Universal computers, semiotics,
and information
I501 – Intro to Informatics
Computers…
Origins in the need to efficiently compute numerical
tables, used in math, ballistics, astronomy, etc.
Napiers and Briggs’ tables
Briggs (1561-1630): logs of 30,000 numbers to 14 decimal
places and logs/tans of 1/100 of every degree, 14 decimal
places
calculators to replace painstaking and error-prone human
calculator work
Let’s not forget…
Some early entries…
Charles Babbage (1791-1871)
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Babbage difference engine (1822)
Babbage analytical engine (1837)
https://www.youtube.com/watch?v=jiRgdaknJCg
Some early contenders (not electronic,
not digital, not Turing complete)
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Turing bombe: Enigma Cracker at
Bletchley Park (1940-1945)
Electro-mechanical, hundreds
produced in UK and US
Some early contenders…
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Colossus Mark 1,2 (1943-1945)
– Electronic (1.6k vacuum tubes) decoders for Lorenz SZ, digital using
Boolean functions
– Paper tape input/output
– Internal simulation of encryption device
– No. 2 using vacuum tubes
Some early contenders…
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Konrad Zuse Z1,2,3 (1941)
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–
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Fully program-controled
Using +1000 electro-mechanical relays
“Turing complete”
http://www.youtube.com/watch?v=vEx4t71jca4
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Harvard Mark I (1944)
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Drive-shafts & switches
Separation data-program
765,000 components
4500 kg
The vacuum tube:
an audiophile’s delight, a turing machine builder’s nightmare
• Vacuum tubes:
– Invented by American physicist Lee De
Forest in 1906.
– Electricity heats a filament inside the tube.
Freed electrons travel through vacuum
from one pole to the next. Grid sits
between poles. Small charges on grid can
block large currents: tube = amplifier or
switch.
– the presence of current represented a one.
• Punched-card input and output
– Boxes & truck load
– Beware of “syntax error”
• Storage of all those vacuum tubes and the
machinery required to keep them cool: entire
floors of building
ENIAC (1945)
Electronic Numerical Integrator and Computer
• First fully programmable, electronic digital computer to be
built in the U.S.
– Electrical Numerical Integrator and Computer
– University of Pennsylvania, for the Army Ordnance
Department, by J. Presper Eckert and John Mauchly.
• Used decimal digits instead of binary ones
• Nearly 18,000 vacuum tubes for switching.
• Far from general-purpose: The primary function was
calculation of tables used in aiming artillery.
• ENIAC was not a stored-program computer, and setting
it up for a new job involved reconfiguring the machine
by means of plugs and switches.
ENIAC 1945
Computer
bug
ENIAC 1945
ENIAC 1945
John von Neumann
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•
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Emphasized stored-program concept for electronic computing (machine modifying its own
program)
At first Macy Meeting
– Compared neurons to binary switches
– “The Computer and the Brain”: bio-inspired design
– Influenced by McCulloch & Pitts, Turing
– Considerabke impact on cybernetics
Lead the ENIAC (1944-1945) group to the EDVAC (1952)
– Von Neumann made the concept of a high-speed stored-program digital computer widely
known through his writings and public addresses: ‘von Neumann machines’.
– von Neumann architecture: The separation of data and program (storage )from the
processing unit = architecture still in use today.
Prolific scientist
– Father of game theory, cellular automata, Cybernetics, Artificial Intelligence
– See book: Aspray, William. 1990. John von Neuman and the Origins of Modern Computing.
Cambride, Mass.: MIT Press.
EDSAC
1949
Electronic Delay Storage Automatic Calculator (Cambridge)
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Stored program
General purpose
EDVAC
1949
(Electronic Delay Variable Automatic Calculator (Cambridge)
Descendent of
ENIAC
Stored program
binary
IAS Machine 1942-1952
First electronic digital computer with 40 bit word (IAS, Princeton)
_
5.1KB memory!
Many descendants,
among them the
MANIAC at Los
Alamos Scientific
Laboratory:
hydrogen bombs and
chess.
