Alan Mathison Turing

A. M. Turing


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Alan Mathison Turing

A. M. Turing

The Rutherford Journal,

The New Zealand Journal for the History and Philosophy of Science and Technology

Volume 4, 2011-2012

This Special Issue for the 2012 Alan Turing Centenary Year is a web-book:

Alan Turing, Father of the Modern Computer
B. Jack Copeland and Diane Proudfoot




Alan Turing Artificial Intelligence 'Gooware', gooware computer, Unorganized machine, ACE Computer (Automatic Computing Engine), Chemical computer, BelousovZhabotinsky reaction / Reacción de Beloúsov-Zhabotinski,




Alan Turing

Wikipedia, (20160612)

Alan Mathison Turing OBE FRS (/ˈtjʊərɪŋ/; 23 June 1912 – 7 June 1954) was a pioneering English computer scientist, mathematician, logician, cryptanalyst and theoretical biologist. He was highly influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general purpose computer. Turing is widely considered to be the father of theoretical computer science and artificial intelligence.

During the Second World War, Turing worked for the Government Code and Cypher School (GC&CS) at Bletchley Park, Britain's codebreaking centre. For a time he led Hut 8, the section responsible for German naval cryptanalysis. He devised a number of techniques for breaking German ciphers, including improvements to the pre-war Polish bombe method and an electromechanical machine that could find settings for the Enigma machine. Turing played a pivotal role in cracking intercepted coded messages that enabled the Allies to defeat the Nazis in many crucial engagements, including the Battle of the Atlantic; it has been estimated that this work shortened the war in Europe by as many as four years.[6]

After the war, he worked at the National Physical Laboratory, where he designed the ACE, among the first designs for a stored-program computer. In 1948 Turing joined Max Newman's Computing Machine Laboratory at the Victoria University of Manchester, where he helped develop the Manchester computers[7] and became interested in mathematical biology. He wrote a paper on the chemical basis of morphogenesis,




‘On Computable Numbers, With an Application to The Entscheidungsproblem’

A. M. Turing 1936



Computing Machinery & Intelligence’, aparecido en la revista Mind (1950),



Beyond Turing's Machines
Science (04/13/12) Vol. 336, No. 6078, P. 163 Andrew Hodges




Alan Turing's most profound achievement is arguably the principle of a universal machine that makes logic rather than arithmetic the computer's driving force, writes the University of Oxford's Andrew Hodges. Turing also defined the concept of computability, and suggested that mathematical steps that do not follow rules, and are thus not computable, could be identified with mental intuition. His 1950 treatise presented a basic argument that if the brain's action is computable, then it can be deployed on a computer or universal machine. Turing later suggested that modeling of the human brain might be impossible because of the nature of quantum mechanics, and his view of what is computable has not changed despite the advent of quantum computing. Many thought-experiment models investigate the implications of going beyond the constraints of the computable, and some require that machine elements operate with unlimited speed or permit unrestricted accuracy of measurement. Others more deeply explore the physical world's nature, with a focus of how mental operations relate to the physical brain and the need to rethink quantum mechanics because uncomputable physics is basic to physical law. Hodges says this way of thinking is part of Turing's legacy even though it superficially runs counter to his vision.



Turing on Computation, Memory and Behavior

Eli Dresner

The Rutherford Journal, Volume 3, 2010

The New Zealand Journal for the History and Philosophy of Science and Technology





Bekenstein bound

“In physics, the Bekenstein bound is a conjectured limit on the entropy S or information that can be contained within a region of space containing a known energy. It implies that information must be material, requiring finite size and energy. In computer science, this implies that there is a maximum information processing rate and that Turing machines, with their (by definition) infinite memory tape, are physically impossible if they are to have a finite size and bounded energy. The bound was originally found by Jacob Bekenstein in the form


where R is loosely defined as the radius of the region, and E is the energy of the contained matter as measured when the matter is moved to an infinite distance, i.e., accounting for binding force potential energies. Note that while gravity plays a significant role in its enforcement, the bound is independent of Newton's Constant G.” (Wikipedia May 25, 2009)






