16 de noviembre de 2015

Inteligencia artificial: Minski Marvin , Dreyfus Hubert



Inteligencia artificial



Marvin Lee Minsky (Nueva York, 9 de agosto de 1927) es un científico estadounidense. Es considerado uno de los padres de las ciencias de la computación[cita requerida] y cofundador del laboratorio de inteligencia artificial del Instituto Tecnológico de Massachusetts o MIT.

Biografía
Marvin Lee Minsky nació en la ciudad de Nueva York, en el seno de una familia judía. Asistió a la Escuela Fieldston y a la Escuela Secundaria de Ciencias del Bronx. Más tarde asistió a la Academia Phillips en Andover, Massachusetts.
Tras acabar la secundaria se unió a la Marina de los Estados Unidos. Tras dos años de servicio entra en la Universidad de Princeton donde se graduaría en 1950. En la actualidad ocupa la plaza de Profesor Toshiba de los Medios de Comunicación y las Ciencias en el Instituto Tecnológico de Massachusetts (MIT).
Minsky ha contribuido en el desarrollo de la descripción gráfica simbólica, geometría computacional, representación del conocimiento, semántica computacional, percepción mecánica, aprendizaje simbólico y conexionista. En 1951 creó SNARC, el primer simulador de redes neuronales.
Escribió el libro "Perceptrones" (con Seymour Papert), que se convirtió en el trabajo fundacional en el análisis de redes neuronales artificiales. Su crítica de la investigación poco rigurosa en el campo ha sido indicada como responsable de la desaparición virtual de la investigación académica en redes neuronales artificiales durante los años 70.
Minsky fue consejero en la película 2001: Una odisea del espacio y hay referencias a él tanto en la película como en el libro. Durante la filmación Minsky casi murió en un accidente.1
Minsky también es responsable de sugerir la trama de "Jurassic Park" a Michael Crichton durante un paseo por la playa de Malibú. En ese punto los dinosaurios fueron concebidos como autómatas. Más tarde Crichton hizo uso de sus conocimientos en biomedicina y concibió los dinosaurios como clones.

El Minskytron o "Pantalla de tres posiciones" operado por la minicomputadora de la compañía ya desaparecida Digital Equipment Corporation (DEC) su modelo PDP-1 en El Museo de Historia de computación, localizado en California, Estados Unidos, mismo monitor que se utilizó en el primer videojuego Spacewar!, Fotografía-2007.
Minsky ha recibido el Premio Fundación BBVA Fronteras del Conocimiento 2013 en Tecnologías de la Información y la Comunicación. El jurado de dicho premio destacó sus trabajos sobre el aprendizaje de las máquinas, en sistemas que integran la robótica, el lenguaje, la percepción y la planificación además de la representación del conocimiento basada en marcos (frames), han conformado el campo de la Inteligencia Artificial.
Marvin Minsky (New York, 1927) es uno de los padres de la Inteligencia Artificial. En 1959, tras completar sus estudios de Matemáticas en Harvard y Princenton, fundó, conjuntamente con John McCarthy el Laboratorio de Inteligencia Artificial del MIT. Ha escrito varias obras de referencia sobre IA e incluso es mencionado en el libro y película '2001: Una Odisea en el Espacio' como responsable de la existencia de HAL. Hoy, a los 88 años, en una lúcida reflexión para Technology Review, sigue mostrando con contundencia su escepticismo acerca del progreso en el campo de la Inteligencia Artificial durante las últimas décadas.
Minsky convivió con personajes de la talla de John von Neumann, Alan Turing, Claude Shannon, Albert Einstein, Isaac Asimov o Carl Sagan en un momento en el que se dieron los primeros pasos en muchos campos de investigación. Y la Inteligencia Artificial fue uno de ellos. Precisamente el término Inteligencia Artificial fue acuñado por John McArthy en 1956 para dar título a una charla donde abordaba esta nueva disciplina diferenciándola explícitamente de otras como la Cibernética.
La inteligencia artificial nació oficialmente como disciplina en una conferencia de ciencias de la computación en el Dartmouth College (New Hampshire, Estados Unidos), en 1956.