First to combine data and program? See Manchester Manchester Small Scale Experimental Machine
https://www.youtube.com/watch?v=8u9ZyV-BHFA
Z80 @ 3.5mhz
1kb of memory
Cassette recorded at 250 baud
(250 bits/s, or 31 ASCII
chars/s)…
Fastest Computers
IBM Roadrunner
IBM BlueGene/L
MDGRAPE-3: Not a generalpurpose computer
China’s Tianhe-2 remains the fastest supercomputer in the world, with a Linpack
benchmark performance of 33.86 petaflops
Big Red II, the new system will be capable of operating at a peak rate of one
petaFLOPS, or one thousand trillion floating-point operations per second -- 25 times
faster than the original Big Red first acquired in 2006.
Fastest general-purpose computer (may 2008): 1 petaflop !!! – 1 quadrillion
calculations per second --- Roadrunner @ Los Alamos--- aprox 214 s needed = 4.6
hours for Hanoi problem (assuming one disk change per operation)
Fastest Computer (june 2006): 1 petaflop !!! – 1 quadrillion calculations per second --MDGRAPE-3 @ Riken, Japan
Fastest Computer (late 2005): 280.6 teraflops - 280.6 trillion calculations a second --Approaching petaflops: 3 petaflops in late 2006????
Fastest Computer (early 2005): 135.5 teraflops - 135.5 trillion calculations a second --Approaching petaflops: 250
What happens to Moore’s law ?
Theoretical perspective
• From mechanical and electronic computers to
theoretical models of computing
• Alan Turing in 1936-1937 introduced the idea of a
Turing Machine, a “theoretical computer”, a
minimal formal description of a computer that
“can be used to compute any computable
sequence”, including other computers (“Universal
Turing Machines”)
• Foundation of computer science as a theoretical
discipline
• More next week…
Stepping back a bit: information, what is
it?
SIGN
ICON
Stepping back a bit: information, what
is it?
“Information is that which reduces
uncertainty”. (Claude Shannon)
“Information is that which changes us”.
(Gregory Bateson)
“Information is a semantic chameleon”.
(Rene Thom)
The word information derives from the Latin
informare
in + formare = give form, shape, or character to.
It is therefore to be the formative principle of,
or to imbue with some specific character or
quality.
From: Von Bayer, H.C. [2004]. Information: The New
Language of Science. Harvard University Press.,
Chapter 3, pp 20-21.\
Systems science: cross-disciplinary
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For hundreds of years, the word information has been used to signify knowledge
and related terms such as meaning, instruction, communication, representation,
signs, symbols, etc.
– “the action of informing; formation or molding of the mind or character,
training, instruction, teaching; communication of instructive knowledge”.
Oxford English Dictionary
Two of the most outstanding achievements of science in the XX century
– (1) Invention of Digital Computers and (2) Information Technology
– Birth of Molecular Biology
• Resulted in the generation of vast amounts of data and information and
new understandings of the concept of information itself
– Modern science is unraveling the nature of information in numerous areas
such as communication theory, biology, neuroscience, cognitive science, and
education, among others.
Organization very tied to idea of information
– Essential for systems approaches
– Cf. Rosen’s comments on energy vs. communication
Information as representation
• We often presume that such and such information is simply
a factual representation of reality
– but representation of reality to whom?
– The act of representing something as a piece of knowledge
demands the existence of a separation between the thing
being represented and the representation of the thing for
somebody – between the known and the knower.
• This is a form of communication:
– the representation of an object communicates the
existence of the (known) object to the knower that
recognizes the representation.
Information as relation
• The central structure of information is a relation
– among signs, objects or things, and agents capable of understanding
(or decoding) the signs.
• Agents are informed by a Sign about some Thing.
sign
thing
agents
Information as relation
• The information relation is a sign system
• Semiotics is the discipline that studies sign systems
sign
thing
agents
Information as representation
• Signs are objects whose function is
to be about other things
– Objects whose function is reference
rather than presence.
– Do not deliver things but a sense or
knowledge of things – a message.