La Singularidad Desnuda

Un universo impredecible de pensamientos y cavilaciones sobre ciencia, tecnología y otros conundros


Incluye temas como

Turing en una cáscara de nuez: No computabilidad


Gödel en una cáscara de nuez: Primer Teorema de Incompletitud


Gödel en una cáscara de nuez: Segundo Teorema de Incompletitud, y Teorema de Completitud


Gödel en una cáscara de nuez: La diagonalización de Cantor


Turing en una cáscara de nuez: Teorema de Rice (o por qué ningún antivirus será fiable al 100%)


Turing en una cáscara de nuez: castores afanosos y la paradoja de Berry


Turing en una cáscara de nuez: Complejidad de Kolmogorov





Morphogenetic field


Morphogenetic fields are defined by Sheldrake as the subset of morphic fields which influence, and are influenced by living things.

“The term [morphic fields] is more general in its meaning than morphogenetic fields, and includes other kinds of organizing fields in addition to those of morphogenesis; the organizing fields of animal and human behaviour, of social and cultural systems, and of mental activity can all be regarded as morphic fields which contain an inherent memory.” — Sheldrake, The Presence of the Past (Chapter 6, page 112)

(Wikipedia October 20, 2008)


Campo morfogenético

“En el marco teórico de la evolución biológica, el campo morfogenético, hipótesis de los campos morfogenéticos, o campos mórficos, sería el nombre dado por Rupert Sheldrake a un campo hipotético que explicaría la evolución simultánea de la misma función adaptativa en poblaciones biológicas no contiguas

Morfo viene de la palabra griega morphe, que significa forma. Los campos morfogenéticos son campos de forma; campos, patrones o estructuras de orden. Estos campos organizan no solo los campos de organismos vivos sino también de cristales y moléculas. Cada tipo de molécula, cada proteína por ejemplo, tiene su propio campo mórfico -un campo de hemoglobina, un campo de insulina, etc. De igual manera cada tipo de cristal, cada tipo de organismo, cada tipo de instinto o patrón de comportamiento tiene su campo mórfico. Estos campos son los que ordenan la naturaleza. Hay muchos tipos de campos porque hay muchos tipos de cosas y patrones en la naturaleza..."

Rupert Sheldrakebold  (Wikipedia, 20 de Octubre del 2008)



Teoría Sintérgica

Jacobo Grinberg Zylberbaum

“Nosotros interactuamos con una matriz informacional o campo informacional que todo lo abarca y envuelve y que contiene en cada una de sus porciones toda la información. Es una matriz de tipo holográfico. En ese nivel de cualidad de la experiencia no hay objetos separados unos de otros, sino que se trata de un extraordinario campo informacional de enorme complejidad.

Nuestro cerebro interactúa con ese campo informacional que algunos llaman campo cuántico y otros como David Böhm, el orden implicado. Los físicos actuales hablan de un campo espacial y la Teoría Sintergica de Grinberg la denomina campo sintérgico.” (Ligado, 20 de Octubre del 2008)



Rupert Sheldrake

“Sheldrake was born in Newark-on-Trent, Nottinghamshire and grew up there. He studied biochemistry at Clare College, Cambridge, graduating with a Double First-Class Honours degree. He was a Frank Knox fellow at Harvard, studying philosophy and history. He returned to Cambridge where he gained a PhD in biochemistry and was a Fellow at Clare College. He was a Research Fellow of the Royal Society and later went to Hyderabad, India where he was Principal Plant Physiologist at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT). For a year and a half he lived in the ashram of Bede Griffiths.” (Wikipedia October 20, 2008)


Rupert Sheldrake

“Los campos morfogenéticos o campos mórficos llevan información, no energía, y son utilizables a través del espacio y del tiempo sin perdida alguna de intensidad después de haber sido creados. Son campos no físicos que ejercen influencia sobre sistemas que presentan algún tipo de organización inherente.” (Wikipedia, 20 de Octubre del 2008)



Morfogénesis y emergencia de patrones en sistemas biológicos: del rompimiento de simetría a la autoorganización y la excitabilidad

Dr. Faustino Sánchez

“Este capítulo expone de manera conceptual algunos mecanismos subyacentes a la emergencia de patrones en sistemas biológicos, así mismo presenta su formulación matemática y algunos ejemplos.”