Los padres de este nuevo campo fueron John McCarthy, de la Universidad de Stanford; Allen Newell y Herbert Simon, de Carnegie Mellon, y Minsky, el único que sobrevive.



El auge y la decadencia de la IA
Básicamente, Minsky está convencido de que la Inteligencia Artificial no puede progresar porque no hay ideas de suficiente trascendencia como para abrir nuevos caminos de investigación. Es más, los que se han seguido durante las últimas décadas han llevado a un punto muerto en el que es imposible resolver los obstáculos que impiden que se consiga diseñar sistemas inteligentes.
Si hay un detalle relevante en estas reflexiones de ahora, es lo poco que ha cambiado el panorama frente al del año 2003 cuando Minsky, en una charla impartida en la Universidad de Boston, sentenció: "La Inteligencia Artificial padece muerte cerebral desde los años 70". Desde entonces hasta ahora han pasado 12 años y la visión de Minsky sigue en la misma línea.
En TED hay una interesante charla de Marvin Minsky del año 2003 en el que ya muestra abiertamente su espíritu crítico acerca de la situación de la Inteligencia Artificial. Para él, se ha seguido un camino equivocado en la investigación desde los años 60.
El tono con el que aborda la situación de la IA en el momento presente, a sus 88 años de edad, no está exento de melancolía recordando los primeros momentos de la Cibernética. Muchos de sus primeros alumnos en el MIT provenían del Club de Modelismo Ferroviario. No hay que dejarse llevar por las apariencias: los miembros de aquel club, allá en los años cincuenta, eran los hackers del momento, innovadores y expertos diseñando mecanismos y máquinas que nadie más se atrevía a construir.
Minsky, ya en 1951, construyó una máquina buscando un comportamiento inteligente a partir de sus circuitos electrónicos: la SNARC (Stochastic Neural Analog Reinforcement Calculator) usaba 3.000 lámparas de vacío para simular una red neuronal con 40 nodos conectados aleatoriamente. Eran tiempos en los que se hacían grandes descubrimientos cada dos o tres días frente al momento actual, en el que los dos o tres días se han convertido en dos o tres años.
En aquellos primeros días de la cibernética, se daba por hecho que todo era mecanizable, no se pensaba si algo sería posible, sino cuándo y cómo se podría hacer, incluyendo la Inteligencia Artificial. Minsky habla de los años 50 y 60 como maravillosos y fructíferos, pero a partir de ahí se tomaron decisiones equivocadas que han conducido a vías muertas sin margen para avanzar.
Arthur C. Clarke habla de Marvin Minsky en su novela '2001: Una Odisea en el Espacio' como el "padre" de HAL9000. En los años 80 (de 1968), Minsky y Good habrían conseguido construir un sistema computacional inteligente a partir de un programa capaz de aprender. Esta idea parece estar en sintonía con la idea de IA de Minsky.
Para Minsky, lo único que se ha hecho en los últimos 10 años es tratar de mejorar sistemas que no son especialmente buenos y que, de hecho, no han evolucionado mucho en las dos últimas décadas. Ya en anteriores declaraciones años atrás, allá por 2003, se mostraba muy crítico con el giro de la Inteligencia Artificial hacia los sistemas expertos, los sistemas basados en reglas, las redes neuronales, los algoritmos genéticos o la robótica. Según él, no son capaces de resolver el problema de la IA al ignorar problemas de alto nivel como el sentido común.
El sentido común al que se refiere Minsky, es el que hace que un niño de tres años sea más inteligente que cualquier sistema diseñado bajo los auspicios de la IA hasta la fecha. Y es precisamente la falta de interés hacia la investigación de aspectos como el sentido común lo que le hace ser tan poco optimista. Según declaraba Minsky en 2003, "La peor moda ha sido la de esos estúpidos pequeños robots. Los estudiantes que se gradúan pierden tres años de sus vidas soldando y reparando robots en vez de hacer que sean inteligentes. Es impactante."
La solución: despide a los expertos
Los años no han hecho que el tono de Minsky se suavice la hora de proponer soluciones para que la Inteligencia Artificial vuelva a la senda de la innovación y el progreso. "Grandes compañías y malas ideas no combinan bien", afirma. La táctica de comercializar los avances existentes no funciona. No hay investigadores que se atrevan a abordar los problemas de más alto nivel que conciernen a la Inteligencia Artificial y se quedan en aspectos parciales en vez de abordar el problema de la IA desde una perspectiva mucho más amplia.
