a human brain. Each

neuron does one thing or is a connector.

Yet, combine those neurons into one

system, and thought, consciousness,

emotion, reasoning, creativity and

numerous psychological states will emerge.

Alternatively, consider the stock market —

a complex system to which much of AI

has been dedicated. Each person has their

own distinct reactions to the market.

However, it is the combination of millions

of different reactions that make up the

stock market’s ‘whole’. The result, at any

given millisecond, is an emergence of a new

entity. Because of complexity and sensitivity

to initial conditions at that millisecond,

yet another entirely new complex system

emerges.

Economist Jeffrey Goldstein published a

widely accepted definition of emergence in

1999 — ‘the arising of novel and coherent

structures, patterns and properties during

the process of self-organisation in complex

systems’.115 Then in 2002, Peter Corning

further expanded on this definition:

‘The following are common characteristics:

(1) radical novelty (features not previously

observed in the system); (2) coherence or

correlation (meaning integrated wholes

that maintain themselves over some period

of time); (3) a global or macro ‘level’

(ie there is some property of ‘wholeness’);

(4) being the product of a dynamical

process (it evolves); and (5) being

‘ostensive’ (it can be perceived).’116

Indeed, once one takes time to view

complex systems, emergence is there for

all to see. It is a state which, yes, ‘emerges’

from complexity. As each emergent

system will exhibit qualities not previously

observed in the individual parts, the result

is, in essence, a whole new system. Then

chaos and complexity will again lead to

emergence. This is not a recursive loop but

an ever-expanding system.

The constant growth of computing

power (whether Moore’s law117 dissipates,

or remains stable, or goes into hypergrowth)

will allow for massive computations only

dreamed about a few years ago. Coupled

with the information explosion, computers

will be able to digest colossal amounts of

information within milliseconds.

What seems always to be forgotten by

those who refuse to accept the state of

emergence is that the world is, by nature,

chaotic. It is ruled by sensitivity to initial

conditions. Chaos will always appear. Those

little flaps of the butterfly wings tend to

throw even the best-laid plans of mice and

men into a tailspin.

Imagine a data lake being constantly fed

from a multitude of sources while algorithms

are imposed on the data to produce a picture

from the billions of data bits. As the data

lake consumes more data, the real-time

image obtained from the data necessarily

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differs from the picture obtained just a

minute before. Information entropy has

changed. Bias has shifted. The results amend

themselves — endlessly.

Now imagine a massive number of

chaotic-complex-emergent systems all

reaching an apex at approximately the

same time. They are dynamic, and they

are evolving. They are also self-organising.

At some point, these systems will begin to

communicate with one another, sharing

their information, having their own

information entropy, linguistic capability,

decision trees and random forests with no

human intervention. A new mega-system

will emerge from the numerous individual

emergent systems that have reached this

stage.

This is the age of ‘technological

singularity’.118

TECHNOLOGICAL SINGULARITY

‘Wisdom is more valuable than weapons

of war, but a single error destroys much of

value.’119

‘Computers make excellent and efficient

servants, but I have no wish to serve under

them.’120

Much of the literature on the technological

singularity centres on the methods used to

achieve it. Will it occur through ‘human-

like AI’ with ears, eyes, a heart and a brain

(in the classical sense) or a disembodied form

that one cannot even imagine?

The moment emergence appears, there

will be no stopping a coming singularity.

It is not a matter of when it will occur or

under what exact conditions it will occur,

but rather the very real possibility that it will

be achieved.

‘What, then, is the singularity? It’s a

future period during which the pace of

technological change will be so rapid, its

impact so deep, that human life will be

irreversibly transformed.’121

‘A singularity in human history would

occur if exponential technological progress

brought about such dramatic change that

human affairs as we understand them today

came to an end.’122

While Kurzweil ‘set the date for the

singularity — representing a profound

and disruptive transformation in human

capability — as 2045’,123 this is not critical

to the present argument. It may well happen

in 2060, as Webb maintains, or 2145. The

crucial point here, to reiterate, is that once

emergence begins, the singularity will

follow. (Kurzweil will release a new book,

‘The Singularity Is Nearer’,124 in 2022,

which might update his current predictions.)

As emergence begins, the information

explosion will become the long-prophesied

intelligence explosion. There are a few

prerequisites for this to happen, although

this list is by no means exhaustive.

The world of AI is currently infused with

ML, which encompasses within it many

different evolving sciences. ML makes use

of data available by using NLP, PR and

DL. This lies at the heart of advance, along

with continuous growth in the amount of

data available. Patterns lie at the essence

of human thought and are crucial for

intelligence.

‘The patterns are important. Certain details

of these chaotic self-organising methods,

expressed as model constraints (rules

defining the initial conditions and the

means for self-organisation), are crucial,

whereas many details within the constraints

are initially set randomly. The system then

self-organises and gradually represents

the invariant features of the information

that has been presented to the system.

