The Relentless Attack of the Contradiction

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The Relentless Attack of the Contradiction 

• Is the information network a secure and managed
  environment or a lifelike, chaotic flow of
  instability?

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Knowledge-meaning gtgtgtgtgtgtgtgtInformation-uncertainty

• We live in a world where there is more and more
  information, and less and less meaning.
  Jean Baudrillard, 1981 p. 79

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Themes of lecture 2

• What is information?
• 4 ways of thinking about information
• Information as a commodity
• Shannons bits
• Crick and Watson - the information of life
• Zellingers Quantum Principle

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Themes of lecture

• The certainties of information
• The whereabouts of matter
• DNA babies
• Information overload an illness that can be
  cured?

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Themes of lecture

• The uncertainties of information
• Logical contradictions lairs and viruses
• Panic computing
• Lifelike, complex networks versus cybercontrol

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4 ways of thinking about information
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information as a commodityinformation meaning

• Market Criteria (See Webster on Schiller, 1995 pp
  75-100)
• Sold for a profit
• Consumption of information
• Leads to class inequalities or new utopias info
  poor/info rich
• Dominated by corporate capitalism

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Rejection of information minus meaning

• The Political Economy of Information (see Mosco
  Wasko, 1988 and Webster, 1995)
• Rejects cybernetics for its tendency
• To operationalize the (social) system in
  mechanistic terms (see Schiller, D in Mosco and
  Wasko (eds), 1998 p. 29)
• To exclude the issue of meaning from debate
  (Webster, 1995 p. 27).

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Shannons bits certain about uncertainty 1948
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Information, meaning and uncertainty

• Nothing to do with meaning
• Information and uncertainty find themselves to be
  partners From Warren
  Weavers Introduction toClaude Shannons The
  Mathematical Theory of Communication1949

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Basis of information theory

• An engineering model designed to reduce noise
  (uncertainty)
• Information can be measured in bits
• A bit amount of information
• reduces the number of choices by half
• yes/no 1/0

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Additional elements are the interesting ones

• Shannon explored the information space like a
  physicist would explore the molecular spaces of
  liquids, gases and solids

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Second Law of Thermodynamics

• (Thermodynamics - the movement of heat)
• Saverys mine pump (1698) to Stephensons steam
  railway (1825)
• Carnot (1824) Reflections on the Motive Power of
  Heat and on Machines Appropriate for Developing
  this Power.
• Clausius (1850)
• Entropy - defined the limits of the efficiency of
  the ideal heat engine

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Entropy

• The energy of the world is constant the entropy
  of the world tends towards a maximum (Clausius,
  1850)
• Things have a tendency to mix, become uncertain!
• Hot and Cold
• Gas Molecules
• The Universe
• Civilisation
• Childs Bedroom
• Information?

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Information turnbefore Shannon

• Maxwell (1831-1879)
• Boltzmann (1877)
• Used statistics
• Probability
• To measure heat loss
• Suicide in 1906

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Entropy Degree of organization- measure of
certainty - high or low entropy
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Maxwells demon holds back time
Maxwells gas molecules Low entropic,
nonequilibrium, unmixed state Improbable 0-20,
1-19, 2-18 distribution High entropic,
equilibrial, mixed probable state
9-11, 10-10, 11-9 distribution becomes more
likely
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Certainty entropy low Message will be
predictable/certain Non-equilibrium
Uncertainty entropy high All bits of
information are present and equally
possible Equilibrium
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• Noise increases uncertainty
• Information increases uncertainty

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Shannons solution

• Use code to correct the errors caused by noise
• Redundancy added to code
• Ensures that an element of sameness is mixed in
  with the change (Campbell p. 201)

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Profound conclusions of entropy

• Entropy
• The arrow of time - irreversible time
• Heat death gtgtgtgtgtgtgtgtgt
• Childs bedroom will never get tidy
• Negative Entropy (negentropy)
• The world, the universe, life are open systems
• Shannons information codes negentropy
• The improbability of life negentropy
• DNA negentropy

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Crick and Watson the information of
life1951-53
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2 April 1953MOLECULAR STRUCTURE OF NUCLEIC ACIDS
A Structure for Deoxyribose Nucleic AcidWe
wish to suggest a structure for the salt of
deoxyribose nucleic acid (D.N.A.). This structure
has novel features which are of considerable
biological interest(Nature Magazine VOL 171,
page737, 1953)
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Passes genetic information from one generation
to the next The building blocks of life The
blueprint for form??? Problem with how this
lot becomes form
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Zellingers Quantum Principle
an elementary system carries one bit of
information
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Zellingers Quantum Principle

• Finding the position of a structureless particle
  (like an electron) does not seem like a yes/no
  question
• But it is,
• What is the probability of its position?

