Download I501- Fall 2009

Informatics
Cybernetics
Informatics
We’ll discuss this week:
 McCulloch, W. and W. Pitts [1943], "A Logical Calculus of Ideas Immanent in
Nervous Activity". Bulletin of Mathematical Biophysics 5:115-133.
 Coutinho, A. [2003]. "On doing science: a speech by Professor Antonio
Coutinho". Economia, 4(1): 7-18, jan./jun. 2003.
 Heims, S.G. [1991]. The Cybernetics Group. MIT Press. Chapters: 1,2, 11, and
12.
Schwartz, M.A. [2008]. "The importance of stupidity in scientific research".
Journal of Cell Science, 121: 1771.
Informatics
Informatics:
a possible parsing
X-Informatics or
Computational X
HealthInformatics
HCID
Bio-
 towards problem solving
 beyond computing
 into the natural and social
 synthesis of information technology
Data &
Search
Security
Computer
Science
Social
Informatics
Data
Mining
Complex
Systems
Music-
Chem-
Geo-
Informatics
Pre-cybernetics
“Cerebral inhibition meeting”
 New York City, May 1942
 Organized by Frank Freemont-Smith of the Josiah Macy Jr. Foundation
 Social Sciences: Lawrence Frank, Margaret Mead and Gregory Bateson
 Sciences: Warren McCulloch and Arturo Rosenblueth
 Result
 Rosenblueth’s presentation of concepts from Norbert Wiener and Julien Bigelow
 Homeostasis, purposeful action (goal-direction), aiming
 A new paradigm of interdisciplinary research?
 Goal-directed actions
 Controversial: explaining actions in terms of future events, violating cause and effect
 Teleological mechanisms
 Circular causality
 requiring negative feedback (postulated to be very common)
 Present state becomes input for action at next moment: State-determined systems
 The mathematics were accessible
Informatics
Post-WWII science: Macy Meetings 1946-1953

The Feedback Mechanisms and Circular Causal Systems in Biology and the Social
Sciences
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Interdisciplinary
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John Von Neumann, Leonard Savage, Norbert Wiener, Arturo Rosenblueth, Walter Pitts, Margaret Mead,
Heinz von Foerster, warren McCulloch, Gregory Bateson, Claude Shannon, Ross Ashby, etc.
Synthetic approach

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
Since a large class of ordinary phenomena exhibit circular causality, and the mathematics is accessible, let’s
look at them with a war-time team culture
Participants


March 1946 (10 meetings between 1946 and 1953)
Engineering-inspired: amplifiers, negative feedback, feedback circuits
Supremacy of mechanism
All can be axiomatized and computed

Mculloch & Pitts’ and Von Neumann’s work was major influence
Informatics
Alan Turing: 1935-1954
pop science hero for Turing test

“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. The new form of the problem can be described in
terms of a game which we call the 'imitation game." It is played with
three people, a man (A), a woman (B), and an interrogator (C) who may
be of either sex. The interrogator stays in a room apart front the other
two. The object of the game for the interrogator is to determine which of
the other two is the man and which is the woman. He knows them by
labels X and Y, and at the end of the game he says either "X is A and Y
is B" or "X is B and Y is A.”
Jack copeland
Informatics
How to fail the Turing test:
Informatics
Alan Turing: 1935-1954
Universal Turing Machine