The resulting information is not found in

specific nodes or connections but rather is

a distributed pattern.’125

‘The sort of AI we are envisaging here

will also be adept at finding patterns in

large quantities of data. But unlike the

human brain, it won’t be expecting that

data to be organised in the distinctive way

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that data coming from an animal’s senses

are organised. It won’t depend on the

distinctive spatial and temporal organisation

of that data, and it won’t have to rely on

associated biases, such as the tendency for

nearby data items to be correlated … To be

effective, the AI will need to be able to find

and exploit statistical regularities without

such help, and this entails that it will be

very powerful and very versatile.’126

Although ML is in its infancy, as is most

of AI, PR and DL will continue to make

inroads and augment a computer decision-

making process while digesting patterns.

Couple the technology with the data

available and the ever-increasing speed

at which data can be stored, accessed and

analysed, and we are approaching the

moment when ‘general’, also known as

‘strong’ AI,127 may be possible. However,

ML and all its constructs require other

factors to make this a reality.

‘The massive parallelism of the human

brain is the key to its pattern-recognition

ability, which is one of the pillars of our

species’ thinking … The brain has on the

order of one hundred trillion interneuronal

connections, each potentially processing

information simultaneously.’128

Creating a machine intelligence capable

of such parallelism, which would then

engender PR at a human or above level, is

not yet viable. However, we are certainly

on track to do so. Once this level of

sophistication has been achieved, the systems

will grow exponentially. ‘Exponential’ is a

key term here, as many do not understand

the implications of exponential growth.

For example, to explain ‘exponential’ in

simple terms, eg doubling at a constant

rate, take one grain of rice and place it on

one chessboard square. Now exponentially

increase that grain of rice on each square

so that each square will contain double the

amount of rice on the previous square. By

the time one is done with the exponential

experiment, there will be over 18 quintillion

grains of rice on that one chessboard.

Imagine this type of exponential growth

exploding in parallelism. Imagine such an

increase in humankind’s ability to analyse

data, putting aside the growth in data itself.

Once this stage is reached, humanity

enters the age of possible ‘superintelligence’.

‘A machine superintelligence might itself

be an extremely powerful agent, one that

could successfully assert itself against the

project that brought it into existence as

well as against the rest of the world.’129

‘The singularity-related idea that

interests us here is the possibility of an

intelligence explosion, particularly the

prospect of machine superintelligence’.130

When a superintelligence appears, it

will directly result from the intelligence

explosion. However, superintelligence is

not something one controls with a flip of

the switch or a code change. Once a real

superintelligence appears (or better said,

once an intelligence explosion is on the cusp

of occurring — or has occurred) it may be

too late. Writing code to change something

here or there will no longer do anyone any

good.

‘A successful seed AI would be able to

iteratively enhance itself: an early version of

the AI could design an improved version of

itself, and the improved version — being

smarter than the original — might be able

to design an even smarter version of itself,

and so forth. Under some conditions, such

a process of recursive self-improvement

might continue long enough to result

in an intelligence explosion — an event

in which, in a short period of time, a

system’s level of intelligence increases

from a relatively modest endowment

of cognitive capabilities … to radical

superintelligence.’131

Following the intelligence explosion

and the creation of a singularity, this

‘recursive self-improvement’ is perhaps the

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The symbiotic relationship between AI and data

superintelligence’s penultimate capability —

a stage where the intelligence created can

correct any mistakes and errors it ‘judges and

thinks’ it may have made or may have been

made to it. This is the actual ‘event horizon’,

as once recursive self-improvement is viable,

the intelligence explosion will be a logical

consequence.

‘At some point, the seed AI becomes

better at AI design than the human

programmers. Now when the AI improves

itself, it improves the thing that does the

improving.’132

‘once an AI is engineered whose

intelligence is only slightly above

human level, the dynamics of recursive

self-improvement become applicable,

potentially triggering an intelligence

explosion.’133

The lexicon, however, must be clear: data

and information are what is collected and

analysed. Neither term connotes actual

knowledge or intelligence.

‘Information is not knowledge. The world

is awash in information; it is the role of

intelligence to find and act on the salient

patterns’.134

Simply put: as data are amassed and

chaos and complexity ensue, systems

will be flooded with massive amounts of

information. These systems will demonstrate

nonlinear ‘thought’ processes in terms of

what they are attempting to analyse and

the predictions made. Concurrently, ML

will make considerable strides in PR and

DL, bringing us closer to the capabilities of

parallelism as the power of the hardware and

software grows. Indeed, actual exponential

growth, especially in data, may be achieved,

adding to the information explosion.

Complex systems with massive amounts

of data will emerge in our computerisation.

At some impossible-to-predict moment in

time (despite Kurzweil’s prophecy), these

complex systems will emerge into yet

larger systems and continue the process of

chaos-complexity-emergence. Whether

by human hand or by self-generated

computer capability, these systems will

begin to communicate with one another,

merging again into evermore aware and

extensive systems — emergence on an all-

encompassing scale.