Lo que propone es sencillo: "Despide a los expertos". Y al mismo tiempo apoyar a los innovadores. Para Minsky es esencial apostar por nuevas ideas de jóvenes capaces de abrir otros caminos de investigación allí donde los actuales se muestran incapaces de avanzar. Abrir la Inteligencia Artificial a perfiles como los de los estudiantes que venían del Club de Modelismo Ferroviario del MIT. Volver atrás, en un melancólico ejercicio de la imaginación, a 1965 y auditar los sistemas y las ideas que siguieron para identificar dónde estaba el error.
Libros
  • “Redes neuronales y el problema del modelo de cerebro”. Título original en inglés “Neural Nets and the Brain Model Problem”. Ph.D. disertación, Universidad de Princeton, 1954.
  • “Computación: máquinas finitas e infinitas”. Título original en inglés “Computation: Finite and Infinite Machines”, Prentice-Hall, 1967.
  • “Procesamiento de información semántica”. Título original en inglés “Semantic Information Processing”. MIT Press, 1968.
  • “Perceptrones”. Título original en inglés “Perceptrons” (con Seymour Papert). MIT Press, 1969.
  • “Inteligencia artificial”. Título original en inglés “Artificial Intelligence” (con Seymour Papert). Prensa de la Universidad de Oregón, 1972.
  • “Robótica”. Título original en inglés "Robotics" Doubleday, 1986.
  • “La sociedad de la mente”. Título original en inglés “The Society of Mind”. Simon and Schuster, 1987.
  • “La opción de Turing”. Título original en inglés “The Turing Option” (con Harry Harrison). Warner Books, New York, 1992.
  • “La máquina con emociones”. Título original en inglés “The Emotion Machine”. ISBN / ASIN: 0743276639.
Marvin Minski Page
http://web.media.mit.edu/~minsky/

The mind, artificial intelligence and emotions
Interview with Marvin Minsky

Conducted by Renato M.E. Sabbatini, PhD, Associate Editor, Brain & Mind Magazine.
Marvin Minsky is respected as one of foremost researchers and writers in many fields of the Computer Sciences, particularly in Artificial Intelligence, the area which studies ways of imitating the human brain’s cognitive functions in a computer. As a professor with the prestigious Massachussetts Institute of Technology (MIT), in Cambridge, USA, he founded the Artificial Intelligence Laboratory, a place where many of the ground-breaking research projects in computer sciences have occurred and still occur, such as the development of programming languages LISP and LOGO. He’s is one of the founders of robotics and is the recipient of a number of awards and honors, such as the Turing Award, which is considered the Nobel of computing. He also participates in the renowned MIT Media Lab, where the media of the future are being researched.
 Due to the many points of contact and interaction between the neurosciences, psychology and computer sciences in the area of Artificial Intelligence, it’s no wonder that the genial mind of Prof. Minsky soon turned to the commonalities and interfaces between both and started to write about the brain and its product, the mind. His "opus magnum" in this area has been a fascinating book, "TheSociety of Mind", which has been translated to many languages,including Portuguese, and which has a number of interesting theories about the organization and workings of the mind. His interest in the area is a long standing one: the first electrical realization of a artificial neural network was made by Minsky while a student. He has even written anovel about building a super-intelligence in 2023 A.D, titled  "The Turing Option", in 1991.
 Prof. Minsky visited São Paulo for his fourth time last May 1998, as invited speaker to Inet’98, a conference on intelligent technologies and networking. During three grueling days, we had the opportunity, as the president of the scientific committee of the conference, to accompany the indefatigable professor (despite his age) in an unending round of press interviews and conferences; as well as to ask many questions of ours. He tremendously impressed us with his intelligence, depth of thinking and originality of ideas, and with his personality. The result is a patchwork of a interview, patched from severalquestions in different settings and times, which we present here for the delight of the reader, who will undoubtedly be mesmerized by the brilliant mind of Marvin Minsky and his many original (and some say, outrageous) ideas and catch phrases.