Dinámica inducida a través de resonancia espacial de patrones estacionarios de Turing

David Gómez Míguez

Memoria realizada en el Departamento de Física de Materia Condensada de la Universidad de Santiago de Compostela para optar al grado de Licenciado en Física. Mayo, 2002

Es sabido que multitud de sistemas descritos por ecuaciones no lineales (químicos, biológicos, ópticos, astrofísicos...), mantenidos lejos de su equilibrio ter-modinámico, pueden producir patrones estacionarios en medios espacialmente ex-tendidos, debido a una rotura de simetría, lo cual implica una diferencia en alguna de las propiedades del sistema si comparamos diferentes puntos espaciales del mismo.

Alan Turing [Turing, 1952] anticipó la idea de que bajo ciertas condiciones, dos procesos antagónicos, uno activador y otro inhibidor de la producción de uno de los reactivos de la reacción, pueden generar una rotura de simetría.

Turing A. M., [1952] “The Chemical Basis of Morphogenesis,” Philos. Trans. R





¿Pueden Pensar las Máquinas?

A.M. Turing, Computing Machinery and Intelligence

A.R. Anderson et alia (eds.), Mentes y máquinas (México: UNAM, 1974).


Por qué la gente piensa que los computadores no pueden

Marvin Minsky, "Why People Think Computers Can't", AI Magazine,1982.




Turing test

“The Turing test is a proposal for a test of a machine's ability to demonstrate intelligence. Described by Alan Turing in the 1950 paper "Computing Machinery and Intelligence," it proceeds as follows: a human judge engages in a natural language conversation with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test.” (Wikipedia October 23, 2008)


Prueba de Turing

“La prueba consiste en un desafío. Se supone un juez situado en una habitación, y una máquina y un ser humano en otras. El juez debe descubrir cuál es el ser humano y cuál es la máquina, estándoles a los dos permitidos mentir al contestar por escrito las preguntas que el juez les hiciera. La tesis de Turing es que si ambos jugadores eran suficientemente hábiles, el juez no podría distinguir quién era el ser humano y quién la máquina.” (Wikipedia, 23 de Octubre del 2008)


Premio Loebner en Inteligencia Artificial, un concurso de carácter anual que se celebra desde 1990 y que somete a varios ordenadores a la Prueba de Turing.

Home Page of The Loebner Prize in Artificial Intelligence

"The First Turing Test"

“The Loebner Prize for artificial intelligence ( AI ) is the first formal instantiation of a Turing Test.”


18th Annual Loebner Prize for Artificial Intelligence
12 October 2008
University of Reading, Reading, UK



A New Interpretation of the Turing Test

Diane Proudfoot

The Rutherford Journal, Volume 1, 2005-2006

The New Zealand Journal for the History and Philosophy of Science and Technology

The Turing Test and the Orthodox Interpretation

In ‘Computing Machinery and Intelligence’ (1950) Turing described an ‘imitation game’ played by three people, an interrogator and two interviewees, one male (A) and one female (B). The interrogator communicates with A and B from a separate room (by means of printed text and nowadays probably using a keyboard and screen); apart from this the three players have no contact with each other. The interrogator’s task is to find out, by asking questions, which of A and B is the man. A’s aim is that the interrogator decide wrongly. (Turing said, ‘The object of the game for the third player (B) is to help the interrogator. The best strategy for her is probably to give truthful answers.’ (1950, p. 434).)