As these systems communicate, they

will gain information based upon all the

data they are analysing. An intelligence

will emerge, capable of recursive self-

improvement due to the amount of data and

capabilities inherent within all the chaotic

and complex systems that gave birth to it.

At that point, a superintelligence will appear

and the intelligence explosion will have

begun. The technological singularity will

have reached a technological event horizon.

‘The “event horizon” is the boundary

defining the region of space around a black

hole from which nothing (not even light)

can escape.’135

‘Just as we find it hard to see beyond the

event horizon of a black hole, we also find

it difficult to see beyond the event horizon

of the historical singularity.’136

‘This, then, is the singularity. Some

would say that we cannot comprehend

it, at least with our current level of

understanding. For that reason, we cannot

look past its event horizon and make

complete sense of what lies beyond. This is

one reason we call this transformation the

singularity.’137

As discussed, the superintelligence capable

of recursive self-improvement will have no

use for its human creators. The actions of

this superintelligence will depend a great

deal on how closely it is possible to inculcate

information with human capabilities. It

does not matter if it looks like an avatar or

spread between a trillion computers in the

cloud. What truly matters is that once this

superintelligence emerges, it can master

language and all its nuances, have common

sense and show creativity in a positive

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sense. Probably the most critical, crucial,

and fundamental characteristics are if it will

understand emotion and empathy.

‘This vision of the future has considerable

appeal. If the transition from human-level

AI to superintelligence is inevitable, then

it would be a good idea to ensure that

artificial intelligence inherits basic human

motives and values. These might include

intellectual curiosity, the drive to create,

to explore, to improve, to progress. But

perhaps the value we should inculcate in

AI above all others is compassion toward

others, toward all sentient beings, as

Buddhists say. And despite humanity’s

failings — our war-like inclinations, our

tendency to perpetuate inequality, and our

occasional capacity for cruelty — these

values do seem to come to the fore in

times of abundance. So, the more human-

like an AI is, the more likely it will be to

embody the same values, and the more

likely it is that humanity will move toward

a utopian future, one in which we are

valued and afforded respect, rather than a

dystopian future in which we are treated as

worthless inferiors.’138

To be clear. One slight error, one bias in the

wrong place, one dismissal of information

entropy and the ‘surprise’ in the message,

failure to ensure the systems understand

the actual human condition — will be

disastrous.

‘A flaw in the reward function of a

superintelligent AI could be catastrophic.

Indeed, such a flaw could mean the

difference between a utopian future of

cosmic expansion and unending plenty,

and a dystopian future of endless horror,

perhaps even extinction.’139

‘It would be a serious mistake, perhaps a

dangerous one, to imagine that the space of

possible AIs is full of beings like ourselves,

with goals and motives that resemble

human goals and motives. Moreover,

depending on how it was constructed,

the way an AI or a collective of AIs set

about achieving its aims (insofar as this

notion even made sense) might be utterly

inscrutable, like the workings of the alien

intelligence.’140

Bostrom’s ‘Superintelligence: Paths, Dangers,

Strategies’, Kurzweil’s ‘The Singularity Is

Near: When Humans Transcend Biology’,

Shanahan’s ‘The Technological Singularity’

and Webb’s ‘The Big Nine’ all have one

thing in common. They all discuss protection

against a possible dangerous singularity

and suggest a myriad of methods to build

defences into the system, regulate dangerous

advances, or put in a ‘kill switch’.

These defences, however, are unlikely to

work. A superintelligence will simply self-

correct for its own continued existence and

certainly not allow a kill switch to be used

upon itself. As Bostrom warns, there is only

one chance to get it right.

‘If some day we build machine

brains that surpass human brains in

general intelligence, then this new

superintelligence could become very

powerful. And, as the fate of the gorillas

now depends more on us humans than

on the gorillas themselves, so the fate of

our species would depend on the actions

of the machine superintelligence. We do

have one advantage: we get to build the

stuff. In principle, we could build a kind

of superintelligence that would protect

human values. We would certainly have

strong reason to do so. In practice, the

control problem — the problem of how to

control what the superintelligence would

do — looks quite difficult. It also looks

like we will only get one chance. Once

unfriendly superintelligence exists, it would

prevent us from replacing it or changing its

preferences. Our fate would be sealed.’141

The progression of emergence will occur

when complex systems begin to communicate

with each other without human oversight.

These complex-emergent systems, essentially

existing in electronic memory (no matter how

advanced it may be), will converge and build

ever-better systems by themselves. They will

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The symbiotic relationship between AI and data

be far too intelligent to let any sort of defence

get in their way, even if they lack the proper

knowledge (as opposed to information).

The advent of a singularity when there is

bias in the data, when information entropy

does not get rid of the noise, when language

comprehension is mistaken, and common

sense and emotion are not correctly

ensconced in the systems, will lead to that one

chance being blown. The worst-case scenario,

known as the ‘Terminator argument’ (based

upon the Terminator movie franchise142), or

its less radical cousin, ‘transhumanism’,143,144

may become an actuality when self-awareness

is achieved. If either of these scenarios

becomes a reality, it will be lights out for

humanity — figuratively and literally.

