1. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Cybernetics

2. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009  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. We’ll discuss this week:

3. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Informatics: a possible parsing Complex Systems Data & Search Data Mining HCID Social Informatics Security Bio- Chem- Geo- Music- Health-  towards problem solving  beyond computing  into the natural and social  synthesis of information technology

4. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

5. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Post-WWII science: Macy Meetings 1946-1953  The Feedback Mechanisms and Circular Causal Systems in Biology and the Social Sciences  March 1946 (10 meetings between 1946 and 1953)  Interdisciplinary  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  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  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

6. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

7. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 How to fail the Turing test:

8. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

9. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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.

10. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Turing machine 1 1 0 0 0 0 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 S(r)= {0,1} m-configuration= {q1,…} (machine state) wrtmovnxqwrtmovnxqwrtmovnxq...... 01Lq20Rq10Lq2 10Rq41Rq31Lq1 S(r)= q1 q2q3 (state transition table) {L,R} += position t {0,1} = write value r

11. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Universal Turing machine  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)

12. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Is Lego a UTM? ;-)

13. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

14. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009  Babbage difference engine (1822)  Babbage analytical engine (turing complete!) Some early contenders (not electronic, not digital, or not Turing complete)

15. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009  Turing bombe: Enigma Cracker (1940- 1945) Some early contenders (not electronic, not digital, not Turing complete)

16. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009  Colossus Mark 1,2  Electromechanical code decoders  Paper tape input/output  Internal simulation of encryption device  No. 2 using vacuum tubes Some early contenders (not electronic, not digital, not Turing complete)

17. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Some early contenders (not electronic, not digital, not Turing complete)  Konrad Zuse Z1,2,3 (1941)  Fully program-controled  Using electro-mechanical relays  Harvard Mark I (1944)  Drive-shafts & switches  Separation data-program  765,000 components  4500 kg http://www.youtube.com/watch?v=vEx4t71jca4

18. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

19. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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.

20. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 ENIAC 1945 Computer bug

21. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 ENIAC 1945

22. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 ENIAC 1945

23. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 John von Neumann  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.

24. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 EDSAC 1949 (Electronic Delay Storage Automatic Calculator (Cambridge) _ Stored program General purpose

25. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 EDVAC 1949 (Electronic Delay Variable Automatic Calculator (Cambridge) _ Descendent of ENIAC Stored program binary

26. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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.

27. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

28. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Macy meetings: other key concepts  Gregory Bateson and Margaret Mead  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  Lawrence Frank  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”.

29. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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) ?...

30. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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

31. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Lives on through its effects on science and language  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

32. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 Informatics: a possible parsing Complex Systems Data & Search Data Mining HCID Social Informatics Security Bio- Chem- Geo- Music- Health-  towards problem solving  beyond computing  into the natural and social  synthesis of information technology

33. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009  Created new fields  analytical in methodology  synthetic  interdisciplinary  concepts useful in constituent fields Cybernetics Biological Sciences Social and Psychological Sciences Mathematics & Engineering AI OR CS

34. I501 – Introduction to Informatics jbollen@indiana.edu http://informatics.indiana.edu/jbollen/I501 Info rm atics and computing lecture 2 – Fall 2009 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. Readings for next week: (General Systems Theory)