The Interview
Sabbatini: Prof. Minsky, in your view, what is the contribution that computer sciences can make to the study of thebrain and the mind ?
 Minsky: Well, it is clear to me that computer sciences will change our lives, but not because it’s about computers. It’s because it will help us to understand our own brains, to learn what is the nature of knowledge. It will teach us how we learn to think and feel. This knowledge will change our views of Humanity and enable us to change ourselves.
 Computer sciences are about managing complicated processes and the most complicated thing around are us.
 Sabbatini: Why computers are so stupid?
  Minsky: A vast amount of information lies within our reach. But no present-day machine yet knows enough to answer the simplest questions about daily life, such as:
  • "You should not move people by pushing them"
  • "If you steal something, the owner will be angry"
  • "You can push things with a straight stick but not pull them"
  • "When you release a thing holding in your hand it will fall toward earth (unless it is a helium balloon)"
  • "You cannot move a object by asking it "please come here"
 No computer knows such things, but every normal child does.
 There are many other examples. Robots make cars in factories, but no robot can make a bed, or clean your house or baby-sit. Robots can solve differential equations, but no robot can understand a first grade child’s story. Robots can beat people at chess, but no robot can fill your glass.
 We need common-sense knowledge – and programs that can use it.Common sense computing needs several ways of representing knowledge. It is harder to make a computer housekeeper than a computer chess-player, because the housekeeper must deal with a wider range of situations.
Sabbatini: How large such a knowledge base would be?
 Minsky: I think it would fit all in one CD-ROM. Of course, there is no psychological experiment ever done to see if a person knows more than a CD’s content (650 MB). It is fairly impossible to estimate how many megabytes of information a person knows, but I think that is not more than this. If you memorize 10 books, it would take no more than 1 megabyte of memory, but very few persons know even a small book by heart.
 Hardware is not the limiting factor for building an intelligent computer. We don’t need supercomputers to do this; the problem is that we don’t know what’s the software to use with them. A 1 MHz computer probably is faster than the brain and would do the job provided that it has the right software.
 Sabbatini: Why there are no computers already working with common sense knowledge ?
 Minsky: There are very few people working with common sense problems in Artificial Intelligence. I know of no more than five people, so probably there are about ten of them out there. Who are these people ? There’s John McCarthy, at Stanford University, who was the first to formalize common sense using logics. He has a very interesting web page. Then, there is Harry Sloaman, from the University of Edinburgh, who’s probably the best philosopher in the world working on Artificial Intelligence, with the exception of Daniel Dennett, but he knows more about computers. Then there’s me, of course. Another person working on a strong common-sense project is Douglas Lenat, who directs the CYC project in Austin. Finally, Douglas Hofstadter, who wrote many books about the mind, artificial intelligence, etc., is working on similar problems.
We talk only to each other and no one else is interested. There is something wrong with computer sciences.
Sabbatini: Is there any AI software that uses the common sense approach ?
 Minsky: As I said, the best system based on common sense is CYC, developed by Doug Lenat, a brilliant guy, but he set up a company, CYCorp, and is developing it as a proprietary system. Many computer scientists have a good idea and then made it a secret and start making proprietary systems. They should distribute copies of their system to graduate systems, so that they could evolve and get new ideas. We must understand how they work.
 I don’t believe in intellectual property. The world has gone crazy. People are patenting genes. Why? They didn’t invent them!
 Sabbatini: Are automatic language translation programs and chess-playing programs good examples of truly intelligent software ?
 Minsky: Of course not. Current machine translation technology still falls short of a reasonably good human translator, because it doesn’t really understands what it is translating. Again, it would need common sense knowledge, besides having knowledge about the vocabulary, syntax, etc. of the source and target languages. Prof Noam Chomsky is to be faulted why we don’t have good machine translation programs. He is so brilliant and his theory of generational grammar is so good, that for 40 years it has been used by everyone in the field, shifting the focus from semantics to syntax.
In the beginning of the AI field, teaching a computer how to play complex games was a big thing. Arthur Samuel wrote a checkers-player program in1957. He will be remembered as the pioneer of computer gaming. We have learnt nothing in 40 years, I think, about making chess playing programs:IBM's Deep Blue plays chess (which has beated Gerry Kasparov, the world chess champion) only more rapidly, but not in a different way from the first chess-playing programs. These programs play well and can even beat the current world chess champion but they do not play in the same mannera s the human brain plays.