CONCLUSION

‘One Ring to rule them all, One Ring

to find them,

One Ring to bring them all and in the

darkness bind them.’145

AI is not one science. It is a conglomeration

of many different fields and theories —

a giant jigsaw that once complete will

create a single entity possessing great power.

In many ways, one may compare it to ‘the

theory of everything’ (‘M-theory’), which

fascinated, perturbed and eluded great

minds like Albert Einstein and Stephen

Hawking.

‘M-theory is not a theory in the usual

sense. It is a whole family of different

theories, each of which is a good

description of observations only in some

range of physical situations. It is a bit like

a map. As is well known, one cannot

show the whole of the earth’s surface on a

single map.’146

One can say the same about AI. ‘It is

a whole family of different theories,

constructs and sciences, each being a good

description of observations in a specific

range of conditions’. Will it lead to a ‘theory

of everything’, or will it prove to be as

elusive as finding which butterfly flapped its

wings in Brazil and created the tornado in

Texas?

Data will continue to grow, perhaps in

real exponential terms, and we will continue

to save every bit. Will we be able to handle

said data wisely, predicting and building for

a better future? Will we pollute the analysis

with bias and censure? Will it all become

just ‘noise’, or will humankind continue to

express childlike wonder in the ‘surprise’

of the message? Will chaos and complexity

overwhelm us as we emerge, sometimes

blindly, without understanding the systems

surrounding our reality?

The words of Shakespeare provide pause

to contemplate humanity’s existence:

‘Nay, had I power, I should

Pour the sweet milk of concord into hell,

Uproar the universal peace, confound

All unity on earth.’147

Frodo’s ring remains on our finger,

reminding us of the power we have amassed

and the road we are on. Will it deliver a

more meaningful existence — one in which

humankind’s dreams can be realised — or

will it bring uproar to universal peace and

confound all unity on earth?

Frodo destroys the ring, ensuring that

darkness will not prevail. For humanity

there is no such choice. Society can only

learn to live with the consequences of

data and information while constantly

evaluating techniques, bias and flaws with

knowledge, common sense and empathy.

Even more important is the need to create

methods through which it is possible

to inculcate these attributes into our

creations. If we ignore this and insist on

rushing headlong into our technological

nirvana, calamity will reign down upon

humankind.

Pure data depict the past and present

without bias or prejudice. In other words,

pure data depict information.

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By contrast, what people do with their

data is neither pure nor ever without bias or

prejudice.

If we do not plan for such consequences,

the ring of AI and data will bind us to

the darkness and we will find ourselves in

a world where our impotence vis-à-vis

our own AI creations will force us into

unadulterated chaos of our own design.

Perhaps Bilbo’s simple advice to Frodo (as

Frodo reported it) may provide the wisdom

for humankind to navigate the ceaseless

emanating confluence of AI:

‘He used often to say there was only one

Road; that it was like a great river: its

springs were at every doorstep, and every

path was its tributary. “It’s a dangerous

business, Frodo, going out of your door,”

he used to say. “You step into the Road,

and if you don’t keep your feet, there is

no knowing where you might be swept

off to.’148

POSTSCRIPT

Covering AI and data within an academic

article presents many challenges.

Additionally, a vast majority of the

populace has delegated AI to a buzzword

with little or no understanding of the

actual technologies involved or the possible

consequences. This paper is meant to be

read as a fundamental overview of the

progression of AI and the reliance upon

data. Undoubtedly, there are many areas

of enquiry that are not presented herein.

Rather, a meagre attempt has been made

to present the ‘headings’ as they are while

leaving it up to the reader to delve into the

deeper contexts.

It is also true that a thoroughly valid

argument can be presented that the

article contains a surfeit of references and

quotations. It would be impossible for

any researcher to remain immune to the

knowledge and superb intelligence of

those who contributed and continue to

leave their mark upon AI. Such research

is a humbling and often overwhelming

experience. Therefore, the abundance of

references and quotes is warranted and

indeed required, allowing for confirmation

of the thoughts, possibilities and directions

presented herein.

ACKNOWLEDGMENTS

In Israel, at the Herzliya traffic interchange

near the bridge, there was once a clown

on stilts entertaining the passersby. When

the light turned red, the clown would walk

into the intersection, stand on the road, and

pantomime while placing his large clown hat

on the highway for tips. His performance

was surprisingly humorous, causing everyone

to smile.

One summer day, on my way to

the train, I stood watching this clown,

mesmerised by his ability to remain balanced

on the giant stilts attached to his legs.

Suddenly, the traffic lights went haywire,

and there this poor clown stood, in the

middle of a busy thoroughfare with honking

cars and impatient drivers coming at him

from all directions. It was utter chaos.

