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Documentacion sobre Turing
- What von Neumann knew of Turing, 1937-39
- Alan Turing’s zeta-function machine, 1939
- Enigma report, November 1939 (transcript), announcing Turing ‘superbombe’
- Alan Turing’s mission to France, January 1940
- Pages from Turing’s report on the Enigma, (the Prof’s Book), 1940
- Operation Ruthless, October 1940
- Alan Turing’s report on the U. S. Navy cryptographic section, Washington, November 1942 (transcript)
- Alan Turing’s report: Visit to National Cash Register Corporation of Dayton, Ohio, December 1942
- Alan Turing’s progress report on the Delilah speech scrambler, June 1944 (transcript)
- Discussion on the Mind and the Computing Machine, October 1949
- Conference on Information Theory, September 1950
La Manzana de Turing
Artículo publicado el domingo, 26 de Marzo de 2000 en El Mercurio, Cuerpo E, Artes y Letras
Por Eric Goles, matemático, Presidente de la Comisión Nacional de Investigación Científica y Tecnológica, CONICYT, Premio Nacional de Ciencias Exactas 1993
Los funcionarios policiales inventariaban con prisa los restos de una manzana, algunas hojas cubiertas de diagramas y ecuaciones, un ajado ar-tículo de la revista Mind y la fotografía de un tigre. Alguien raspó la comisura de los labios antes de que retiraran el cuerpo. La pieza quedó entonces abandonada a esa atmósfera sin mañana que dejan los muertos. Pero aquel muerto dejaba una hebra que porfiadamente sigue incrustándose en la trama de la humanidad, desde una isla y un país donde la niebla no dejaba ver el cielo, el espejo también era una puerta y la maravilla el nombre de un libro.
En ese país vivía un hombre con voz de gaita y un desenfoque total en la mirada. Un hombre que cada mañana se amarraba el botapié con un alambre y en una vieja bicicleta se dirigía a su oficina. No veía la ruta, su cuerpo asumía la tarea. Sólo daba curso a sus obsesiones, desplegándolas contra el paisaje. No aquel que recorrían cuerpo y bicicleta sino el otro, su paisaje. La brizna de una frase en inglés, una palabra alemana impronunciable, entscheidungsproblem, ecuaciones, filas desordenadas de números y una geometría de trazos de imaginaria luz detrás de los ojos. Un continuo, incluyendo trayecto, la pizarra donde trataba de mapear esa geografía interior, una partida de ajedrez para el afán de la tarde y la manzana que comía al abandonarse cada noche. Escenario sin cuajar, pensamiento en embrión, todavía una posibilidad. Nada de esto era útil, sólo imprescindible.
El Juego de las imitaciones
Singular ese señor, excéntrico, difícil, algo desagradable sin duda, aunque la mayoría lo admiraba y unos pocos lo querían. Su nombre era Turing, Alan Mathison Turing y, entre otras cosas, era matemático.
En realidad uno de los más brillantes de aquella isla y también del planeta. Había asumido la educación que en aquel país era la norma, escuela pública que también era privada. Había descubierto, acaso sin drama, su homosexualidad y la aparición de un primer amor hacia un condiscípulo, el cual nunca conoció de esa pasión ni el devenir del joven Turing. Simplemente murió, como se morían en ese país en aquel tiempo, sin aspaviento, en la semilla de la edad.
Así crecía Turing, desde aquella cicatriz, en un imperio que era y no era suyo, que atraía y repelía o castigaba para ajustar al molde. Defendiéndose, perdiendo todos sus latines y arrasando en su primera lengua: matemática en estado químico puro. Ojo para la sorpresa desde adentro, curiosidad y asombro para inventar el mundo.
Y estaba enfermo, contagiado hasta el tuétano con un virus que llamaremos Godel. Otro matemático, ya de regreso con un inquietante resultado: cualquier sistema de axiomas, de reglas podríamos decir, que incluyese la aritmética, contenía proposiciones verdaderas que eran indemostrables a partir de los axiomas. Entonces no bastaban las reglas para asegurar el juego. Aparecía un delicioso espacio de ambigüedad y la matemática era asimilable a una religión que había demostrado su necesidad de fe.
Acaso Turing vio allí la manera de sustraerse al molde, a la ciudad y la isla. De jugar en el filo del borde el otro que ya era. Preguntándose sin tregua, hasta que le hirvieran las meninges, sobre la existencia de un procedimiento mecánico para averiguar, en un tiempo finito, la verdad o falsedad de cualquier afirmación matemática. Y divagó, fracasó, dejó y retomó la tarea. Intentó todos los ardides más uno para llenar esa interrogante y aún fue insuficiente.
Alan Turing, llamó “máquina universal” al resultado de su ingenio y lo enfrentó a una pregunta endemoniada: si se detendría cuando estaba imitándose a sí misma.
Aunque siguió borroneando cuadernos de aritmética. Pequeños cuadrados dispuestos sobre la superficie de la hoja. En blanco todavía o intervenidos con número o letra, pensó, viéndose desde arriba. Encaramándose encima del espacio de su única obsesión, espiándose. El, allá abajo, insignificante. Mano empuñando el lápiz, desplazándose sobre el papel, cuadro a cuadro. Pensando sobre lo leído, anotando otro símbolo. Reiterando esas acciones. Casi automáticas, casi mecánicas… una máquina de calcular. Allí estaba el hilo que deshacía el laberinto. Esa rutina contenía la esencia del cálculo.
Contra insomnio y ansiedad vino el resto: la descripción formal de la máquina. Validó su construcción demostrando que ésta era capaz de calcular todo aquello que podía ser descrito sin ambigüedad.
Había diseñado, sin saberlo todavía, el esqueleto lógico del computador y la programación.
Sin embargo, este avance no era suficiente para lo que pretendía, así que ideó un programa que imitaba cualquier máquina. El monstruo resultante lo llamó máquina universal y lo enfrentó a una pregunta endemoniada: si se detendría cuando estaba imitándose a sí misma. Turing contra el Turing del espejo, persiguiendo cada uno de sus movimientos, perdiéndose en esa dinámica de simulaciones, sin saber quién remedaba a quién.
Demostró entonces que ese juego de espejos se escapaba. No había respuesta, no era posible negar o afirmar, simplemente era indecidible.
De esta manera, ponía en irrefutable evidencia la imposibilidad de un procedimiento mecánico para informarnos sobre la verdad de una sentencia matemática arbitraria. Entonces, sólo entonces, puso los pies sobre el escritorio, mascó la cotidiana manzana de sus noches y en un infinitesimal instante comprendió que había creado una rosa más eterna que la rosa y durmió.
El Enigma y la Clave
Despertó en la víspera de una pesadilla planetaria, la guerra, trayendo variados asombros: una plural noción del deshonor, la muerte industrial, soluciones finales y el resplandor del fósforo sobre las ciudades nocturnas.
A Turing le trajo otro problema, descifrar la clave de comunicación del enemigo en la segunda guerra mundial.
“Eso es tarea de matemáticos”, habría indicado un odioso coronel a cargo del laboratorio secreto, escrutándolo con algún desprecio. Había pues otra obsesión para no darse cuenta y visitar al reverendo Dogson al otro lado del espejo.
La clave enemiga se determinaba mediante una pequeña máquina hecha de cilindros superpuestos con letras y símbolos. Cada día, la posición de esos cilindros permitía que el sentido se disfrazara de aparente caos. No bastaba con decodificar un mensaje particular. Había que meterse en esa lógica mecanizada, en esa cábala de círculos concéntricos que recordaba al iluminado catalán del siglo trece, Raimundo Lulio.
Turing ocurrió en ese problema, se alimentó de él, lo dejó crecer, convirtiéndolo en una pesadilla personal que opacó la colectiva. Atado por sus propios fantasmas regresó a la máquina universal y, por segunda vez, al juego de las imitaciones. Ahora se trataba de imitar aquella mano que giraba los cilindros al otro lado del mar, construir un artefacto que fuese la sombra de aquel enigma. Pidió ingenieros y la mejor tecnología disponible para interpolar en la realidad una primera aproximación de sus sueños.
Bautizaron Coloso a ese espejo del artificio enemigo y la guerra desde entonces tuvo menos tiempo para su infamia.
Máquinas y Mentes
Aunque su cuerpo todavía deambulaba por ese laboratorio, ahora, ya resuelto el problema, él no estaba ahí, seguía en otra. Intuía algo más radical y vasto que un aparato destinado a descifrar. Quería construir la máquina universal. No le creyeron, había cosas más importantes, más inmediatas, más útiles.
Además, Turing, el desastrado marginal, era impresentable en las audiencias de los ministerios, en las mesas donde se entregaban los créditos. Pero tenía razón, profunda certeza, y en otro país, otro matemático, John von Neuman, que sabía de esos sueños y que usaba, además de su ciencia, la corbata donde se debía, emprendió la construcción de esa máquina y la llamó computador.
Turing intuía algo más radical y vasto que un aparato destinado a descifrar. Quería construir la máquina universal.
No le creyeron. Pero tenía razón, profunda certeza, y en otro país, otro matemático, John von Neuman, que sabía de esos sueños y que usaba, además de su ciencia, la corbata donde se debía, emprendió la construcción de esa máquina y la llamó computador.
En la isla ahora tenían un motivo serio para continuar el proyecto inconcluso, debía imitarse al imitador de ultramar. Sin embargo, Turing quería utilizarla para emular al hacedor de fantasías, el hombre. Para ello, menos que la complejidad del programa, necesitaba un criterio, una definición de mente o inteligencia. Discurre entonces que, al conversar o escribirse con alguien, estas cualidades del otro aparecían sólo al compararla con las propias.