 Sabbatini: How your concept of the "society of mind" relate to common-sense knowledge ?
 Minksy: Take the human vision system, for example. There is no computer today that can look around a room and make a map of what it seems, a feat that even a four-year old is able to do. We have programs that can recognize faces, that can do somef ocal vision processing and recognition, but not this higher-order processing. Thus, human distance perception is a great example of a "society of mind". There is a suite of cooperating methods, such as gradients, border detection, haze, occlusion, shadow, focus, brightness, motion, disparity, perspective, convergence, shading knowledge, etc.
 A computer program typically has one or two ways of doing something, a human brain has dozen of different methods to use.
  Sabbatini: You seem to believe that we will be able to build truly intelligence in the future. But humans have consciousness, awareness of themselves. Will computers ever be able to have this ?
 Minsky: It’s very easy to make computers aware of themselves. For example, all computers have a stack; a special area of memory where the computer can look to see it’s past actions. It is a trivial problem and not a very important one. The real problem is to know how the mind knows about itself. We don’t understand how this happens. Persons have a very shallow awareness about themselves. This is no mysterious thing.
 As soon as computers gets a minimum of common sense we will know.
 Sabbatini: When will this happen?
 Minsky: Never, at the present rate. The public doesn’t value basic research enough to let this situation be fixed. I suggest that you get a Brazilian center to work on common sense problems for the next 10 years!.
 Sabbatini: Your upcoming book will be about the role of emotions. Could you tell us a little about this ?
 Minsky: Emotion is only adifferent way to think. It may use some of the body functions, such aswhen we prepare to fight (the heart beats faster, etc.). Emotions havea survival value, so that we are able to behave efficiently in some situations.Animals have better, stronger and faster emotions than us.
 Therefore, truly intelligent computers will need to have emotions.This is not impossible or even difficult to achieve. Once we understand the relationship between thinking, emotion and memory, it will be easy to implement these functions into the software.
Freud was one of the first computer scientists, because he studied the importance of memory. He was also a pioneer in proposing the role of emotions in personality and behavior. It is a pity because everyone listened only to his ideas on sex. Freud is more about complicated processes.
According to Freud, the mind is organized as a sandwich. It is made of three layers: the superego, which provides us with attachment, self-image, etc., and that learns social values and ideas, prohibitionsa nd taboos, acquired mainly from our parents. Under it there’s the ego, which mediates conflict resolution and connects to sensory input and motor expression. Under the ego, we find the id, which is responsible for thei nnate drives system, our basic urges, such as hunger, thirst, sex, etc.
 This could be a model for a computer program having personality, knowledge and emotion, social perception, moral constraints, etc.
Selected Resources on the Internet: Artificial
Intelligence
Hubert Dreyfus's views on artificial intelligence


Book cover of the 1979 paperback edition


See also: Philosophy of artificial intelligence
Hubert Dreyfus has been a critic of artificial intelligence research since the 1960s. In a series of papers and books, including Alchemy and AI (1965), What Computers Can't Do (1972; 1979; 1992) and Mind over Machine (1986), he presented a pessimistic assessment of AI's progress and a critique of the philosophical foundations of the field. Dreyfus' objections are discussed in most introductions to the philosophy of artificial intelligence, including Russell & Norvig (2003), the standard AI textbook, and in Fearn (2007), a survey of contemporary philosophy.[1]
Dreyfus argued that human intelligence and expertise depend primarily on unconscious instincts rather than conscious symbolic manipulation, and that these unconscious skills could never be captured in formal rules. His critique was based on the insights of modern continental philosophers such as Merleau-Ponty and Heidegger, and was directed at the first wave of AI research which used high level formal symbols to represent reality and tried to reduce intelligence to symbol manipulation.