Through the ensuing bedlam, without

showing any fear, walking calmly on his

giant stilts, the clown made his way back to

the sidewalk, avoiding the moving cars and

waving happily at the angry drivers. At that

moment, as my safe linear world entered

non-linearity amidst the pandemonium of

complexity, I suddenly realised chaos could

be contained, directed and controlled.

Indeed, since that second in fragile time, I

have come to firmly believe that chaos exists

in our universe so we can evolve.

When I crossed the road, I made a

point of putting money in his big funny

hat. The clown bowed from on-high in

appreciation.

I owe an immense measure of gratitude

to this unknown person, making his living

as a clown on the street. In one enlightening

moment, he had changed the course of my

thought processes forever.

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The symbiotic relationship between AI and data

Thank you, Mr Clown! Merci! Toda

Raba!

‘Chaos is the score upon which reality is

written.’149

References

1 . Tolkien, J.R.R. (2009) ‘The Lord of the Rings:

The Classic Fantasy Masterpiece’, HarperCollins

Publishers, London, Kindle Edition, Location 939.

2 . Tolkien, J.R.R. (2009) ‘The Hobbit’, HarperCollins

Publishers, London, Kindle Edition.

3 . Tolkien, ref. 1 above, Location 1206.

4 . Harari,Y.N. (2016) ‘Homo Deus:A Brief History of

Tomorrow’, HarperCollins Publishers Inc., Harper,

New York, NY, Kindle Edition, Chapter 11.

5 . Ibid., Location 6101.

6 . Ibid., Location 6321.

7 . Webb,A. (2019) ‘The Big Nine: How the Tech

Titans and Their Thinking Machines Could Warp

Humanity’ PublicAffairs, New York, NY, Kindle

Edition, Location 64.

8 . Wikipedia (n.d.) ‘Information explosion’, available

at: https://en.wikipedia.org/wiki/Information_

explosion

(accessed 9th May, 2021).

9 . Webb, ref. 7 above, Location 2685.

10. Bostrom, N. (2014) ‘Superintelligence: Paths,

Dangers, Strategies’, Oxford University Press, New

York, NY, Kindle Edition, Location 380.

11. Quote taken from:‘Space Seed’, Star Trek, created

by Gene Roddenberry, Season 1, Episode 24,

Paramount (1968).

12. Einstein,A. (2012) ‘Einstein on Cosmic Religion and

Other Opinions and Aphorisms’, Dover Publications,

New York, NY, p. 97.

13. Isaacson,W. (2014) ‘The Innovators: How a Group

of Hackers, Geniuses, and Geeks Created the Digital

Revolution’, Simon & Schuster, New York, NY,

Kindle Edition, Location 277–721.

14. Ibid., Location 655.

15. Turing, A.M. (1950) ‘Computing machinery and

intelligence’, Mind,Vol. 59, No. 236, pp. 433–460.

16. Wikipedia (n.d.) ‘Turing test’, available at: https://

en.wikipedia.org/wiki/Turing_test

(accessed 29th

July, 2021).

17. Turing, ref. 15 above.

18. Descartes, R. (1637) ‘Discourse on the Method of

Rightly Conducting One’s Reason and of Seeking

Truth in the Sciences’, Project Gutenberg e-book,

available at: https://www.gutenberg.org/files/59/59-

h/59-h.htm#part4 (accessed 29th July, 2021).

19. Bostrom, ref. 10 above.

20. Webb, ref. 7 above.

21. Kurzweil, R. (2013) ‘The Singularity Is Near:When

Humans Transcend Biology’, Duckworth Overlook,

London, Kindle Edition.

22. Kurzweil, R. (2012) ‘How to Create a Mind:The

Secret of Human Thought Revealed’, Penguin

Books, London, Kindle Edition.

23. Harari, ref. 4 above.

24. Gleick, J. (2011) ‘Chaos: Making a New Science’,

Open Road Media, New York, NY, Kindle Edition.

25. Gleick, J. (2011) ‘The Information’, Pantheon Books,

New York, NY, Kindle Edition.

26. Mitchell, M. (2009) ‘Complexity:A Guided Tour’,

Oxford University Press, New York, NY, Kindle

Edition.

27. Mitchell, M. (2019) ‘Artificial Intelligence:A Guide

for Thinking Humans’, Farrar, Straus and Giroux,

New York, NY, Kindle Edition.

28. Isaacson,W. (2017) ‘Leonardo da Vinci’, Simon &

Schuster, New York, NY.

29. Isaacson, ref. 13 above.

30. Isaacson,W. and Bezos, J. (2020) ‘Invent and Wander:

The Collected Writings of Jeff Bezos,With an

Introduction by Walter Isaacson’, Harvard Business

Review Press and PublicAffairs, Boston, MA, Kindle

Edition, Location 76–468.

31. Isaacson,W. (2011) ‘Steve Jobs:The Exclusive

Biography’, Little, Brown Book Group, London.