In 1935, at Cambridge University, Turing
invented the principle of the modern computer:
Universal Turing Machine.
 Abstract digital computing machine consisting of
a limitless memory and a scanner that moves
back and forth through the memory, symbol by
symbol, reading what it finds and writing further
symbols (Turing [1936]).
 The actions of the scanner are dictated by a
program of instructions that is stored in the
memory in the form of symbols.
 Note: Turing machine is mathematical construct
to study/define notions of computability
Informatics
Turing Machine
From : A. M. Turing (1936) On Computable numbers… Proceedings of the London Mathematical Society.
We have said that the computable numbers are those whose decimals are calculable by finite means. This requires rather more explicit
definition. No real attempt will be made to justify the definitions given until we reach §9. For the present I shall only say that the
justification lies in the fact that the human memory is necessarily limited.
We may compare a man in the process of computing a real number to a machine which is only capable of a finite number of conditions
q1, q2, ..., qR which will be called “m-configurations”. The machine is supplied with a “tape”, (the analogue of paper) running
through it, and divided into sections (called “squares”) each capable of bearing a “symbol”. At any moment there is just one
square, say the r-th, bearing the symbol S(r) which is “in the machine”. We may call this square the “scanned square”. The symbol
on the scanned square may be called the “scanned symbol”. The “scanned symbol” is the only one of which the machine is, so to
speak, “directly aware”. However, by altering its m-configuration the machine can effectively remember some of the symbols which
it has “seen” (scanned) previously. The possible behaviour of the machine at any moment is determined by the m-configuration qn
and the scanned symbol S(r). This pair qn, S(r) will be called the “configuration”: thus the configuration determines the possible
behaviour of the machine. In some of the configurations in which the scanned square is blank (i.e. bears no symbol) the machine
writes down a new symbol on the scanned square: in other configurations it erases the scanned symbol. The machine may also
change the square which is being scanned, but only by shifting it one place to right or 1eft. In addition to any of these operations
the m-configuration may be changed. Some of the symbols written down will form the sequence of figures which is the decimal of
the real number which is being computed. The others are just rough notes to “assist the memory”. It will only be these rough notes
which will be liable to erasure.
It is my contention that these operations include all those which are used in the computation of a number.
Informatics
Turing machine
q1
wrt
S(r)=
q2
q3
mov
nxq
wrt
mov
nxq
wrt
mov
nxq
0 1
L
q2
0
R
q1
0
L
q2
1 0
R
q4
1
R
q3
1
L
q1
(state transition table)
m-configuration= {q1,…}
{L,R} += position t
(machine state)
S(r)= {0,1}
{0,1}
= write value
0 0 1 0 1 0 1 1 1 0 1
r
.
.
.
Informatics
Universal Turing machine

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State transition table ITSELF can be stored on dedicated section of tape!
 Hence “Universal Turing Machine”: all possible turing machines can be described
as strings on tape
 Data and program are encoded on same substrate, in same manner
 Later instantiated by Von Neumann as “stored program concept”
 "We are trying to build a machine to do all kinds of different things simply by
programming rather than by the addition of extra apparatus," (1947)
Demonstrating functional analogy (mathematical isomorphism) with UTM is a big deal
 Defines mathematical constraints
 Cf. Wolfram’s announcement
(http://www.wolframscience.com/prizes/tm23/background.html)
Informatics
Is Lego a UTM? ;-)
Informatics
Is your brain a universal turing machine?


McCulloch, W. and W. Pitts [1943], "A Logical Calculus of Ideas
Immanent in Nervous Activity". Bulletin of Mathematical
Biophysics 5:115-133.
 A finite network of binary neuron/switches ~ Turing machine
program
 Neurons as basic computing unit of the brain
 Circularity is essential for memory (closed loops to
sustain memory)
 Brain (mental?) function as computing
Others at Macy Meeting emphasized other aspects of brain
activity
 Chemical concentrations and field effects (not digital)
 Ralph Gerard and Fredrik Bremmer
Informatics
Some early contenders (not electronic, not digital, or not
Turing complete)

Babbage difference engine (1822)

Babbage analytical engine (turing
complete!)
Informatics
Some early contenders (not electronic, not digital, not
Turing complete)
 Turing bombe:
Enigma Cracker (19401945)
Informatics
Some early contenders (not electronic, not digital, not
Turing complete)

Colossus Mark 1,2
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Electromechanical code decoders
Paper tape input/output
Internal simulation of encryption device
No. 2 using vacuum tubes
Informatics
Some early contenders (not electronic, not digital, not
Turing complete)

Konrad Zuse Z1,2,3 (1941)

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Fully program-controled
Using electro-mechanical relays
http://www.youtube.com/watch?v=vEx4t71jca4

Harvard Mark I (1944)
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Drive-shafts & switches
Separation data-program
765,000 components
4500 kg
Informatics
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
Informatics
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.
Informatics
ENIAC 1945
Computer
bug
Informatics
ENIAC 1945
Informatics
ENIAC 1945
Informatics
John von Neumann