In a second version of the game, a computer takes the part of A and a male or female the part of B.1 Now the interrogator’s task is to discover which of A and B is the computer; to do so he or she is permitted to ask any question, on any topic. The computer is allowed to do everything possible so that the interrogator makes the wrong identification. Turing said that the question ‘Can machines think?’ is ‘too meaningless to deserve discussion’ and proposed replacing it by the question ‘Are there imaginable digital computers which would do well in the [computer-imitates-human] game?’ (ibid., p. 442). The upshot of his famous proposal is: if some computer does well in the imitation game, the answer to (an appropriate substitute for) the question ‘Can machines think?’ is ‘yes’. Hence Turing offers a test of intelligence in machines.



UK university holds artificial intelligence test

By RAPHAEL G. SATTER,  READING, England Associated Press October 13, 2008



Breves explicaciones acerca de tópicos básicos de la inteligencia artificial





Constante de Chaitin

“La constante de Chaitin es un número entre 0 y 1. Es la probabilidad que un programa elegido al azar detenga correctamente a una máquina de Turing determinada.

Sea P el conjunto de todos los programas que se detienen, y |p| el tamaño en bits de un programa p, Ω está definida de la siguiente manera:


Esta constante no es computable. Es posible conocer los primeros decimales, pero a partir de cierto decimal (que depende de la codificación elegida) no es posible saber más decimales.”


Chaitin's constant

“In the computer science subfield of algorithmic information theory a Chaitin constant or halting probability is a real number that informally represents the probability that a randomly-chosen program will halt. These numbers are formed from a construction due to Gregory Chaitin.

Although there are infinitely many halting probabilities, it is common to use the letter Ω to refer to them as if there were only one. Because Ω depends on the program encoding used, it is sometimes called Chaitin's construction instead of Chaitin's constant when not referring to any specific encoding.

Each halting probability is a normal and transcendental real number which is definable but not computable, which means that there is no halting algorithm that enumerates its digits.”



Good–Turing frequency estimation

“Good–Turing frequency estimation is a statistical technique for predicting the probability of occurrence of objects belonging to an unknown number of species, given past observations of such objects and their species. (In drawing balls from an urn, the 'objects' would be balls and the 'species' would be the distinct colors of the balls (finite but unknown in number). After drawing Rred red balls, Rblack black balls and Rgreen green balls, we would ask what is the probability of drawing a red ball, a black ball, a green ball or one of a previously unseen color.)”


UCSD scientists explain and improve upon 'enigmatic' probability formula

Findings could have implications for speech recognition, machine learning, information retrieval

Contact: Doug Ramsey, San Diego, Oct. 16, 200

“Scientists at the University of California, San Diego (UCSD) have developed new insight into a formula that helped British cryptanalysts crack the German Enigma code in World War II. Writing in the Oct. 17 edition of the journal Science, UCSD Jacobs School of Engineering professor Alon Orlitsky and graduate students Narayana P. Santhanam and Junan Zhang shed light on a lingering mathematical mystery and propose a new solution that could help improve automatic speech recognition, natural language processing, and other machine learning software.

In the article, Orlitsky and his colleagues unlock some of the secrets of the "Good-Turing estimator," a formula for estimating the probability of elements based on observed data.”





Unorganized machine

From Wikipedia, the free encyclopedia (20121214)

“An unorganized machine is a concept mentioned in a far-sighted 1948 report in which Alan Turing suggested that the infant human cortex was what he called an "unorganized machine".[1][2] Turing defined the class of unorganized machines as largely random in their initial construction, but capable of being trained to perform particular tasks. Turing's unorganized machines were in fact very early examples of randomly-connected, binary neural networks, and Turing claimed that these were the simplest possible model of the nervous system.