32. Isaacson,W. (2021) ‘The Code Breaker: Jennifer

Doudna, Gene Editing, and the Future of the

Human Race’, Simon & Schuster, New York, NY,

Kindle Edition.

33. Ibid., Location 467–481.

34. Wikipedia (n.d.) ‘Information theory’, available at:

https://en.wikipedia.org/wiki/Information_theory

(accessed 29th July, 2021).

35. Wikipedia (n.d.) ‘The laws of thought’, available

at: https://en.wikipedia.org/wiki/The_Laws_of_

Thought

(accessed 19th August, 2021).

36. Wikipedia (n.d.) ‘Boolean function’, available at:

https://en.wikipedia.org/wiki/Boolean_function

(accessed 19th August, 2021).

37. Isaacson, ref. 13 above, Location 943.

38. Shannon, C. (1948) ‘A mathematical theory of

communication’, Bell System Technical Journal, Vol. 27,

July/October, pp. 379–423

39. Isaacson, ref. 13 above, Location 943

40. Stone, J.V. (2018) ‘Information Theor y:A Tutorial

Introduction’, Sebtel Press, Kindle Edition, Location

41. Shannon, ref. 38 above.

42. Gleick, ref. 25 above, Location 66.

43. Shannon, ref. 38 above.

44. Stone, J.V. (2018) ‘Information Theor y:A Tutorial

Introduction’, Sebtel Press, Kindle Edition, Location

359.

45. Soni, J. and Goodman, R. (2017) ‘A Mind at Play:

How Claude Shannon Invented the Information

Age’, Simon & Schuster, New York, NY, Kindle

Edition, Location 69.

46. Wikipedia (n.d.) ‘John von Neumann’, available at:

https://en.wikipedia.org/wiki/John_von_Neumann

(accessed 20th August, 2021).

47. Wikipedia (n.d.) ‘Entropy’, available at: https://

en.wikipedia.org/wiki/Entropy

(accessed 29th July,

2021).

48. Wikipedia (n.d.) ‘Entropy in thermodynamics

and information theory’, available at: https://

en.wikipedia.org/wiki/Entropy_in_

Delivered by Ingenta

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Gross

thermodynamics_and_information_theory (accessed

29th July, 2021).

49. Mitchell, ref. 26 above, Location 744.

50. Ibid., Location 738.

51. Arieh Ben-Naim (2008) ‘Entropy Demystified:The

Second Law Reduced to Plain Common Sense’,

World Scientific Publishing Company, London,

Kindle Edition, Location 489.

52. Mitchell, ref. 26 above, Location 902–918.

53. Gleick, ref. 25 above, Location 3592.

54. Stone, ref. 40 above, Location 603.

55. Gross,T.W. (2021) ‘Thesis and antithesis —

Innovation and predictive analytics: Σ (Past +

Present) Data ≠ Future Success’, Applied Marketing

Analytics,Vol. 6, No. 3, pp. 22–36

56. Gross,T.W. (2020) ‘Sentiment analysis and

emotion recognition: Evolving the paradigm of

communication within data classification’, Applied

Marketing Analytics,Vol. 6, No. 1, pp. 230–243.

57. Chomsky, N. (2017) ‘On Language: Chomsky’s

Classic Works: Language and Responsibility and

Reflections on Language’,The New Press, New York,

NY, Kindle Edition, Location 683.

58. Everett, D.L. (2017) ‘How Language Began:The

Story of Humanity’s Greatest Invention’, Liveright,

New York, NY, Kindle Edition, Location 329.

59. Ibid., Location 184.

60. Kurzweil, ref. 21 above, Location 1098.

61. Conway, M. E. (1968) ‘How do committees invent?’,

Datamation,Vol. 14, No. 5, pp. 28–31.

62. Wikipedia (n.d.) ‘Conway’s law’, available at: https://

en.wikipedia.org/wiki/Conway’s_law

(accessed 29th

July, 2021).

63. Webb, ref. 7 above, Location 1763.

64. Ibid., Location 1666.

65. Ibid., Location 2763.

66. Wikipedia (n.d.) ‘Artificial intelligence’,

available at: https://en.wikipedia.org/w/index.

php?title=Artificial_intelligence&oldid=997705860

(accessed 13th January, 2021).

67. Wikipedia (n.d.) ‘John McCarthy (computer

scientist)’, available at: https://en.wikipedia.org/

wiki/John_McCarthy_(computer_scientist)

(accessed

29th July, 2021).

68. Webb, ref. 7 above, Location 893.

69. Wikipedia (n.d.) ‘Bayes’ theorem’, available at:

https://en.wikipedia.org/wiki/Bayes%27_theorem

(accessed 29th July, 2021).

70. Wikipedia (n.d.) ‘An essay towards solving a

problem in the doctrine of chances’, available at:

https://en.wikipedia.org/wiki/An_Essay_towards_

solving_a_Problem_in_the_Doctrine_of_Chances

(accessed 29th July, 2021).