• Example: Road Signs
– Not a distant thing; but about distant
things
• For information to work
– There has to be a system of signs
– Recognizable by the relevant group of
people (drivers!)
Playing with sign systems
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Language and sign systems surround us
– We are often not aware we use them
We notice them when an object oscillates between sign and thing
– Reverts from reference to presence
Playing with reference in sign systems is common in Art
“beware: Cliff”
Or
“beware: low gravity”?
Playing with sign systems
Kitty
O
I
am
my
own
way
of being in
view and yet
invisible at
once Hearing
everything
you see I
see all of
whatever you
can have heard
even inside the
deep silences of
black silhouettes
like these images
of furry surfaces
darkly playing cat
and mouse with your
doubts about whether
other minds can ever
be drawn from hiding
and made to be heard
in inferred language
I can speak only in
your voice Are you
done with my shadow
That thread of dark
word
can
all
run
out
now
and
end
our
tale
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Symbols are used as
pictorial objects to draw
the picture of Kitty:
presence
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But within the silhouette
of Kitty there is also a tale
of cats: reference
by John Hollander. Kitty, Black domestic
shorthair
Paul van Ostayen
The name of the rose
•
Movie version of the Umberto Eco’s
book
– An old manuscript, the message, is
literarily dangerous
– Becomes literally poisonous
– reference and presence become
very intertwined indeed!
Play on reference
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The accepted meaning of the symbols
conflicts with the object
Highlights how arbitrary symbols are
“This is not a pipe”
The Key of Dreams, 1930,
Rene Maggritte
When is an object a sign or a thing?
information
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Semantics
– the content or meaning of the Sign of a Thing for an Agent
• Relations between signs and objects for an agent
• the study of meaning.
Syntax
– the characteristics of signs and symbols devoid of meaning
• Relations among signs such as their rules of operation,
production, storage, and manipulation.
Pragmatics
– the context of signs and repercussions of sign-systems in an
environment
• it studies how context influences the interpretation of
signs and how well a signs-system represents some aspect
of the environment
infromatics
Semiotics and informatics
(Peirce’s) Typology of Signs
1839-1914
• Icons are direct representations of objects.
– Similar to the thing they represent.
– Pictorial road signs, scale models, computer icons.
• A footprint on the sand is an icon of a foot.
• Common in computer interface (watch the evil
metaphore!)
(Peirce’s) Typology of Signs
• Indices are indirect representations of objects, but necessarily
related.
– Smoke is an index of fire, the bell is an index of the tolling
stroke
– a footprint is an index of a person.
(Peirce’s) Typology of Signs
• Symbols are arbitrary representations of objects
– Require exclusively a social convention to be understood
– Convention establishes a code, agreed by a group of agents,
for understanding (decoding) the information contained in
symbols.
– Smoke is an index of fire, but if we agree on an appropriate
code (e.g. Morse code) we can use smoke signals to
communicate symbolically.
Internally consistent coding
+ indices:
~ non-arbitrary symbols
(Peirce’s) Typology of Signs
•
•
•
Icons are direct representations of objects.
– Similar to the thing they represent.
– Pictorial road signs, scale models, computer icons.
• A footprint on the sand is an icon of a foot.
Indices are indirect representations of objects, but necessarily related.
– Smoke is an index of fire, the bell is an index of the tolling stroke
• a footprint is an index of a person.
Symbols are arbitrary representations of objects
– Require exclusively a social convention to be understood
• Convention establishes a code, agreed by a group of agents,
for understanding (decoding) the information contained in
symbols.
• Smoke is an index of fire, but if we agree on an appropriate
code (e.g. Morse code) we can use smoke signals to
communicate symbolically.
Readings next week
Christopher Miles: Hughes (2009)
Quantification of artistic style through sparse
coding analysis in the drawings of Pieter Bruegel
the Elder. PNAS 107(4):1279–1283
Ben Jelen: Piantadosi, S. T.,et al (2011). Word
lengths are optimized for efficient
communication. PNAS, 108(9), 3526–3529.