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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
 High 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.
Informatics
EDSAC 1949
(Electronic Delay Storage Automatic Calculator (Cambridge)
_
Stored program
General purpose
Informatics
EDVAC 1949
(Electronic Delay Variable Automatic Calculator (Cambridge)
_
Descendent of ENIAC
Stored program
binary
Informatics
IAS Machine 1942-1952
First electronic digital computer with 40 bit word (IAS, Princeton)
_
First to combine data and program? See Manchester Manchester Small Scale Experimental Machine
5.1KB memory!
Many descendants, among
them the MANIAC at Los
Alamos Scientific Laboratory:
hydrogen bombs and chess.
Informatics
Meanwhile at the MACY meetings:
Norbert Wiener and Arturo Rosenblueth:
Goal-directed behavior and negative feedback (control)
Homeostasis and circular causality
In machines and biology
Automata Theory
Communication
The fundamental idea is the message, even though the message may not be sent by man and
the fundamental element of the message is the decision” (Norbert Wiener)
Information and Communication Theory
Natural semiotics (McCulloch and others later get into Peircean Semiotics)
“functional equivalence” of systems (general systems)
Bio-inspired mathematics and engineering and computing/mechanism-inspired biology and
social science
Informatics
Macy meetings:
other key concepts

Gregory Bateson and Margaret Mead

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Lawrence Frank
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Homeostasis and circular causality in society
Transvestite ceremony to diffuse aggressive action in Iatmul
culture
Learning and evolution
 Can a computer learn to learn?
A new organizing principle for the social sciences (control and
communication)
 As much as evolution was for Biology
The new interdisciplinary concepts needed a new kind of
language
 Higher generality than what is used in single topic
disciplines
 A call for a science of systems
Yehoshua Bar-Hillel


Optimism of a new (cybernetics and information) age
“A new synthesis […] was destined to open new vistas on
everything human to help solve many of the disturbing open
problems concerning man and humanity”.
Informatics
Cybernetics as a discipline

Norbert Wiener’s book had huge impact
 Coined the term “Cybernetics”
 Κυβερνήτης (kybernētēs, steersman, governor, pilot, or rudder — the same root
as government).
 Overoptimism?
 “Those of us who have contributed to the new science of cybernetics, stand in a
moral position which is, to say the least, not very comfortable. We have
contributed to the initiation of a new science which, as I have said, embraces
technical developments with great possibilities for good and for evil”. [1948]
 A “premature delivery” (Ralph Gerard)?
 “excessive optimism and a misunderstanding of the nature of scientific
achievement.” (Gregory Bateson) ?
...
Informatics
Cybernetics as a discipline

Death by its own success
 Success meant adoption by many fields
 But not a highly successful discipline in itself

Most practitioners became marginal in their original disciplines
 The price of interdisciplinary research in Academia?
 Successful descendents in more interdisciplinary settings outside of
academia
 Government labs (e.g. Los Alamos National Laboratory)
 Private Institutes (e.g. Santa Fe Institute)

What about informatics as a field?
 Finally academia accepting the reality of the information age in society?...
 Need to define identity, lest same trap of interdisciplinary
Informatics
Lives on through its effects on science and
language
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Learning as information transmission
Computer is prevalent analogy for understanding life and cognition
“Feedback” is now general terms to mean information about outcomes
(“terugkoppeling”)
Shift fom individualism and cause & effect, to circular causation and social interaction
“Programmed” behavior
Society and organisms as systems
Wiener’s prediction of a second industrial revolution centered on communication,
control, computation, information, and organization was correct


Abundance of technology and mass production of communication devices
 Grew out of the ideas first reported by the cyberneticians
Informatics is an offspring of cybernetics
Informatics
Informatics:
a possible parsing
X-Informatics or
Computational X
HealthInformatics
HCID
Bio-
 towards problem solving
 beyond computing
 into the natural and social
 synthesis of information technology
Data &
Search
Security
Computer
Science
Social
Informatics
Data
Mining
Complex
Systems
Music-
Chem-
Geo-
Informatics
Cybernetics
 Created new fields
 analytical in methodology
 synthetic
 interdisciplinary
 concepts useful in constituent fields
Social and
Psychological
Sciences
Mathematics
&
Engineering
Biological
Sciences
Cybernetics
AI
CS
OR
Informatics
Readings for next week:
(General Systems Theory)
Klir, G.J. [2001]. Facets of systems Science. Springer.
Chapters: 1 and 2
Rosen, R. [1986]. "Some comments on systems and
system theory". Int. J. of General Systems, 13: 1—3.
Ashby, W.R.[1956]. An Introduction to Cybernetics,
Chapman & Hall, London, Chapter 1.