• Detectors count its arrival at a certain
  position.
• Yes, yes, yes, yes, no, yes, no, no, no yes
• It is vertical for 60 of the time

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Information/Meaning

• Information has nothing to do with meaning
  (Weaver, 1949)

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The really big questions

• What makes meaning?
• How was the Universe created?
• What is the cause?
• What is the essence of life?
• Replaced by

• How does a stone fall?
• How does water flow in a tube?
• What is the probability?
• What is the probable position of matter?

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Information/Meaning

• The search for meaning (lecture 1)
• plunges straight into the abyss of metaphysical
  inquiry (Hans Christian von Baeyer, 2003 p.
  29-30)
• Shannon information and DNA puts an end to the
  search for meaning - a disappointment to
• humanistic side of the two cultures debate

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The meaning of life

• While asking general questions led to very
  limited answers, asking limited questions turned
  out to provide more and more general answers
  (Biologist Francois
  Jacob in von Baeyer, 2003 p. 30)

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The certainties and the uncertainties of
information
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Certainties
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Info- certainties

• The whereabouts of matter
• Quantum computing
• the qubit replaces the bit a blend of yes/no
• Nanotechnology
• very small factories
• AI AL programs
• Genetic algorithms
• search space
• Neural nets
• fuzzy logic replaces binary

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From Binary to Fuzzy Logic

• Discrete Binary
• True or False
• 1/0
• no in-between

• Continuous, Fuzzy Logic (see von Neumann,
  1940-50s)
• Completely true
• Very true
• Fairly true
• Fairly false
• Very false
• Completely false
• ad infinitum (Cryan, Shatil and Mayblin,
  2001 p. 89).

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DNA certainties

• Baby 81
• Following the boxing day tsunami
• This baby was claimed by 9 parents
• DNA tests proved who the real parents were
• Barcoding Noah's Ark
• Biometrics

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Information Overloadbeating the randomness

• Increased speed of transmission
• overload of data produced
• creates a sense of disorder within the digital
  space
• But randomness is not chaos
• layer of unknowable complexity for which the
  perceiving mind can only create metaphors for
• (N Katherine Hayles 1999 pp. 286-287)

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Making order out of chaos

• If information overload is an illness then we
  have a cure
• More information
• Logic machines

• Bots
• Spiders
• Crawlers
• Filters
• Intelligent agents
• Programs (automated algorithms) work at speeds
  that exceed human information gathering capacity

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Uncertainties
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Info-uncertainties

• Information system built around rules of formal
  logic
• Crisis in the 1930s!!!
• Incompleteness - Gödel 1931
• Undecidability - Turing 1936

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Syllogism - Boolean Logic

• Syllogism
• Aristotle 384-322 BC
• All cats are grey
• (premise one)
• Oscar is a cat
• (premise two and the predicate of the first
  statement)
• Oscar is grey
• (the conclusion)

• Boolean version
• George Boole 1847
• All Xs are Y
• P is X
• Therefore P is Y
• Shannon used this system

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The Liar Paradox (or cycle)- an earlier crisis

• Lair paradoxes
• this sentence is false
• what I am now saying if false
• this program is a virus

• It is in the logical outcome that if one supposes
  that the sentence is true, then it must also be
  false, while if it is false then it must also be
  true

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Incompleteness, undecidability and the logic
system under attack

• The logic system, under pressure for
  completeness, is beleaguered by a relentless,
  massive assault from contradictions. There is a
  deliberate sacrifice of the opportunity to be
  contradiction-free for the certainty of having a
  tolerable frequency of contradiction (see
  Sorensen, 2001 p. 155).