Turing had been interested in the possibility of simulating neural systems for at least the previous two years. In correspondence with William Ross Ashby in 1946 he writes:

"I am more interested in the possibility of producing models of the action of the brain than in the applications to practical computing...although the brain may in fact operate by changing its neuron circuits by the growth of axons and dendrites, we could nevertheless make a model, within the ACE, in which this possibility was allowed for, but in which the actual construction of the ACE did not alter, but only the remembered data"”





ACE Computer


Automatic Computing Engine

From Wikipedia, the free encyclopedia (20121214)

Pilot ACE

“The Automatic Computing Engine (ACE) was an early electronic stored-program computer design produced by Alan Turing at the invitation of John R. Womersley, superintendent of the Mathematics Division of the National Physical Laboratory (NPL). The use of the word Engine was in homage to Charles Babbage and his Difference Engine and Analytical Engine. Turing's technical design Proposed Electronic Calculator was the product of his theoretical work in 1936 "On Computable Numbers"[1] and his wartime experience at Bletchley Park where the Colossus computers had been successful in breaking German military codes. In his 1936 paper, Turing described his idea as a "universal computing machine", but it is now known as the Universal Turing machine.”





Discovery of a Prototype Hollerith Machine in Paris

David Anderson, Janet Delve and Hans Pufal

The Rutherford Journal, Volume 1, 2005-2006

The New Zealand Journal for the History and Philosophy of Science and Technology


Probably the oldest surviving example of the Hollerith Tabulating Machine is to be found in Paris as part of the collection of the Conservatoire des Arts et Métiers (CNAM). Unfortunately Hollerith’s device is not on general display but is in the CNAM reserves stored on a pallet at a height of almost 2 metres above floor level making examination on a first visit quite difficult.





Researchers Developing Alan Turing Artificial Intelligence 'Gooware' (12/07/12)

”University of the West of England researchers have made progress in building a "gooware" computer, based on Alan Turing’s idea of "unorganized machines." Turing argued the simplest form of an unorganized machine would be a randomly connected network of NAND logic gates, known as A-type machines. The West of England researchers are focusing on an unusual group of chemical reactions called Belousov-Zhabotinsky (BZ) reactions. These reactions do not reach a stable equilibrium point, and subtle stimuli can produce patterns in a normally calm mixture. The waves of chemical changes in the BZ reaction are used as the basis of transferring information. A processor made from a BZ reaction could potentially move information in any direction and handle more data than a traditional computer. The researchers have also shown that they can use their BZ NAND gate as the basis of an A-type unorganized machine. "It was then shown how a number of well-known benchmark logic gates can be designed from A-type unorganized machines using an approach inspired by a comment from Turing on cultural search," according to the researchers.”

ACM TechNews, Friday, December 14, 2012


Researchers developing Alan Turing artificial intelligence 'gooware'

07 Dec 2012





Algoritmos Evolutivos, Curso 2009

Centro de Cálculo, Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Uruguay.

“Los programas “autoreplicables” y “evolutivos” fueron sugeridos desde los inicios de la era de la computación.

Alan Turing

Neumann János

Reseña histórica

• Las primeras ideas se formulan entre 1948 y 1960.

• Alan Turing investigó las relaciones sobre evolución natural y aprendizaje.

Propuso el desarrollo de programas automodificables, capaces de jugar ajedrez y simular otras actividades inteligentes sencillas, utilizando técnicas evolutivas.

• Neumann János trabajó sobre autómatas celulares evolutivos.

Propuso mecanismos evolutivos para implementar autómatas con poder computacional equivalente a una máquina de Turing.

Conjeturó sobre poblaciones de autómatas trabajando cooperativamente y comunicándose entre sí. (“Teoría de autómatas autorreplicables”, texto inconcluso, 1966).”





Bletchley Park's Sturgeon, the Fish that Laid No Eggs

Frode Weierud

The Rutherford Journal, Volume 1, 2005-2006

The New Zealand Journal for the History and Philosophy of Science and Technology


The German armed forces employed three different types of teleprinter cipher machines during the Second World War, the Lorenz machines SZ40 and SZ42 also called Tunny by Bletchley Park (BP), the Siemens & Halske Schlüsselfernschreibmaschine (SFM) T52, and the one-time-tape machine T43, also manufactured by Siemens.1 The Lorenz machines, which existed in three different models, SZ40, SZ42a, and SZ42b, are well known as the machines that were broken at BP with the aid of Colossus. The Siemens T52 existed in four functionally distinct models, T52a/b, T52c and T52ca - which was a modified version of the T52c machine, T52d, and T52e, all going under the BP code name of Sturgeon, while the Siemens T43 probably was the unbreakable machine that BP called Thrasher. The T43 machine came into use relatively late in the war and appears to have been used only on a few selected circuits.