When Dreyfus' ideas were first introduced in the mid-1960s, they were met with ridicule and outright hostility.[2][3] By the 1980s, however, many of his perspectives were rediscovered by researchers working in robotics and the new field of connectionism—approaches now called "sub-symbolic" because they eschew early AI research's emphasis on high level symbols. Historian and AI researcher Daniel Crevier writes: "time has proven the accuracy and perceptiveness of some of Dreyfus's comments."[4] Dreyfus said in 2007 "I figure I won and it's over—they've given up."[5]
Dreyfus' critique
The grandiose promises of artificial intelligence
In Alchemy and AI (1965) and What Computers Can't Do (1972), Dreyfus summarized the history of artificial intelligence and ridiculed the unbridled optimism that permeated the field. For example, Herbert A. Simon, following the success of his program General Problem Solver (1957), predicted that by 1967:[6]
  1. A computer would be world champion in chess.
  2. A computer would discover and prove an important new mathematical theorem.
  3. Most theories in psychology will take the form of computer programs.
The press reported these predictions in glowing reports of the imminent arrival of machine intelligence.
Dreyfus felt that this optimism was totally unwarranted. He believed that they were based on false assumptions about the nature of human intelligence. Pamela McCorduck explains Dreyfus position:
[A] great misunderstanding accounts for public confusion about thinking machines, a misunderstanding perpetrated by the unrealistic claims researchers in AI have been making, claims that thinking machines are already here, or at any rate, just around the corner.[7]
These predictions were based on the success of an "information processing" model of the mind, articulated by Newell and Simon in their physical symbol systems hypothesis, and later expanded into a philosophical position known as computationalism by philosophers such as Jerry Fodor and Hilary Putnam.[8] Believing that they had successfully simulated the essential process of human thought with simple programs, it seemed a short step to producing fully intelligent machines. However, Dreyfus argued that philosophy, especially 20th-century philosophy, had discovered serious problems with this information processing viewpoint. The mind, according to modern philosophy, is nothing like a computer.[7]
Dreyfus' four assumptions of artificial intelligence research
In Alchemy and AI and What Computers Can't Do, Dreyfus identified four philosophical assumptions that supported the faith of early AI researchers that human intelligence depended on the manipulation of symbols.[9] "In each case," Dreyfus writes, "the assumption is taken by workers in [AI] as an axiom, guaranteeing results, whereas it is, in fact, one hypothesis among others, to be tested by the success of such work."[10]
The biological assumption
The brain processes information in discrete operations by way of some biological equivalent of on/off switches.
In the early days of research into neurology, scientists realized that neurons fire in all-or-nothing pulses. Several researchers, such as Walter Pitts and Warren McCulloch, argued that neurons functioned similar to the way Boolean logic gates operate, and so could be imitated by electronic circuitry at the level of the neuron.[11] When digital computers became widely used in the early 50s, this argument was extended to suggest that the brain was a vast physical symbol system, manipulating the binary symbols of zero and one. Dreyfus was able to refute the biological assumption by citing research in neurology that suggested that the action and timing of neuron firing had analog components.[12] To be fair, however, Daniel Crevier observes that "few still held that belief in the early 1970s, and nobody argued against Dreyfus" about the biological assumption.[13]
The psychological assumption
The mind can be viewed as a device operating on bits of information according to formal rules.
He refuted this assumption by showing that much of what we "know" about the world consists of complex attitudes or tendencies that make us lean towards one interpretation over another. He argued that, even when we use explicit symbols, we are using them against an unconscious background of commonsense knowledge and that without this background our symbols cease to mean anything. This background, in Dreyfus' view, was not implemented in individual brains as explicit individual symbols with explicit individual meanings.
The epistemological assumption
All knowledge can be formalized.
This concerns the philosophical issue of epistemology, or the study of knowledge. Even if we agree that the psychological assumption is false, AI researchers could still argue (as AI founder John McCarthy has) that it was possible for a symbol processing machine to represent all knowledge, regardless of whether human beings represented knowledge the same way. Dreyfus argued that there was no justification for this assumption, since so much of human knowledge was not symbolic.
The ontological assumption
The world consists of independent facts that can be represented by independent symbols
Dreyfus also identified a subtler assumption about the world. AI researchers (and futurists and science fiction writers) often assume that there is no limit to formal, scientific knowledge, because they assume that any phenomenon in the universe can be described by symbols or scientific theories. This assumes that everything that exists can be understood as objects, properties of objects, classes of objects, relations of objects, and so on: precisely those things that can be described by logic, language and mathematics. The question of what exists is called ontology, and so Dreyfus calls this the ontological assumption. If this is false, then it raises doubts about what we can ultimately know and what intelligent machines will ultimately be able to help us to do.