¿Por qué debería ser diferente el criterio frente a una máquina?
Luego, la inteligencia de ésta dependería de la percepción del observador.
Emerge entonces el tercer juego de imitación, el test de Turing.
Una persona dialoga a través de un teletipo con alguien o algo. Si concluye que el interlocutor es un ser humano y se trataba de una máquina, entonces esta última es inteligente. Aventura entonces que en ciertos dominios restringidos, como el ajedrez, el test sería superado antes de fines de siglo. Entre otros engaños parciales Kasparov y Azul Profundo lo corroboran.
El Oro de los Tigres
Cuando todo parecía resolverse, otra vez todo se planteaba. No era suficiente aquella maquinaria ni el test. No era suficiente programarla, como lo hizo, para escribir cartas de amor dirigidas al que no tenía ya quien le escribiera y recorría esa playa virtual esperando noticias de nadie, piadosas mentiras digitales que botaba la ola. Entonces, el porfiado Turing se lanza contra la vida misma. Porque tal vez letra y número constituyan los ingredientes de un juego infinitamente más vasto y las reglas de ese juego se escriban con palabras y las palabras, combinadas de particular manera, crean la complejidad del universo, se entrega al enigma que se pasea en la raya y el oro de los tigres o la pigmentación de la concha de un molusco. Se enfrentaba así a uno de los problemas centrales del desarrollo de lo viviente, la morfogénesis.
Cómo, a partir de la igualdad, emerge lo distinto.
Así, en certeras ecuaciones da cuenta del cambio, la diferenciación de forma o textura del organismo que crece. Sin embargo, ya no tenía tiempo. Aunque esta última teoría es de profunda actualidad y no menos importante que sus aportes a la lógica y la computación, el marginal matemático no alcanzó a estar ahí para comprobarlo. Un día arbitrario se le cruzó la isla entera en su camino y acabó con él. La circunstancia es miserable. Triste emergencia de la oscuridad que nos habita y que tanto iluminó este hombre especial. Turing es denunciado por un ocasional amante de comercio homosexual y condenado a un tratamiento hormonal que arrasaba con cuerpo, paisaje interior y la mirada aquella que recreaba lo cotidiano.
Y como es demasiado duro percatarse que se está siendo cada vez menos de lo que se es, la noche del siete de Junio de 1954 Alan Mathison Turing, entrando ya al país de los cuentos, inyecta una manzana con cianuro y la va mascando, quizás con la parsimonia necesaria para conjeturar que esa fruta que nos persigue desde el comienzo del verbo sería el perdido talismán para abrir o inventar un justo, sólo justo, pa-raíso.
Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.
COMPUTING MACHINERY AND INTELLIGENCE
By A. M. Turing
“El inglés Alan Turing (1912-1954) puede ser considerado el padre de la Inteligencia Artificial (IA), aunque este nombre no se usase hasta después de 1956. Turing estudió lógica matemática en la Universidad de Cambridge y en 1937 estuvo en el Institute for Advanced Studies de Princeton, donde estaban Gödel y Von Newman, entre otros destacados lógicos y matemáticos, además de Albert Einstein Durante la segunda guerra mundial trabajó para su país en los servicios de información; en 1949 en la Universidad de Manchester y en el programa MADAM (Manchester Authomatic Digital Machine) que resultó ser el equipo de computación de mayor memoria construido hasta entonces. Condenado a causa de su homosexualidad a un tratamiento, o tortura, médico-farmacéutica equivalente a la castración, Turing se suicidó por envenenamiento en 1954.” (Enric Trillas50-51)
“Durante los años de la segunda guerra mundial, Turing colaboró en el diseño de una máquina llamada la ‘Bomba’ que exploraba las combinaciones posibles generadas por la máquina codificadora alemana ‘Enigma’. Tal ‘Bomba’ fue una máquina de propósito especial, el de descifrar códigos, construida electromecánicamente con relés. Asimismo, trabajó en el desarrollo de la ‘Colossus’ (que algunos consideran como el primer ordenador electrónico) que ya funcionaba con válvulas (tubos de vacío) en lugar de relés; gracias a ella los británicos pudieron mantener alejados de los submarinos alemanes a los barcos de suministro que cruzaban el Atlántico… Turing no recibió en vida reconocimiento alguno de la sociedad a la que tanto ayudó en los momentos más difíciles.” (Enric Trillas51)
“Más sorprendente todavía es que Turing demostró que para cualquier sistema de sus máquinas que sea necesario para efectuar algoritmos cada vez más complicados existe una máquina de Turing capaz de hacerlo todo ella sola. Tal máquina hipotética recibe el nombre de ‘máquina de Turing universal’, y su existencia teórica pone de manifiesto que el concepto de máquina de Turing es de una versatilidad sin fin, al permitir que cualquier incremento de la complejidad del algoritmo pueda ser aceptado por una lista más larga de especificaciones… Los ordenadores actuales son realizaciones de las ideas de John von Newmann y de Alan Turing.” (Enric Trillas53)
Turing “en 1950 propuso una prueba que se conoce como el ‘test de Turing’, el cual se basa en la idea siguiente: si una persona se comunica sólo a través de un terminal con otras dos partes, que están escondidas,, y no se puede discriminar a través de preguntas cuál de ambas partes es una persona y cuál es un ordenador, entonces no se puede negar que la máquina muestra la cualidad que, en las personas, se llama ‘inteligencia’. Tal procedimiento tiene la ventaja de no tener que definir lo que es la inteligencia. Turing creía firmemente que máquinas que piensen llegarían a existir y predijo que hacia el año 2000 una máquina jugaría al ‘juego de imitación’, como él llamó al test, de manera que un interrogador medio no tendría más del 70 por 100 de posibilidades de efectuar la identificación correcta tras cinco minutos de preguntas.” (Enric Trillas55)
En el desarrollo de la computadora, la teoría antecedió a la práctica. El manifiesto del nuevo orden electrónico de cosas fue un trabajo (“On Computable Numbers” -Sobre números calculables-) publicado en 1936, por el matemático y lógico A.M.Turing, el cual determinó la naturaleza y las limitaciones teóricas de las máquinas lógicas antes de que se construyera siquiera una sencilla computadora por completo programable.(Bolter17)
Turing… en 1950 publicó “Computing Machinery and Intelligence”… expresó su convicción de que las computadoras eran capaces de imitar perfectamente la inteligencia humana y que tal hazaña la realizarían hacia el año 2000.
Al prometer (o al amenazar) sustituir al hombre, la computadora nos ofrece una nueva definición de hombre, como “procesador de información”, y de naturaleza, como “información que debe ser procesada”.(Bolter18)
“En 1936 Turing concibió su propio autómata imaginario. La máquina de Turing, como se le llegó a conocer, no hizo intento alguno para unirse a la sociedad de las criaturas vivas. Podría visualizarse más como un tocacintas muy sofisticado con una cinta arbitrariamente infinita.
“Siendo una Máquina de Estados Finitos, se podría concebir como un autómata finito”.(Lévy 22-23)
He aqui el gran texto de Alan Turing. Enjoy!
1. The Imitation Game
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.” The interrogator is allowed to put questions to A and B thus:
C: Will X please tell me the length of his or her hair?
Now suppose X is actually A, then A must answer. It is A’s object in the game to try and cause C to make the wrong identification. His answer might therefore be:
“My hair is shingled, and the longest strands are about nine inches long.”
In order that tones of voice may not help the interrogator the answers should be written, or better still, typewritten. The ideal arrangement is to have a teleprinter communicating between the two rooms. Alternatively the question and answers can be repeated by an intermediary. 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. She can add such things as “I am the woman, don’t listen to him!” to her answers, but it will avail nothing as the man can make similar remarks.
We now ask the question, “What will happen when a machine takes the part of A in this game?” Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?
These questions replace our original, “Can machines think?”
2. Critique of the New Problem
As well as asking, “What is the answer to this new form of the question,” one may ask, “Is this new question a worthy one to investigate?” This latter question we investigate without further ado, thereby cutting short an infinite regress.
The new problem has the advantage of drawing a fairly sharp line between the physical and the intellectual capacities of a man. No engineer or chemist claims to be able to produce a material which is indistinguishable from the human skin. It is possible that at some time this might be done, but even supposing this invention available we should feel there was little point in trying to make a “thinking machine” more human by dressing it up in such artificial flesh. The form in which we have set the problem reflects this fact in the condition which prevents the interrogator from seeing or touching the other competitors, or hearing -their voices. Some other advantages of the proposed criterion may be shown up by specimen questions and answers. Thus:
Q: Please write me a sonnet on the subject of the Forth Bridge.
A : Count me out on this one. I never could write poetry.
Q: Add 34957 to 70764.
A: (Pause about 30 seconds and then give as answer) 105621.
Q: Do you play chess?
Q: I have K at my K1, and no other pieces. You have only K at K6 and R at R1. It is your move. What do you play?
A: (After a pause of 15 seconds) R-R8 mate.
The question and answer method seems to be suitable for introducing almost any one of the fields of human endeavour that we wish to include. We do not wish to penalise the machine for its inability to shine in beauty competitions, nor to penalise a man for losing in a race against an aeroplane. The conditions of our game make these disabilities irrelevant. The “witnesses” can brag, if they consider it advisable, as much as they please about their charms, strength or heroism, but the interrogator cannot demand practical demonstrations.