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THE VIRAL PROBLEM

• We are faced today with an entire system of
  communication technology which is the perfect
  medium to host and transfer the very programs
  designed to destroy the functionality of the
  system. (IBM Researcher
  Sarah Gordon, 1995)
• Originally published in Computers and Security
  magazine Published by Elsevier Press' Computers
  and Security

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THE DETECTION PROBLEM

• A viral logic paradox

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Undecidability paradox

• Fred Cohen (1984) argues that detection of
  viruses is logically undecidable (see Turing,
  1936)
• There is no proactive algorithmic defence
  against viral attack

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Strange loops in formal logic

• The virus is, as Cohen (1984) defined

• A self-contradictory statement
• A self-referencing statement
• Viruses contain a decision procedure that is
  self-contradictory a type of lair paradox
  making precise determination of a virus by its
  appearance unresolved (see Cohen, 1984)

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THE VIRAL SOLUTION

• The biological analogy

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• If you want understand life, dont think about
  vibrant, throbbing gels and oozes. Think about
  information technology (Dawkins in Louw and
  Duffy, 1992 p. 34)

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computer software and genetic material Louw and
Duffy, 1992

• Both contain information
• Both are parasitic
• appropriate hosts operating system and hardware
• Both can evolve
• Biological viruses naturally mutate
• Computer viruses are programmed to
  spontaneously mutate

• Both contain a set of instructions a type of
  encoded blueprint
• enables them to actively propagate or replicate
  themselves

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The Solution

• Epidemiology
• Mathematical study of disease as basis of
  understanding viral spread

• Biological immune system cure?
• IBM/Symantec Digital Immune System technology

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The limits of antivirus methods

• The analogy breaks down
• No cure as such

• Reactive, not proactive solution
• Unlike human, Turing composition is both self and
  other
• Viruses do not spread like diseases
• The immune system is still a black box
• Just automated detection

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Virus triggers further info uncertainties

• High entropy
• Chaos
• Disorder
• Noise

• Wild nature
• Struggle between human life and lifelike
  computer intelligence

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Panic computing

• Our perception of viruses stems both from the
  way we consider the most recent epidemic
  diseases, such as AIDS, and from an innate fear
  of having one's own body invaded by other
  efficient organisms (Ludovico, 2002) 

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Panic computing

• what scares you most about the virus?
• Alien presence
• Intrusive otherness
• the virus hews to its own agenda of survival and
  reproduction. Its oblivious self-interest
  violates the unity of purpose that defines your
  system as yours. (Dibbell, 1995)

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Panic computing

• As much as the actual damage that any virus may
  do to an operating system, the thought of the
  virus and its possible consequences induces panic
  in users, producing a kind of ambient fear that
  infects users much more than it effects
  computers (Bell, 2000)

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The contagious idea

• A manifestation of
• The barely submerged emotions of hostility and
  fear that humans have towards computer
  technology (Lupton, D in Bell and
  Kennedy (eds) 2000)

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Panic computing

• AI may need human input initially, but once
  under way it can, and does, take on an existence
  of its own, apparently independent of human
  concerns and with its own internal dynamic. As a
  case in point, the love bug virus very soon
  started to mutate into more complex functions
  that rendered it all the more difficult to track
  down and neutralise (Sim, 2001 p. 22)

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Info-uncertaintiesbeyond analogies

• Complex networks and self-organisation

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Self-organisation

• Networks surround us
  (Andrea Scharnhorst, 2003 Complex Networks
  and the Web Insights From Nonlinear Physics)
• We are built from networks of genes and form part
  of networks
• railways
• electricity lines
• food webs
• neuronal cells
• semantic webs
• trade relationships

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The interaction between cells Life as it is?
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Conways Game of Life life as it could be?
http//www.bitstorm.org/gameoflife/
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Cybercontrol Robins Webster, 1999

• Sociological circles during the 1990s reject
  natural metaphors
• Nothing social can be regarded as natural (See
  Terranova 2004 p. 121)

• Information as commodity cannot be lifelike
• Or else we accept that capitalism is also
  lifelike
• Networks shaped by
• Corporation
• Military

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Conways Game of Life creative evolution

• The Rules
• Based on the interaction between cells

• Loneliness
• Each cell with one or no neighbours dies
• Overpopulation
• Each cell with four or more neighbours dies
• Survives
• Each cell with two or three neighbours becomes
  populated.

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or life as it could be?
http//www.bitstorm.org/gameoflife/