This paper will explain in detail the events that led to BP breaking the Sturgeon machines. In 1964, the Swedish Under-Secretary of State Erik Boheman first revealed that Sweden had broken the German Geheimschreiber (T52) during the Second World War. In 1967, David Kahn gave further details about this achievement. However, it was only in 1984, when Hinsley et al. published part one of the third volume of "British Intelligence in the Second World War," that it was officially acknowledged that BP also had experienced some success against the Siemens T52. Previously, many authors had confused the T52 with the Lorenz SZ40/42 machines and had erroneously linked the Siemens T52 to Colossus. Since 1982, Donald Davies has published detailed information about the electrical and mechanical construction of the machines. And Wolfgang Mache has through his contacts and interviews with former Geheimschreiber operators and technicians presented the evolutionary history of the Siemens T52 machines. Apart from Sir Harry Hinsley's and Professor Tutte's references to BP's attack against the T52 there had not been any detailed account of this part of BP's history before an earlier, shorter version of this paper was published in 2000.



The Turing Bombe

Frank Carter

The Rutherford Journal, Volume 3, 2010

The New Zealand Journal for the History and Philosophy of Science and Technology



Colossus: Breaking the German ‘Tunny’ Code at Bletchley Park. An Illustrated History

B. Jack Copeland

The Rutherford Journal, Volume 3, 2010

The New Zealand Journal for the History and Philosophy of Science and Technology





The ACM A.M. Turing Award is ACM's most prestigious technical award. It recognizes contributions of lasting and major technical importance, and honors


Turing Award

From Wikipedia, the free encyclopedia


ACM Turing Award

Awarded for

Outstanding contributions in Computer science

Presented by

Association for Computing Machinery (ACM)


New York, (United States)


US $250,000

First awarded


Last awarded


Official website

ACM List of Turing Laureates

The ACM A.M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) to "an individual selected for contributions of a technical nature made to the computing community". It is stipulated that "The contributions should be of lasting and major technical importance to the computer field".[1] The Turing Award is recognized as the "highest distinction in Computer science"[2] and "Nobel Prize of computing".[3]





The Mathematic Model of Consciousness

Zhang Yinsheng

AMS '08: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) - Volume 00 , Volume 00, May 2008

Publisher: IEEE Computer Society


“The thought referring homomorphism of a group to the model of transformation from physical structures to the psychological originated from Jean Piaget. How ever, Jean Piaget has not given a mathematic expression by which the psychological phenomena can be represented, and he has not taking the model as the common model of consciousness. In the paper, the homomorphic model of consciousness is given in a mathematic expression, by which the some important questions relevant to the consciousness are formalized and the answer can be acquired, the questions (with the followed answer solved by the homomorphic model of consciousness) are Chinese room argument, Turing machine question, Turing Test question, and about if animals have consciousness, in what the meaning of utilizing and making tools lies, if DNA and protein system have consciousness. And finally the paper concludes that consciousness exists when : (1) the units of system certainly map the entities outside the system; (2) the system operate by some rules; and (3) the rules certainly map some laws or relations in the entities. The essence of consciousness is certain correspondence between substance with some rules or laws.”




Book reivew: Consciousness Reconsidered by Owen Flanagan (The MIT Press. 1992)

Joseph O'Rourke

SIGART Bulletin , Volume 4 Issue 3, July 1993

Publisher: ACM



“Design and manufacturing play a crucial role in the wealth creation of nations and their use of computer technology forms an important element in the process. Much has been written on the application of AI to design and manufacturing [1,2,3,5,7] in which AI has been touted to be able to increase the effectiveness and sophistication of computer applications in these areas.”


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