The game may perhaps be criticised on the ground that the odds are weighted too heavily against the machine. If the man were to try and pretend to be the machine he would clearly make a very poor showing. He would be given away at once by slowness and inaccuracy in arithmetic. May not machines carry out something which ought to be described as thinking but which is very different from what a man does? This objection is a very strong one, but at least we can say that if, nevertheless, a machine can be constructed to play the imitation game satisfactorily, we need not be troubled by this objection.
It might be urged that when playing the “imitation game” the best strategy for the machine may possibly be something other than imitation of the behaviour of a man. This may be, but I think it is unlikely that there is any great effect of this kind. In any case there is no intention to investigate here the theory of the game, and it will be assumed that the best strategy is to try to provide answers that would naturally be given by a man.
3. The Machines Concerned in the Game
The question which we put in 1 will not be quite definite until we have specified what we mean by the word “machine.” It is natural that we should wish to permit every kind of engineering technique to be used in our machines. We also wish to allow the possibility than an engineer or team of engineers may construct a machine which works, but whose manner of operation cannot be satisfactorily described by its constructors because they have applied a method which is largely experimental. Finally, we wish to exclude from the machines men born in the usual manner. It is difficult to frame the definitions so as to satisfy these three conditions. One might for instance insist that the team of engineers should be all of one sex, but this would not really be satisfactory, for it is probably possible to rear a complete individual from a single cell of the skin (say) of a man. To do so would be a feat of biological technique deserving of the very highest praise, but we would not be inclined to regard it as a case of “constructing a thinking machine.” This prompts us to abandon the requirement that every kind of technique should be permitted. We are the more ready to do so in view of the fact that the present interest in “thinking machines” has been aroused by a particular kind of machine, usually called an “electronic computer” or “digital computer.” Following this suggestion we only permit digital computers to take part in our game.
This restriction appears at first sight to be a very drastic one. I shall attempt to show that it is not so in reality. To do this necessitates a short account of the nature and properties of these computers.
It may also be said that this identification of machines with digital computers, like our criterion for “thinking,” will only be unsatisfactory if (contrary to my belief), it turns out that digital computers are unable to give a good showing in the game.
There are already a number of digital computers in working order, and it may be asked, “Why not try the experiment straight away? It would be easy to satisfy the conditions of the game. A number of interrogators could be used, and statistics compiled to show how often the right identification was given.” The short answer is that we are not asking whether all digital computers would do well in the game nor whether the computers at present available would do well, but whether there are imaginable computers which would do well. But this is only the short answer. We shall see this question in a different light later.
4. Digital Computers
The idea behind digital computers may be explained by saying that these machines are intended to carry out any operations which could be done by a human computer. The human computer is supposed to be following fixed rules; he has no authority to deviate from them in any detail. We may suppose that these rules are supplied in a book, which is altered whenever he is put on to a new job. He has also an unlimited supply of paper on which he does his calculations. He may also do his multiplications and additions on a “desk machine,” but this is not important.
If we use the above explanation as a definition we shall be in danger of circularity of argument. We avoid this by giving an outline. of the means by which the desired effect is achieved. A digital computer can usually be regarded as consisting of three parts:
(ii) Executive unit.
The store is a store of information, and corresponds to the human computer’s paper, whether this is the paper on which he does his calculations or that on which his book of rules is printed. In so far as the human computer does calculations in his bead a part of the store will correspond to his memory.
The executive unit is the part which carries out the various individual operations involved in a calculation. What these individual operations are will vary from machine to machine. Usually fairly lengthy operations can be done such as “Multiply 3540675445 by 7076345687” but in some machines only very simple ones such as “Write down 0” are possible.
We have mentioned that the “book of rules” supplied to the computer is replaced in the machine by a part of the store. It is then called the “table of instructions.” It is the duty of the control to see that these instructions are obeyed correctly and in the right order. The control is so constructed that this necessarily happens.
The information in the store is usually broken up into packets of moderately small size. In one machine, for instance, a packet might consist of ten decimal digits. Numbers are assigned to the parts of the store in which the various packets of information are stored, in some systematic manner. A typical instruction might say-
“Add the number stored in position 6809 to that in 4302 and put the result back into the latter storage position.”
Needless to say it would not occur in the machine expressed in English. It would more likely be coded in a form such as 6809430217. Here 17 says which of various possible operations is to be performed on the two numbers. In this case the)e operation is that described above, viz., “Add the number. . . .” It will be noticed that the instruction takes up 10 digits and so forms one packet of information, very conveniently. The control will normally take the instructions to be obeyed in the order of the positions in which they are stored, but occasionally an instruction such as
“Now obey the instruction stored in position 5606, and continue from there”
may be encountered, or agaiN
“If position 4505 contains 0 obey next the instruction stored in 6707, otherwise continue straight on.”
Instructions of these latter types are very important because they make it possible for a sequence of operations to be replaced over and over again until some condition is fulfilled, but in doing so to obey, not fresh instructions on each repetition, but the same ones over and over again. To take a domestic analogy. Suppose Mother wants Tommy to call at the cobbler’s every morning on his way to school to see if her shoes are done, she can ask him afresh every morning. Alternatively she can stick up a notice once and for all in the hall which he will see when he leaves for school and which tells him to call for the shoes, and also to destroy the notice when he comes back if he has the shoes with him.
The reader must accept it as a fact that digital computers can be constructed, and indeed have been constructed, according to the principles we have described, and that they can in fact mimic the actions of a human computer very closely.
The book of rules which we have described our human computer as using is of course a convenient fiction. Actual human computers really remember what they have got to do. If one wants to make a machine mimic the behaviour of the human computer in some complex operation one has to ask him how it is done, and then translate the answer into the form of an instruction table. Constructing instruction tables is usually described as “programming.” To “programme a machine to carry out the operation A” means to put the appropriate instruction table into the machine so that it will do A.
An interesting variant on the idea of a digital computer is a “digital computer with a random element.” These have instructions involving the throwing of a die or some equivalent electronic process; one such instruction might for instance be, “Throw the die and put the-resulting number into store 1000.” Sometimes such a machine is described as having free will (though I would not use this phrase myself), It is not normally possible to determine from observing a machine whether it has a random element, for a similar effect can be produced by such devices as making the choices depend on the digits of the decimal for .
Most actual digital computers have only a finite store. There is no theoretical difficulty in the idea of a computer with an unlimited store. Of course only a finite part can have been used at any one time. Likewise only a finite amount can have been constructed, but we can imagine more and more being added as required. Such computers have special theoretical interest and will be called infinitive capacity computers.
The idea of a digital computer is an old one. Charles Babbage, Lucasian Professor of Mathematics at Cambridge from 1828 to 1839, planned such a machine, called the Analytical Engine, but it was never completed. Although Babbage had all the essential ideas, his machine was not at that time such a very attractive prospect. The speed which would have been available would be definitely faster than a human computer but something like I 00 times slower than the Manchester machine, itself one of the slower of the modern machines, The storage was to be purely mechanical, using wheels and cards.
The fact that Babbage’s Analytical Engine was to be entirely mechanical will help us to rid ourselves of a superstition. Importance is often attached to the fact that modern digital computers are electrical, and that the nervous system also is electrical. Since Babbage’s machine was not electrical, and since all digital computers are in a sense equivalent, we see that this use of electricity cannot be of theoretical importance. Of course electricity usually comes in where fast signalling is concerned, so that it is not surprising that we find it in both these connections. In the nervous system chemical phenomena are at least as important as electrical. In certain computers the storage system is mainly acoustic. The feature of using electricity is thus seen to be only a very superficial similarity. If we wish to find such similarities we should took rather for mathematical analogies of function.
5. Universality of Digital Computers
The digital computers considered in the last section may be classified amongst the “discrete-state machines.” These are the machines which move by sudden jumps or clicks from one quite definite state to another. These states are sufficiently different for the possibility of confusion between them to be ignored. Strictly speaking there, are no such machines. Everything really moves continuously. But there are many kinds of machine which can profitably be thought of as being discrete-state machines. For instance in considering the switches for a lighting system it is a convenient fiction that each switch must be definitely on or definitely off. There must be intermediate positions, but for most purposes we can forget about them. As an example of a discrete-state machine we might consider a wheel which clicks round through 120 once a second, but may be stopped by a ]ever which can be operated from outside; in addition a lamp is to light in one of the positions of the wheel. This machine could be described abstractly as follows. The internal state of the machine (which is described by the position of the wheel) may be q1, q2 or q3. There is an input signal i0. or i1 (position of ]ever). The internal state at any moment is determined by the last state and input signal according to the table
The output signals, the only externally visible indication of the internal state (the light) are described by the table
State q1 q2 q3
output o0 o0 o1
This example is typical of discrete-state machines. They can be described by such tables provided they have only a finite number of possible states.
It will seem that given the initial state of the machine and the input signals it is always possible to predict all future states, This is reminiscent of Laplace’s view that from the complete state of the universe at one moment of time, as described by the positions and velocities of all particles, it should be possible to predict all future states. The prediction which we are considering is, however, rather nearer to practicability than that considered by Laplace. The system of the “universe as a whole” is such that quite small errors in the initial conditions can have an overwhelming effect at a later time. The displacement of a single electron by a billionth of a centimetre at one moment might make the difference between a man being killed by an avalanche a year later, or escaping. It is an essential property of the mechanical systems which we have called “discrete-state machines” that this phenomenon does not occur. Even when we consider the actual physical machines instead of the idealised machines, reasonably accurate knowledge of the state at one moment yields reasonably accurate knowledge any number of steps later.