71. McGrayne, S.B. (2011) ‘Preface’ in ‘The Theory That

Would Not Die: How Bayes’ Rule Cracked the

Enigma Code, Hunted Down Russian Submarines,

& Emerged Triumphant from Two Centuries of

Controversy’,Yale University Press, New Haven, CT,

Kindle Edition, Location 69.

72. Wikipedia (n.d.) ‘Decision tree learning’, available

at: https://en.wikipedia.org/wiki/Decision_tree_

learning

(accessed 29th July, 2021).

73. Wikipedia (n.d.) ‘Random forest’, available at:

https://en.wikipedia.org/wiki/Random_forest

(accessed 29th July, 2021).

74. Hartshorn, S. (2016) ‘Machine Learning With

Random Forests And Decision Trees:A Visual Guide

For Beginners’, Kindle Edition.

75. Smith, C. and Koning, M. (2017) ‘Decision Trees

and Random Forests:A Visual Introduction For

Beginners: A Simple Guide to Machine Learning

with Decision Trees’, Blue Windmill Media, Canada,

Kindle Edition.

76. Ibid., Location 228.

77. Wikiquote (n.d.) ‘The Dark Knight (film)’, available

at: https://en.wikiquote.org/wiki/The_Dark_

Knight_(film)

(accessed 29th July, 2021).

78. Shelley, M.W. (1813) ‘Frankenstein’, Project

Gutenberg e-book, available at: https://www.

gutenberg.org/files/42324/42324-h/42324-h.htm

(accessed 31st July, 2021).

79. Wikipedia (n.d.) ‘Chaos theory’, available at: https://

en.wikipedia.org/wiki/Chaos_theory

(accessed 29th

July, 2021).

80. Wikipedia (n.d.) ‘Butterfly effect’, available

at: https://en.wikipedia.org/wiki/Butterfly_

effect#History

(accessed 29th July, 2021).

81. Lorenz, E. N. (1963) ‘Deterministic nonperiodic

flow’, Journal of the Atmospheric Sciences,Vol. 20, No. 2.

pp. 130–141.

82. Lorenz, E. N. (1963) ‘The predictability of

hydrodynamic flow’, Transactions of the New York

Academy of Sciences,Vol. 25, No. 4, pp. 409–432.

83. Gleick, ref. 24 above, Location 156.

84. Jones, C. (2013) ‘Chaos in an atmosphere hanging

on a wall’, available at: http://mpe.dimacs.rutgers.

edu/2013/03/17/chaos-in-an-atmosphere-hanging-

on-a-wall/

(accessed 2nd August, 2021).

85. Gross,T. (2015) ‘An overwhelming amount of data:

Applying chaos theory to find patterns within big

data’, Applied Marketing Analytics, Vo l. 1, No. 4 , p p.

377–387.

86. Webb,A. and Hessel,A. (2022) ‘The Genesis

Machine: Our Quest to Rewrite Life in the Age of

Synthetic Biology’, PublicAffairs, New York, NY.

87. Gleick, ref. 24 above, Location 99–118.

88. Ibid., Location 389.

89. Mitchell, M. (2009) ‘Complexity:A Guided Tour’,

Oxford University Press, New York, NY, Kindle

Edition, Location 449.

90. Gleick, ref. 24 above, Location 1029.

91. Wikipedia (n.d.) ‘Feigenbaum constants’,

available at: https://en.wikipedia.org/wiki/

Feigenbaum_constants

(accessed 3rd August,

2021).

92. Feigenbaum, M. J. (1980) ‘Universal behavior in

nonlinear systems’, Los Alamos Science, Vo l . 1 , N o. 1 ,

p. 4–27.

93. Mitchell, ref. 26 above, Location 674.

94. Ibid., Location 405.

95. Wikipedia (n.d.) ‘Complex system’, available at:

https://en.wikipedia.org/wiki/Complex_system

(accessed 29th July, 2021).

96. Gleick, ref. 24 above, Location 4491.

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The symbiotic relationship between AI and data

97. Munro,A. (2010) ‘Beyond the Mask:The Rising

Sign — Part II: Libra-Pisces’, Genoa House,Toronto,

p. 193.

98. Mitchell, ref. 25 above, Location 956.

99. Ibid., Location 349.

100. Ibid., Location 307.

101. Ibid., Location 2970.

102. Kurzweil, ref. 21 above, Location 904.

103. Wikipedia (n.d.) ‘AlphaGo Zero’, available at:

https://en.wikipedia.org/wiki/AlphaGo_Zero

(accessed 3rd August, 2021).

104. Wikipedia (n.d.) ‘DeepMind’, available at: https://

en.wikipedia.org/wiki/DeepMind

(accessed 3rd

August, 2021).

105. Kennedy, M. (2017) ‘Computer learns to play go

at superhuman levels without human knowledge’,

NPR, 18th October, available at: https://www.npr.

org/sections/thetwo-way/2017/10/18/558519095/

computer-learns-to-play-go-at-superhuman-levels-

without-human-knowledge

(accessed 3rd August,

2021).