As we have mentioned, digital computers fall within the class of discrete-state machines. But the number of states of which such a machine is capable is usually enormously large. For instance, the number for the machine now working at Manchester is about 2 165,000, i.e., about 10 50,000. Compare this with our example of the clicking wheel described above, which had three states. It is not difficult to see why the number of states should be so immense. The computer includes a store corresponding to the paper used by a human computer. It must be possible to write into the store any one of the combinations of symbols which might have been written on the paper. For simplicity suppose that only digits from 0 to 9 are used as symbols. Variations in handwriting are ignored. Suppose the computer is allowed 100 sheets of paper each containing 50 lines each with room for 30 digits. Then the number of states is 10 100x50x30 i.e., 10 150,000 . This is about the number of states of three Manchester machines put together. The logarithm to the base two of the number of states is usually called the “storage capacity” of the machine. Thus the Manchester machine has a storage capacity of about 165,000 and the wheel machine of our example about 1.6. If two machines are put together their capacities must be added to obtain the capacity of the resultant machine. This leads to the possibility of statements such as “The Manchester machine contains 64 magnetic tracks each with a capacity of 2560, eight electronic tubes with a capacity of 1280. Miscellaneous storage amounts to about 300 making a total of 174,380.”
Given the table corresponding to a discrete-state machine it is possible to predict what it will do. There is no reason why this calculation should not be carried out by means of a digital computer. Provided it could be carried out sufficiently quickly the digital computer could mimic the behavior of any discrete-state machine. The imitation game could then be played with the machine in question (as B) and the mimicking digital computer (as A) and the interrogator would be unable to distinguish them. Of course the digital computer must have an adequate storage capacity as well as working sufficiently fast. Moreover, it must be programmed afresh for each new machine which it is desired to mimic.
This special property of digital computers, that they can mimic any discrete-state machine, is described by saying that they are universal machines. The existence of machines with this property has the important consequence that, considerations of speed apart, it is unnecessary to design various new machines to do various computing processes. They can all be done with one digital computer, suitably programmed for each case. It ‘ill be seen that as a consequence of this all digital computers are in a sense equivalent.
We may now consider again the point raised at the end of §3. It was suggested tentatively that the question, “Can machines think?” should be replaced by “Are there imaginable digital computers which would do well in the imitation game?” If we wish we can make this superficially more general and ask “Are there discrete-state machines which would do well?” But in view of the universality property we see that either of these questions is equivalent to this, “Let us fix our attention on one particular digital computer C. Is it true that by modifying this computer to have an adequate storage, suitably increasing its speed of action, and providing it with an appropriate programme, C can be made to play satisfactorily the part of A in the imitation game, the part of B being taken by a man?”
6. Contrary Views on the Main Question
We may now consider the ground to have been cleared and we are ready to proceed to the debate on our question, “Can machines think?” and the variant of it quoted at the end of the last section. We cannot altogether abandon the original form of the problem, for opinions will differ as to the appropriateness of the substitution and we must at least listen to what has to be said in this connexion.
It will simplify matters for the reader if I explain first my own beliefs in the matter. Consider first the more accurate form of the question. I believe that in about fifty years’ time it will be possible, to programme computers, with a storage capacity of about 109, to make them play the imitation game so well that an average interrogator will not have more than 70 per cent chance of making the right identification after five minutes of questioning. The original question, “Can machines think?” I believe to be too meaningless to deserve discussion. Nevertheless I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted. I believe further that no useful purpose is served by concealing these beliefs. The popular view that scientists proceed inexorably from well-established fact to well-established fact, never being influenced by any improved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research.
I now proceed to consider opinions opposed to my own.
(1) The Theological Objection
Thinking is a function of man’s immortal soul. God has given an immortal soul to every man and woman, but not to any other animal or to machines. Hence no animal or machine can think.
I am unable to accept any part of this, but will attempt to reply in theological terms. I should find the argument more convincing if animals were classed with men, for there is a greater difference, to my mind, between the typical animate and the inanimate than there is between man and the other animals. The arbitrary character of the orthodox view becomes clearer if we consider how it might appear to a member of some other religious community. How do Christians regard the Moslem view that women have no souls? But let us leave this point aside and return to the main argument. It appears to me that the argument quoted above implies a serious restriction of the omnipotence of the Almighty. It is admitted that there are certain things that He cannot do such as making one equal to two, but should we not believe that He has freedom to confer a soul on an elephant if He sees fit? We might expect that He would only exercise this power in conjunction with a mutation which provided the elephant with an appropriately improved brain to minister to the needs of this sort[. An argument of exactly similar form may be made for the case of machines. It may seem different because it is more difficult to “swallow.” But this really only means that we think it would be less likely that He would consider the circumstances suitable for conferring a soul. The circumstances in question are discussed in the rest of this paper. In attempting to construct such machines we should not be irreverently usurping His power of creating souls, any more than we are in the procreation of children: rather we are, in either case, instruments of His will providing .mansions for the souls that He creates.
However, this is mere speculation. I am not very impressed with theological arguments whatever they may be used to support. Such arguments have often been found unsatisfactory in the past. In the time of Galileo it was argued that the texts, “And the sun stood still . . . and hasted not to go down about a whole day” (Joshua x. 13) and “He laid the foundations of the earth, that it should not move at any time” (Psalm cv. 5) were an adequate refutation of the Copernican theory. With our present knowledge such an argument appears futile. When that knowledge was not available it made a quite different impression.
(2) The “Heads in the Sand” Objection
The consequences of machines thinking would be too dreadful. Let us hope and believe that they cannot do so.”
This argument is seldom expressed quite so openly as in the form above. But it affects most of us who think about it at all. We like to believe that Man is in some subtle way superior to the rest of creation. It is best if he can be shown to be necessarily superior, for then there is no danger of him losing his commanding position. The popularity of the theological argument is clearly connected with this feeling. It is likely to be quite strong in intellectual people, since they value the power of thinking more highly than others, and are more inclined to base their belief in the superiority of Man on this power.
I do not think that this argument is sufficiently substantial to require refutation. Consolation would be more appropriate: perhaps this should be sought in the transmigration of souls.
(3) The Mathematical Objection
There are a number of results of mathematical logic which can be used to show that there are limitations to the powers of discrete-state machines. The best known of these results is known as Godel’s theorem ( 1931 ) and shows that in any sufficiently powerful logical system statements can be formulated which can neither be proved nor disproved within the system, unless possibly the system itself is inconsistent. There are other, in some respects similar, results due to Church (1936), Kleene (1935), Rosser, and Turing (1937). The latter result is the most convenient to consider, since it refers directly to machines, whereas the others can only be used in a comparatively indirect argument: for instance if Godel’s theorem is to be used we need in addition to have some means of describing logical systems in terms of machines, and machines in terms of logical systems. The result in question refers to a type of machine which is essentially a digital computer with an infinite capacity. It states that there are certain things that such a machine cannot do. If it is rigged up to give answers to questions as in the imitation game, there will be some questions to which it will either give a wrong answer, or fail to give an answer at all however much time is allowed for a reply. There may, of course, be many such questions, and questions which cannot be answered by one machine may be satisfactorily answered by another. We are of course supposing for the present that the questions are of the kind to which an answer “Yes” or “No” is appropriate, rather than questions such as “What do you think of Picasso?” The questions that we know the machines must fail on are of this type, “Consider the machine specified as follows. . . . Will this machine ever answer ‘Yes’ to any question?” The dots are to be replaced by a description of some machine in a standard form, which could be something like that used in §5. When the machine described bears a certain comparatively simple relation to the machine which is under interrogation, it can be shown that the answer is either wrong or not forthcoming. This is the mathematical result: it is argued that it proves a disability of machines to which the human intellect is not subject.
The short answer to this argument is that although it is established that there are limitations to the Powers If any particular machine, it has only been stated, without any sort of proof, that no such limitations apply to the human intellect. But I do not think this view can be dismissed quite so lightly. Whenever one of these machines is asked the appropriate critical question, and gives a definite answer, we know that this answer must be wrong, and this gives us a certain feeling of superiority. Is this feeling illusory? It is no doubt quite genuine, but I do not think too much importance should be attached to it. We too often give wrong answers to questions ourselves to be justified in being very pleased at such evidence of fallibility on the part of the machines. Further, our superiority can only be felt on such an occasion in relation to the one machine over which we have scored our petty triumph. There would be no question of triumphing simultaneously over all machines. In short, then, there might be men cleverer than any given machine, but then again there might be other machines cleverer again, and so on.
Those who hold to the mathematical argument would, I think, mostly he willing to accept the imitation game as a basis for discussion, Those who believe in the two previous objections would probably not be interested in any criteria.
(4) The Argument from Consciousness
This argument is very, well expressed in Professor Jefferson’s Lister Oration for 1949, from which I quote. “Not until a machine can write a sonnet or compose a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain-that is, not only write it but know that it had written it. No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or depressed when it cannot get what it wants.”
This argument appears to be a denial of the validity of our test. According to the most extreme form of this view the only way by which one could be sure that machine thinks is to be the machine and to feel oneself thinking. One could then describe these feelings to the world, but of course no one would be justified in taking any notice. Likewise according to this view the only way to know that a man thinks is to be that particular man. It is in fact the solipsist point of view. It may be the most logical view to hold but it makes communication of ideas difficult. A is liable to believe “A thinks but B does not” whilst B believes “B thinks but A does not.” instead of arguing continually over this point it is usual to have the polite convention that everyone thinks.
I am sure that Professor Jefferson does not wish to adopt the extreme and solipsist point of view. Probably he would be quite willing to accept the imitation game as a test. The game (with the player B omitted) is frequently used in practice under the name of viva voce to discover whether some one really understands something or has “learnt it parrot fashion.” Let us listen in to a part of such a viva voce:
Interrogator: In the first line of your sonnet which reads “Shall I compare thee to a summer’s day,” would not “a spring day” do as well or better?
Witness: It wouldn’t scan.
Interrogator: How about “a winter’s day,” That would scan all right.
Witness: Yes, but nobody wants to be compared to a winter’s day.
Interrogator: Would you say Mr. Pickwick reminded you of Christmas?
Witness: In a way.
Interrogator: Yet Christmas is a winter’s day, and I do not think Mr. Pickwick would mind the comparison.
Witness: I don’t think you’re serious. By a winter’s day one means a typical winter’s day, rather than a special one like Christmas.
And so on, What would Professor Jefferson say if the sonnet-writing machine was able to answer like this in the viva voce? I do not know whether he would regard the machine as “merely artificially signalling” these answers, but if the answers were as satisfactory and sustained as in the above passage I do not think he would describe it as “an easy contrivance.” This phrase is, I think, intended to cover such devices as the inclusion in the machine of a record of someone reading a sonnet, with appropriate switching to turn it on from time to time.
In short then, I think that most of those who support the argument from consciousness could be persuaded to abandon it rather than be forced into the solipsist position. They will then probably be willing to accept our test.
I do not wish to give the impression that I think there is no mystery about consciousness. There is, for instance, something of a paradox connected with any attempt to localise it. But I do not think these mysteries necessarily need to be solved before we can answer the question with which we are concerned in this paper.
(5) Arguments from Various Disabilities
These arguments take the form, “I grant you that you can make machines do all the things you have mentioned but you will never be able to make one to do X.” Numerous features X are suggested in this connexion I offer a selection:
Be kind, resourceful, beautiful, friendly, have initiative, have a sense of humour, tell right from wrong, make mistakes, fall in love, enjoy strawberries and cream, make some one fall in love with it, learn from experience, use words properly, be the subject of its own thought, have as much diversity of behaviour as a man, do something really new.
No support is usually offered for these statements. I believe they are mostly founded on the principle of scientific induction. A man has seen thousands of machines in his lifetime. From what he sees of them he draws a number of general conclusions. They are ugly, each is designed for a very limited purpose, when required for a minutely different purpose they are useless, the variety of behaviour of any one of them is very small, etc., etc. Naturally he concludes that these are necessary properties of machines in general. Many of these limitations are associated with the very small storage capacity of most machines. (I am assuming that the idea of storage capacity is extended in some way to cover machines other than discrete-state machines. The exact definition does not matter as no mathematical accuracy is claimed in the present discussion,) A few years ago, when very little had been heard of digital computers, it was possible to elicit much incredulity concerning them, if one mentioned their properties without describing their construction. That was presumably due to a similar application of the principle of scientific induction. These applications of the principle are of course largely unconscious. When a burnt child fears the fire and shows that he fears it by avoiding it, f should say that he was applying scientific induction. (I could of course also describe his behaviour in many other ways.) The works and customs of mankind do not seem to be very suitable material to which to apply scientific induction. A very large part of space-time must be investigated, if reliable results are to be obtained. Otherwise we may (as most English ‘Children do) decide that everybody speaks English, and that it is silly to learn French.
There are, however, special remarks to be made about many of the disabilities that have been mentioned. The inability to enjoy strawberries and cream may have struck the reader as frivolous. Possibly a machine might be made to enjoy this delicious dish, but any attempt to make one do so would be idiotic. What is important about this disability is that it contributes to some of the other disabilities, e.g., to the difficulty of the same kind of friendliness occurring between man and machine as between white man and white man, or between black man and black man.
The claim that “machines cannot make mistakes” seems a curious one. One is tempted to retort, “Are they any the worse for that?” But let us adopt a more sympathetic attitude, and try to see what is really meant. I think this criticism can be explained in terms of the imitation game. It is claimed that the interrogator could distinguish the machine from the man simply by setting them a number of problems in arithmetic. The machine would be unmasked because of its deadly accuracy. The reply to this is simple. The machine (programmed for playing the game) would not attempt to give the right answers to the arithmetic problems. It would deliberately introduce mistakes in a manner calculated to confuse the interrogator. A mechanical fault would probably show itself through an unsuitable decision as to what sort of a mistake to make in the arithmetic. Even this interpretation of the criticism is not sufficiently sympathetic. But we cannot afford the space to go into it much further. It seems to me that this criticism depends on a confusion between two kinds of mistake, We may call them “errors of functioning” and “errors of conclusion.” Errors of functioning are due to some mechanical or electrical fault which causes the machine to behave otherwise than it was designed to do. In philosophical discussions one likes to ignore the possibility of such errors; one is therefore discussing “abstract machines.” These abstract machines are mathematical fictions rather than physical objects. By definition they are incapable of errors of functioning. In this sense we can truly say that “machines can never make mistakes.” Errors of conclusion can only arise when some meaning is attached to the output signals from the machine. The machine might, for instance, type out mathematical equations, or sentences in English. When a false proposition is typed we say that the machine has committed an error of conclusion. There is clearly no reason at all for saying that a machine cannot make this kind of mistake. It might do nothing but type out repeatedly “O = I.” To take a less perverse example, it might have some method for drawing conclusions by scientific induction. We must expect such a method to lead occasionally to erroneous results.
The claim that a machine cannot be the subject of its own thought can of course only be answered if it can be shown that the machine has some thought with some subject matter. Nevertheless, “the subject matter of a machine’s operations” does seem to mean something, at least to the people who deal with it. If, for instance, the machine was trying to find a solution of the equation x2 – 40x – 11 = 0 one would be tempted to describe this equation as part of the machine’s subject matter at that moment. In this sort of sense a machine undoubtedly can be its own subject matter. It may be used to help in making up its own programmes, or to predict the effect of alterations in its own structure. By observing the results of its own behaviour it can modify its own programmes so as to achieve some purpose more effectively. These are possibilities of the near future, rather than Utopian dreams.
The criticism that a machine cannot have much diversity of behaviour is just a way of saying that it cannot have much storage capacity. Until fairly recently a storage capacity of even a thousand digits was very rare.
The criticisms that we are considering here are often disguised forms of the argument from consciousness, Usually if one maintains that a machine can do one of these things, and describes the kind of method that the machine could use, one will not make much of an impression. It is thought that tile method (whatever it may be, for it must be mechanical) is really rather base. Compare the parentheses in Jefferson’s statement quoted on page 22.
(6) Lady Lovelace’s Objection
Our most detailed information of Babbage’s Analytical Engine comes from a memoir by Lady Lovelace ( 1842). In it she states, “The Analytical Engine has no pretensions to originate anything. It can do whatever we know how to order it to perform” (her italics). This statement is quoted by Hartree ( 1949) who adds: “This does not imply that it may not be possible to construct electronic equipment which will ‘think for itself,’ or in which, in biological terms, one could set up a conditioned reflex, which would serve as a basis for ‘learning.’ Whether this is possible in principle or not is a stimulating and exciting question, suggested by some of these recent developments But it did not seem that the machines constructed or projected at the time had this property.”
I am in thorough agreement with Hartree over this. It will be noticed that he does not assert that the machines in question had not got the property, but rather that the evidence available to Lady Lovelace did not encourage her to believe that they had it. It is quite possible that the machines in question had in a sense got this property. For suppose that some discrete-state machine has the property. The Analytical Engine was a universal digital computer, so that, if its storage capacity and speed were adequate, it could by suitable programming be made to mimic the machine in question. Probably this argument did not occur to the Countess or to Babbage. In any case there was no obligation on them to claim all that could be claimed.
This whole question will be considered again under the heading of learning machines.
A variant of Lady Lovelace’s objection states that a machine can “never do anything really new.” This may be parried for a moment with the saw, “There is nothing new under the sun.” Who can be certain that “original work” that he has done was not simply the growth of the seed planted in him by teaching, or the effect of following well-known general principles. A better variant of the objection says that a machine can never “take us by surprise.” This statement is a more direct challenge and can be met directly. Machines take me by surprise with great frequency. This is largely because I do not do sufficient calculation to decide what to expect them to do, or rather because, although I do a calculation, I do it in a hurried, slipshod fashion, taking risks. Perhaps I say to myself, “I suppose the Voltage here ought to he the same as there: anyway let’s assume it is.” Naturally I am often wrong, and the result is a surprise for me for by the time the experiment is done these assumptions have been forgotten. These admissions lay me open to lectures on the subject of my vicious ways, but do not throw any doubt on my credibility when I testify to the surprises I experience.
I do not expect this reply to silence my critic. He will probably say that h surprises are due to some creative mental act on my part, and reflect no credit on the machine. This leads us back to the argument from consciousness, and far from the idea of surprise. It is a line of argument we must consider closed, but it is perhaps worth remarking that the appreciation of something as surprising requires as much of a “creative mental act” whether the surprising event originates from a man, a book, a machine or anything else.