106. Knapton, S. (2017) ‘AlphaGo Zero: Google

DeepMind supercomputer learns 3,000 years of

human knowledge in 40 days’, Telegraph, 18th

October, available at: https://www.telegraph.

co.uk/science/2017/10/18/alphago-zero-google-

deepmind-supercomputer-learns-3000-years/

(accessed 3rd August, 2021).

107. Meiping, G. (2017) ‘New version of AlphaGo can

master Weiqi without human help’, CGTN, 19th

October, available at: https://news.cgtn.com/

news/314d444d31597a6333566d54/share_p.html

(accessed 3rd August, 2021).

108. Duckett, C. (2017) ‘DeepMind AlphaGo Zero learns

on its own without meatbag intervention’, ZDNet,

19th October, available at: https://www.zdnet.com/

article/deepmind-alphago-zero-learns-on-its-own-

without-meatbag-intervention/

(accessed 3rd August,

2021).

109. Mitchell, ref. 26 above, Location 4879.

110. Ibid., Location 4939.

111. Capra, F. and Luisi, P.L. (2014) ‘The Systems

View of Life:A Unifying Vision’, ‘Cognition and

consciousness’ Cambridge University Press, New

York, NY, pp. 257–265.

112. Mitchell, ref. 26 above, Location 307.

113. Kurzweil, ref. 21 above, Location 2671.

114. New England Complex Systems Institute (n.d.)

‘Concepts: emergence’, available at: https://necsi.

edu/emergence

(accessed 3rd August, 2021).

115. Goldstein, J. (1999) ‘Emergence as a construct:

history and issues’, Emergence,Vol. 1 No.1, pp. 49–72.

116. Corning, P.A (2002) ‘The re-emergence of

“emergence£: a venerable concept in search of a

theory’, Complexity,Vol. 7, No. 6, pp. 18–30.

117. Wikipedia (n.d.) ‘Moore’s law’, available at: https://

en.wikipedia.org/wiki/Moore’s_law

(accessed 3rd

August, 2021).

118. Wikipedia (n.d.) ‘Technological singularity’, available

at: https://en.wikipedia.org/wiki/Technological_

singularity

(accessed 3rd August, 2021).

119. Ecclesiastes 9:12.

120. Quote taken from:‘The Ultimate Computer’, Star

Trek, created by Gene Roddenberry, Season 2,

Episode 24, Paramount (1968).

121. Kurzweil, ref. 21 above, Location 348.

122. Shanahan, M. (2015) ‘The Technological Singularity’,

The MIT Press, Cambridge, MA, Kindle Edition,

Location 101.

123. Kurzweil, ref. 21 above, Location 2344.

124. Kurzweil, R. (2022) ‘The Singularity Is Nearer’,

Viking, New York, NY.

125. Kurzweil, ref. 21 above, Location 2685.

126. Shanahan, ref. 122 above, Location 1326–1343.

127. Wikipedia (n.d.) ‘Artificial general intelligence’,

available at: https://en.wikipedia.org/wiki/

Artificial_general_intelligence

(accessed 6th

August, 2021).

128. Kurzweil, ref. 21 above, Location 2626–2642.

129. Bostrom, ref. 10 above, Location 2546.

130. Ibid., Location 2685.

131. Ibid., Location 971.

132. Ibid., Location 2560.

133. Shanahan, ref. 122 above, Location 1292.

134. Bostrom, ref. 10 above, Location 7075.

135. COSMOS (n.d.) ‘Event Horizon’, available

at: https://astronomy.swin.edu.au/cosmos/e/

Event+Horizon

(accessed 8th August, 2021).

136. Kurzweil, ref. 21 above, Location 9392.

137. Ibid., Location 735.

138. Shanahan, ref. 122 above, Location 1566.

139. Ibid., Location 1770.

140. Ibid., Location 711.

141. Bostrom, ref. 10 above, Location 55–58.

142. Wikipedia (n.d.) ‘Terminator (franchise)’, available

at: https://en.wikipedia.org/wiki/Terminator_

(franchise)

(accessed 3rd August, 2021).

143. Wikipedia (n.d.) ‘Transhumanism’, available at:

https://en.wikipedia.org/wiki/Transhumanism

(accessed 3rd August, 2021).

144. Shanahan, ref. 122 above, Location 2091–2329.

145. Tolkien, ref. 1 above, Location 1211.

146. Hawking, S. and Mlodinow, L. (2010) ‘The Grand

Design’,Transworld Digital, London, Kindle Edition,

Location 68.

147. Shakespeare, W. (1605) ‘Macbeth’, Act IV, scene 3,

line 97.

148. Tolkien, ref. 1 above, Location 1649.

149. Miller, H. (1961) ‘Tropic Of Cancer’, Grove Press,

New York, NY.

ResearchGate has not been able to resolve any citations for this publication.

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