The view that machines cannot give rise to surprises is due, I believe, to a fallacy to which philosophers and mathematicians are particularly subject. This is the assumption that as soon as a fact is presented to a mind all consequences of that fact spring into the mind simultaneously with it. It is a very useful assumption under many circumstances, but one too easily forgets that it is false. A natural consequence of doing so is that one then assumes that there is no virtue in the mere working out of consequences from data and general principles.
(7) Argument from Continuity in the Nervous System
The nervous system is certainly not a discrete-state machine. A small error in the information about the size of a nervous impulse impinging on a neuron, may make a large difference to the size of the outgoing impulse. It may be argued that, this being so, one cannot expect to be able to mimic the behaviour of the nervous system with a discrete-state system.
It is true that a discrete-state machine must be different from a continuous machine. But if we adhere to the conditions of the imitation game, the interrogator will not be able to take any advantage of this difference. The situation can be made clearer if we consider sonic other simpler continuous machine. A differential analyser will do very well. (A differential analyser is a certain kind of machine not of the discrete-state type used for some kinds of calculation.) Some of these provide their answers in a typed form, and so are suitable for taking part in the game. It would not be possible for a digital computer to predict exactly what answers the differential analyser would give to a problem, but it would be quite capable of giving the right sort of answer. For instance, if asked to give the value of (actually about 3.1416) it would be reasonable to choose at random between the values 3.12, 3.13, 3.14, 3.15, 3.16 with the probabilities of 0.05, 0.15, 0.55, 0.19, 0.06 (say). Under these circumstances it would be very difficult for the interrogator to distinguish the differential analyser from the digital computer.
(8) The Argument from Informality of Behaviour
It is not possible to produce a set of rules purporting to describe what a man should do in every conceivable set of circumstances. One might for instance have a rule that one is to stop when one sees a red traffic light, and to go if one sees a green one, but what if by some fault both appear together? One may perhaps decide that it is safest to stop. But some further difficulty may well arise from this decision later. To attempt to provide rules of conduct to cover every eventuality, even those arising from traffic lights, appears to be impossible. With all this I agree.
From this it is argued that we cannot be machines. I shall try to reproduce the argument, but I fear I shall hardly do it justice. It seems to run something like this. “if each man had a definite set of rules of conduct by which he regulated his life he would be no better than a machine. But there are no such rules, so men cannot be machines.” The undistributed middle is glaring. I do not think the argument is ever put quite like this, but I believe this is the argument used nevertheless. There may however be a certain confusion between “rules of conduct” and “laws of behaviour” to cloud the issue. By “rules of conduct” I mean precepts such as “Stop if you see red lights,” on which one can act, and of which one can be conscious. By “laws of behaviour” I mean laws of nature as applied to a man’s body such as “if you pinch him he will squeak.” If we substitute “laws of behaviour which regulate his life” for “laws of conduct by which he regulates his life” in the argument quoted the undistributed middle is no longer insuperable. For we believe that it is not only true that being regulated by laws of behaviour implies being some sort of machine (though not necessarily a discrete-state machine), but that conversely being such a machine implies being regulated by such laws. However, we cannot so easily convince ourselves of the absence of complete laws of behaviour as of complete rules of conduct. The only way we know of for finding such laws is scientific observation, and we certainly know of no circumstances under which we could say, “We have searched enough. There are no such laws.”
We can demonstrate more forcibly that any such statement would be unjustified. For suppose we could be sure of finding such laws if they existed. Then given a discrete-state machine it should certainly be possible to discover by observation sufficient about it to predict its future behaviour, and this within a reasonable time, say a thousand years. But this does not seem to be the case. I have set up on the Manchester computer a small programme using only 1,000 units of storage, whereby the machine supplied with one sixteen-figure number replies with another within two seconds. I would defy anyone to learn from these replies sufficient about the programme to be able to predict any replies to untried values.
(9) The Argument from Extrasensory Perception
I assume that the reader is familiar with the idea of extrasensory perception, and the meaning of the four items of it, viz., telepathy, clairvoyance, precognition and psychokinesis. These disturbing phenomena seem to deny all our usual scientific ideas. How we should like to discredit them! Unfortunately the statistical evidence, at least for telepathy, is overwhelming. It is very difficult to rearrange one’s ideas so as to fit these new facts in. Once one has accepted them it does not seem a very big step to believe in ghosts and bogies. The idea that our bodies move simply according to the known laws of physics, together with some others not yet discovered but somewhat similar, would be one of the first to go.
This argument is to my mind quite a strong one. One can say in reply that many scientific theories seem to remain workable in practice, in spite of clashing with ESP; that in fact one can get along very nicely if one forgets about it. This is rather cold comfort, and one fears that thinking is just the kind of phenomenon where ESP may be especially relevant.
A more specific argument based on ESP might run as follows: “Let us play the imitation game, using as witnesses a man who is good as a telepathic receiver, and a digital computer. The interrogator can ask such questions as ‘What suit does the card in my right hand belong to?’ The man by telepathy or clairvoyance gives the right answer 130 times out of 400 cards. The machine can only guess at random, and perhaps gets 104 right, so the interrogator makes the right identification.” There is an interesting possibility which opens here. Suppose the digital computer contains a random number generator. Then it will be natural to use this to decide what answer to give. But then the random number generator will be subject to the psychokinetic powers of the interrogator. Perhaps this psychokinesis might cause the machine to guess right more often than would be expected on a probability calculation, so that the interrogator might still be unable to make the right identification. On the other hand, he might be able to guess right without any questioning, by clairvoyance. With ESP anything may happen.
If telepathy is admitted it will be necessary to tighten our test up. The situation could be regarded as analogous to that which would occur if the interrogator were talking to himself and one of the competitors was listening with his ear to the wall. To put the competitors into a “telepathy-proof room” would satisfy all requirements.
7. Learning Machines
The reader will have anticipated that I have no very convincing arguments of a positive nature to support my views. If I had I should not have taken such pains to point out the fallacies in contrary views. Such evidence as I have I shall now give.
Let us return for a moment to Lady Lovelace’s objection, which stated that the machine can only do what we tell it to do. One could say that a man can “inject” an idea into the machine, and that it will respond to a certain extent and then drop into quiescence, like a piano string struck by a hammer. Another simile would be an atomic pile of less than critical size: an injected idea is to correspond to a neutron entering the pile from without. Each such neutron will cause a certain disturbance which eventually dies away. If, however, the size of the pile is sufficiently increased, tire disturbance caused by such an incoming neutron will very likely go on and on increasing until the whole pile is destroyed. Is there a corresponding phenomenon for minds, and is there one for machines? There does seem to be one for the human mind. The majority of them seem to be “subcritical,” i.e., to correspond in this analogy to piles of subcritical size. An idea presented to such a mind will on average give rise to less than one idea in reply. A smallish proportion are supercritical. An idea presented to such a mind that may give rise to a whole “theory” consisting of secondary, tertiary and more remote ideas. Animals minds seem to be very definitely subcritical. Adhering to this analogy we ask, “Can a machine be made to be supercritical?”
The “skin-of-an-onion” analogy is also helpful. In considering the functions of the mind or the brain we find certain operations which we can explain in purely mechanical terms. This we say does not correspond to the real mind: it is a sort of skin which we must strip off if we are to find the real mind. But then in what remains we find a further skin to be stripped off, and so on. Proceeding in this way do we ever come to the “real” mind, or do we eventually come to the skin which has nothing in it? In the latter case the whole mind is mechanical. (It would not be a discrete-state machine however. We have discussed this.)
These last two paragraphs do not claim to be convincing arguments. They should rather be described as “recitations tending to produce belief.”
The only really satisfactory support that can be given for the view expressed at the beginning of §6, will be that provided by waiting for the end of the century and then doing the experiment described. But what can we say in the meantime? What steps should be taken now if the experiment is to be successful?
As I have explained, the problem is mainly one of programming. Advances in engineering will have to be made too, but it seems unlikely that these will not be adequate for the requirements. Estimates of the storage capacity of the brain vary from 1010 to 1015 binary digits. I incline to the lower values and believe that only a very small fraction is used for the higher types of thinking. Most of it is probably used for the retention of visual impressions, I should be surprised if more than 109 was required for satisfactory playing of the imitation game, at any rate against a blind man. (Note: The capacity of the Encyclopaedia Britannica, 11th edition, is 2 X 109) A storage capacity of 107, would be a very practicable possibility even by present techniques. It is probably not necessary to increase the speed of operations of the machines at all. Parts of modern machines which can be regarded as analogs of nerve cells work about a thousand times faster than the latter. This should provide a “margin of safety” which could cover losses of speed arising in many ways, Our problem then is to find out how to programme these machines to play the game. At my present rate of working I produce about a thousand digits of progratiirne a day, so that about sixty workers, working steadily through the fifty years might accomplish the job, if nothing went into the wastepaper basket. Some more expeditious method seems desirable.
In the process of trying to imitate an adult human mind we are bound to think a good deal about the process which has brought it to the state that it is in. We may notice three components.
(a) The initial state of the mind, say at birth,
(b) The education to which it has been subjected,
(c) Other experience, not to be described as education, to which it has been subjected.
Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain. Presumably the child brain is something like a notebook as one buys it from the stationer’s. Rather little mechanism, and lots of blank sheets. (Mechanism and writing are from our point of view almost synonymous.) Our hope is that there is so little mechanism in the child brain that something like it can be easily programmed. The amount of work in the education we can assume, as a first approximation, to be much the same as for the human child.
We have thus divided our problem into two parts. The child programme and the education process. These two remain very closely connected. We cannot expect to find a good child machine at the first attempt. One must experiment with teaching one such machine and see how well it learns. One can then try another and see if it is better or worse. There is an obvious connection between this process and evolution, by the identifications
Structure of the child machine = hereditary material
Changes of the child machine = mutation,
Natural selection = judgment of the experimenter
One may hope, however, that this process will be more expeditious than evolution. The survival of the fittest is a slow method for measuring advantages. The experimenter, by the exercise of intelligence, should he able to speed it up. Equally important is the fact that he is not restricted to random mutations. If he can trace a cause for some weakness he can probably think of the kind of mutation which will improve it.
It will not be possible to apply exactly the same teaching process to the machine as to a normal child. It will not, for instance, be provided with legs, so that it could not be asked to go out and fill the coal scuttle. Possibly it might not have eyes. But however well these deficiencies might be overcome by clever engineering, one could not send the creature to school without the other children making excessive fun of it. It must be given some tuition. We need not be too concerned about the legs, eyes, etc. The example of Miss Helen Keller shows that education can take place provided that communication in both directions between teacher and pupil can take place by some means or other.
We normally associate punishments and rewards with the teaching process. Some simple child machines can be constructed or programmed on this sort of principle. The machine has to be so constructed that events which shortly preceded the occurrence of a punishment signal are unlikely to be repeated, whereas a reward signal increased the probability of repetition of the events which led up to it. These definitions do not presuppose any feelings on the part of the machine, I have done some experiments with one such child machine, and succeeded in teaching it a few things, but the teaching method was too unorthodox for the experiment to be considered really successful.
The use of punishments and rewards can at best be a part of the teaching process. Roughly speaking, if the teacher has no other means of communicating to the pupil, the amount of information which can reach him does not exceed the total number of rewards and punishments applied. By the time a child has learnt to repeat “Casabianca” he would probably feel very sore indeed, if the text could only be discovered by a “Twenty Questions” technique, every “NO” taking the form of a blow. It is necessary therefore to have some other “unemotional” channels of communication. If these are available it is possible to teach a machine by punishments and rewards to obey orders given in some language, e.g., a symbolic language. These orders are to be transmitted through the “unemotional” channels. The use of this language will diminish greatly the number of punishments and rewards required.
Opinions may vary as to the complexity which is suitable in the child machine. One might try to make it as simple as possible consistently with the general principles. Alternatively one might have a complete system of logical inference “built in.”‘ In the latter case the store would be largely occupied with definitions and propositions. The propositions would have various kinds of status, e.g., well-established facts, conjectures, mathematically proved theorems, statements given by an authority, expressions having the logical form of proposition but not belief-value. Certain propositions may be described as “imperatives.” The machine should be so constructed that as soon as an imperative is classed as “well established” the appropriate action automatically takes place. To illustrate this, suppose the teacher says to the machine, “Do your homework now.” This may cause “Teacher says ‘Do your homework now’ ” to be included amongst the well-established facts. Another such fact might be, “Everything that teacher says is true.” Combining these may eventually lead to the imperative, “Do your homework now,” being included amongst the well-established facts, and this, by the construction of the machine, will mean that the homework actually gets started, but the effect is very satisfactory. The processes of inference used by the machine need not be such as would satisfy the most exacting logicians. There might for instance be no hierarchy of types. But this need not mean that type fallacies will occur, any more than we are bound to fall over unfenced cliffs. Suitable imperatives (expressed within the systems, not forming part of the rules of the system) such as “Do not use a class unless it is a subclass of one which has been mentioned by teacher” can have a similar effect to “Do not go too near the edge.”
The imperatives that can be obeyed by a machine that has no limbs are bound to be of a rather intellectual character, as in the example (doing homework) given above. important amongst such imperatives will be ones which regulate the order in which the rules of the logical system concerned are to be applied, For at each stage when one is using a logical system, there is a very large number of alternative steps, any of which one is permitted to apply, so far as obedience to the rules of the logical system is concerned. These choices make the difference between a brilliant and a footling reasoner, not the difference between a sound and a fallacious one. Propositions leading to imperatives of this kind might be “When Socrates is mentioned, use the syllogism in Barbara” or “If one method has been proved to be quicker than another, do not use the slower method.” Some of these may be “given by authority,” but others may be produced by the machine itself, e.g. by scientific induction.
The idea of a learning machine may appear paradoxical to some readers. How can the rules of operation of the machine change? They should describe completely how the machine will react whatever its history might be, whatever changes it might undergo. The rules are thus quite time-invariant. This is quite true. The explanation of the paradox is that the rules which get changed in the learning process are of a rather less pretentious kind, claiming only an ephemeral validity. The reader may draw a parallel with the Constitution of the United States.
An important feature of a learning machine is that its teacher will often be very largely ignorant of quite what is going on inside, although he may still be able to some extent to predict his pupil’s behavior. This should apply most strongly to the later education of a machine arising from a child machine of well-tried design (or programme). This is in clear contrast with normal procedure when using a machine to do computations one’s object is then to have a clear mental picture of the state of the machine at each moment in the computation. This object can only be achieved with a struggle. The view that “the machine can only do what we know how to order it to do,”‘ appears strange in face of this. Most of the programmes which we can put into the machine will result in its doing something that we cannot make sense (if at all, or which we regard as completely random behaviour. Intelligent behaviour presumably consists in a departure from the completely disciplined behaviour involved in computation, but a rather slight one, which does not give rise to random behaviour, or to pointless repetitive loops. Another important result of preparing our machine for its part in the imitation game by a process of teaching and learning is that “human fallibility” is likely to be omitted in a rather natural way, i.e., without special “coaching.” (The reader should reconcile this with the point of view on pages 23 and 24.) Processes that are learnt do not produce a hundred per cent certainty of result; if they did they could not be unlearnt.
It is probably wise to include a random element in a learning machine. A random element is rather useful when we are searching for a solution of some problem. Suppose for instance we wanted to find a number between 50 and 200 which was equal to the square of the sum of its digits, we might start at 51 then try 52 and go on until we got a number that worked. Alternatively we might choose numbers at random until we got a good one. This method has the advantage that it is unnecessary to keep track of the values that have been tried, but the disadvantage that one may try the same one twice, but this is not very important if there are several solutions. The systematic method has the disadvantage that there may be an enormous block without any solutions in the region which has to be investigated first, Now the learning process may be regarded as a search for a form of behaviour which will satisfy the teacher (or some other criterion). Since there is probably a very large number of satisfactory solutions the random method seems to be better than the systematic. It should be noticed that it is used in the analogous process of evolution. But there the systematic method is not possible. How could one keep track of the different genetical combinations that had been tried, so as to avoid trying them again?
We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English. This process could follow the normal teaching of a child. Things would be pointed out and named, etc. Again I do not know what the right answer is, but I think both approaches should be tried.
We can only see a short distance ahead, but we can see plenty there that needs to be done.
Sobre Searle y Turing
John Searle (Denver, 1932), considerado una de las eminencias de la filosofía contemporánea, es también uno de los pensadores más críticos con la noción de la inteligencia artificial. Profesor de la Universidad de Berkeley, sostiene que los ordenadores nunca llegarán a sustituir a la mente humana. Ayer abrió en San Sebastián el séptimo Congreso Internacional de Ontología con su conferencia Qué es el lenguaje.
“Es muy necesaria una concepción filosófica que aglutine las ciencias naturales”
Pregunta. ¿Mantiene hoy, 25 años después, las mismas tesis que planteó con su imagen de la habitación china?
Respuesta. Por supuesto. El principal fallo de la inteligencia artificial es pensar que el ser humano puede funcionar sin cerebro, como una computadora. En el ordenador se pueden hacer muchas simulaciones (meteorológicas, por ejemplo), pero no son reales. El cerebro es una máquina cuyo funcionamiento no conocemos, luego es imposible reproducirlo. Es mucho más complejo, puesto que se rige por un sistema bioquímico. Lo crucial del pensamiento es que funciona mediante transferencias de energía, con un complejo sistema aleatorio, lo que no es posible con los ordenadores.
P. Pero, al ritmo que avanzan las tecnologías, ¿cabe la posibilidad de que los humanos acabemos siendo prisioneros de los ordenadores?
R. No. La única posibilidad de que eso ocurra es que las máquinas acaben teniendo consciencia. En ese momento habría una rivalidad entre la consciencia humana y la artificial, pero hoy por hoy no sabemos cómo se crea la segunda. Si todos los ordenadores del mundo desarrollaran una consciencia, sí habría motivos para preocuparse. Pero no hay peligro: el pensamiento no es sólo un programa informático. Podemos estar preocupados por muchas cuestiones que pasan en el mundo, pero no por el hecho de que los ordenadores vayan a suplantarnos.
P. ¿Se está configurando un nuevo ser humano a partir de la revolución tecnológica?
R. No, pero estamos entrando en una nueva era de la filosofía. Este siglo, a diferencia del anterior, que fue el de la física, será el de las neurociencias. Hoy es muy necesaria una concepción filosófica que aglutine todas las ciencias que estudian el ser humano, como la neurobiología, la química, la física, las ciencias naturales… La neurobiología está yendo más allá que la filosofía, y está arrojando algo de luz sobre el concepto de la consciencia desde el punto de vista filosófico.
P. ¿Cuáles son sus últimas averiguaciones sobre el lenguaje?
R. En el terreno de la reflexión lingüística hay que estudiar de qué forma el lenguaje constituye la sociedad. Los animales tienen grupos sociales, pero no tienen nada parecido a la civilización humana. ¿Por qué? Porque ésta es la consecuencia del lenguaje. El lenguaje no sólo facilita la civilización, sino que la crea. El dinero, las vacaciones, el gobierno, el matrimonio… todo está constituido por el lenguaje. El lenguaje es lo fundamental en las relaciones humanas.
P. ¿Qué opina de la gramática generativa de Chomsky?
R. Ha cambiado tanto que ya no sabemos lo que es. Ha sufrido cuatro revoluciones y hoy no se sabe dónde está Chomsky en gramática. Está en el aire, sin definir. Pero Chomsky es el lingüista más importante del mundo.
THE CATHOLIC UNIVERSITY OF AMERICA
JOHN SEARLE’S CHINESE ROOM ARGUMENT
Nathan D. March
Rev. David D. Thayer, S.S., Ph.D.
PHIL 315 Philosophy of Language
04 December 2002
John Searle, in his paper “Minds, Brains, and Programs” argues against the two strong claims of artificial intelligence (AI) proposed by researchers such as A.M. Turing and Roger Schank. The first claim suggests that properly programmed computers can understand as human beings understand. The second claim alleges computer programs themselves can explain the human activity of understanding. Searle rejects these claims. His thesis is that machines cannot be said to understand if their operation is defined as an instantiation of a computer program because “no purely formal model will ever be by itself sufficient for intentionality.”
The Claims of Artificial Intelligence
Turing’s Imitation Machine
A.M. Turing in his article “Computing Machinery and Intelligence,” considers the question,
“Can machines think?”
He proposes a scientific solution to the problem by restating it in terms of an “imitation game” of question-and-answer. Turing defines the game as a human interrogator questioning, in separate rooms, both a human computer and a digital computer. He considers the best strategy for the computer is to “provide answers that would naturally be given by a man.” Turing envisions human beings as machines who follow fixed rules with no freedom or authority to deviate from them. For the experiment, the human is required to answer according to rules supplied in a book. It seems plausible to him that from the perspective of the human interrogator the computer simulation could perfectly correlate with the human computer. Turing believes this perfect correspondence, or correctness, validates the claim that the computer thinks.
Robert Schank’s Machine
Following Turing’s work, Robert Schank considered the human ability to understand stories and answer questions about them, even regarding information not explicitly stated in the story. Schank’s machine can answer this way because it has a “representation”, or database, of the sort of information that human beings would have about the objects referenced in the stories. He concludes, a computer simulation that can “print out answers of the sort that we would expect human beings to give if told similar stories,” can be said to literally understand the story. He claims what the computer and its program do explains the human ability to understand stories and answer questions about them.
John R. Searle’s Argument
Searle does not believe these formalist accounts are able to explain the human mind’s ability to ascribe meaning or intentionality to formal symbols. However, he does not reject the claim that machines can think or understand; for Searle, human beings are thinking machines or even digital computers. Rather, his argument is that no machine can think, mean, or understand solely in virtue of its instantiating a computer program.
The Chinese Room Experiment
He begins his argument by suggesting, “A way to test any theory of mind is to ask oneself what it would be like if one’s own mind actually worked on the principles that the theory says all minds work on.” He employs an ingenious experiment based on Schank’s example, but indicates it will work for any Turing machine.
The experiment places Searle in a locked room with two stacks of Chinese writing and a book of rules written in English. From outside the room an interrogator submits a third batch of Chinese writing to him, along with a set of instructions written in English. The instructions allow him to correlate elements of the third batch of Chinese writing with the first two batches according to the book of rules, and instruct him how to return Chinese symbols in response.
Searle can understand English since it is his native language. With respect to the Chinese language he states, “I know no Chinese either written or spoken, and that I’m not even confident that I could recognize Chinese writing as Chinese writing distinct from say, Japanese writing or meaningless squiggles.” Since he does not know Chinese, Searle does not recognize the first batch of Chinese writing as “a script”, the second batch as “a story”, and the third set as “a question.” The English instructions serve as “a program” and allow him to generate Chinese symbols as “an answer.” In this way, Searle is simply behaving like a computer and performing computational operations on formally specified elements. He is an instantiation of a computer program.
Searle supposes that given time he could become proficient at manipulating Chinese symbols in this manner such that, from the point of view of the interrogator, his answers to the questions would be indistinguishable from those of native Chinese speakers. This perfect correspondence was the condition for the possibility of understanding for Turing.
The Chinese Room and the First Claim
The first AI claim states that properly programmed computers can understand as human beings understand. Through the Chinese Room Seale responds, “it seems to me obvious in the example that I do not understand a word of the Chinese stories…I can have any formal program you like, but I still understand nothing.” In the same way, Schank’s computer understands nothing, “since in the Chinese case the computer is me; and in cases where the computer is not me, the computer has nothing more than I have in the case where I understand nothing.”
The Chinese Room and the Second Claim
The second AI claim suggests computer programs themselves can explain the human activity of understanding. Searle responds, “we can see that the computer and its program do not provide sufficient conditions of understanding, since the computer and the program are functioning and there is no understanding.” What the Chinese Room experiment suggests is that as long as the program is defined in terms of computational operations on purely formally-defined elements they by themselves have no interesting connection with understanding since a human will be able to follow the formal principles without understanding anything.
The Problem of Artificial Intelligence
Having countered the two claims of Artificial Intelligence, Searle asks the question, “What is it, then, that I have in the case of the English sentences which I do not have in the case of the Chinese sentences?” He answers that on the one hand, he knows what the symbols mean, and on the other, he has not a clue. For Searle, the fundamental question of AI becomes, “In what does understanding consist, and why couldn’t we give it to a machine?”
Searle understands understanding as a cognitive state conditioned by an intentional phenomenon. This intentionality allows for the possibility of ascribing meaning to symbols. He defines intentionality as, “that feature of certain mental states by which they are directed at or are about objects and states of affairs in the world.” Searle asserts that he understands, not because he is an instantiation of a computer program, but because he is, “a certain sort of organism with a certain biological structure, and this structure under certain conditions is causally capable of producing perception, action, understanding, learning, and other intentional phenomena.”
The fact computers are a machine does not deny the possibility they might think. Searle simply claims, as demonstrated in the Chinese Room experiment, the formal properties instantiated in a computer program lack the causal power to produce intentionality. Searle suggests AI’s misplacement of intentionality is the result of the misunderstanding of analogies proponents of AI use when discussing the subject.
The Problem of Analogy
Analogy of Intentionality
Searle perceives AI’s emphasis on the instantiation of a formal program as the result of the misapprehension of intentionality. He states, “We often attribute ‘understanding’ and other cognitive predicates by analogy.” Further, he suggests human beings metaphorically extend human intentionality to their artifacts and tools as an extension of their purposes. Thus, whatever intentionality computers appear to have is exclusively in the minds of those who program them. Therefore, the claim made by AI that instantiating a program is a sufficient condition for intentionality is ungrounded.
The aim of the Chinese Room example was to show that the attributions of intentionality have nothing to do with formal programs. They are based on the assumption that if the computer looks and behaves sufficiently like a human it must have similar mental states. The experiment illustrates that, if something with intentionality is placed into the system and formally programmed, the formal program produces no additional intentionality and adds nothing to the intentional being’s ability to understand.
Analogy of Mind-Program
Part of the confusion arises from the mind-program analogy: “Mind is to brain as program is to hardware.” Searle implies such a conviction stems from a “residual behaviorism or operationalism that acknowledges similar input and output patterns between computers and humans and therefore postulates similar mental states.” Such a position is consistent with modern technological objective thinking that defines truth in terms of correctness and duplication. The Chinese Room experiment questions the position by illustrating an example of a system that has input and output capabilities, which duplicate those of a native Chinese speaker, and does not understand Chinese.
The mind-program analogy, and the dualistic assumption that the mind is independent of the brain, allows for the AI claim that programs, independent of their realization, provide the causal capacity to produce intentionality. The entire AI endeavor, to emulate and explain the mental by designing programs that are independent of any realization, necessitates the mind’s empirical independence from the brain. The Chinese Room example specifically denies this by illustrating that “no purely formal model, independent of it realization, will ever be by itself sufficient for intentionality.”
Analogy of Information Processing
According to the mind-program analogy, computers perform information processing and the human brain with its mind must do the same. Proponents of AI claim that if a computer is properly programmed the information processing would be identical to human information processing and therefore the computer would be said to understand as a human understands. Searle suggests this argument is supported by an ambiguous notion of the term. He notes that computers do not do information processing in the same sense that people process information. Unlike humans, computers manipulate formal symbols with no associated meaning. “Thus if you type into the computer, ‘2 plus 2 equals?’ it will answer ‘4’ but it has no idea that ‘4’ means 4, or that it means anything at all.” Searle suggests, “If AI workers totally repudiated behaviorism and operationalism, much of the confusion between simulation and duplication would be eliminated.”
John Searle argues against the two strong claims of artificial intelligence: that properly programmed computers can understand as human beings understand and computer programs themselves can explain the human activity of understanding.
Using the Chinese Room experiment he demonstrates the inability of a formal instantiation to result in understanding. Searle defines human understanding as a biological phenomenon resulting from the brain’s causal capacity to produce intentionality. His prejudice towards cognitive science and the philosophy of the mind limits his exploration of artificial intelligence with respect to the philosophy of language. Only secondarily, does he consider how misinterpretation and ambiguous analogy provide the impetus for artificial intelligence.
Turing segun Dangerous Knowledge BBC Four. David Malone (2007):