Knowing-how vs. knowing-that: the primacy of intuition
In Mind Over Machine (1986), written during the heyday of expert systems, Dreyfus analyzed the difference between human expertise and the programs that claimed to capture it. This expanded on ideas from What Computers Can't Do, where he had made a similar argument criticizing the "cognitive simulation" school of AI research practiced by Allen Newell and Herbert A. Simon in the 1960s.
Dreyfus argued that human problem solving and expertise depend on our background sense of the context, of what is important and interesting given the situation, rather than on the process of searching through combinations of possibilities to find what we need. Dreyfus would describe it in 1986 as the difference between "knowing-that" and "knowing-how", based on Heidegger's distinction of present-at-hand and ready-to-hand.[14]
Knowing-that is our conscious, step-by-step problem solving abilities. We use these skills when we encounter a difficult problem that requires us to stop, step back and search through ideas one at time. At moments like this, the ideas become very precise and simple: they become context free symbols, which we manipulate using logic and language. These are the skills that Newell and Simon had demonstrated with both psychological experiments and computer programs. Dreyfus agreed that their programs adequately imitated the skills he calls "knowing-that."
Knowing-how, on the other hand, is the way we deal with things normally. We take actions without using conscious symbolic reasoning at all, as when we recognize a face, drive ourselves to work or find the right thing to say. We seem to simply jump to the appropriate response, without considering any alternatives. This is the essence of expertise, Dreyfus argued: when our intuitions have been trained to the point that we forget the rules and simply "size up the situation" and react.
The human sense of the situation, according to Dreyfus, is based on our goals, our bodies and our culture—all of our unconscious intuitions, attitudes and knowledge about the world. This “context” or "background" (related to Heidegger's Dasein) is a form of knowledge that is not stored in our brains symbolically, but intuitively in some way. It affects what we notice and what we don't notice, what we expect and what possibilities we don't consider: we discriminate between what is essential and inessential. The things that are inessential are relegated to our "fringe consciousness" (borrowing a phrase from William James): the millions of things we're aware of, but we're not really thinking about right now.
Dreyfus did not believe that AI programs, as they were implemented in the 70s and 80s, could capture this "background" or do the kind of fast problem solving that it allows. He argued that our unconscious knowledge could never be captured symbolically. If AI could not find a way to address these issues, then it was doomed to failure, an exercise in "tree climbing with one's eyes on the moon."[15]
History
Dreyfus began to formulate his critique in the early 1960s while he was a professor at MIT, then a hotbed of artificial intelligence research. His first publication on the subject is a half-page objection to a talk given by Herbert A. Simon in the spring of 1961.[16] Dreyfus was especially bothered, as a philosopher, that AI researchers seemed to believe they were on the verge of solving many long standing philosophical problems within a few years, using computers.
Alchemy and AI
In 1965, Dreyfus was hired (with his brother Stuart Dreyfus' help) by Paul Armer to spend the summer at RAND Corporation's Santa Monica facility, where he would write Alchemy and AI, the first salvo of his attack. Armer had thought he was hiring an impartial critic and was surprised when Dreyfus produced a scathing paper intended to demolish the foundations of the field. (Armer stated he was unaware of Dreyfus' previous publication.) Armer delayed publishing it, but ultimately realized that "just because it came to a conclusion you didn't like was no reason not to publish it."[17] It finally came out as RAND Memo and soon became a best seller.[18]
The paper flatly ridiculed AI research, comparing it to alchemy: a misguided attempt to change metals to gold based on a theoretical foundation that was no more than mythology and wishful thinking.[19] It ridiculed the grandiose predictions of leading AI researchers, predicting that there were limits beyond which AI would not progress and intimating that those limits would be reached soon.[20]
Reaction
The paper "caused an uproar", according to Pamela McCorduck.[21] The AI community's response was derisive and personal. Seymour Papert dismissed one third of the paper as "gossip" and claimed that every quotation was deliberately taken out of context.[22] Herbert A. Simon accused Dreyfus of playing "politics" so that he could attach the prestigious RAND name to his ideas. Simon says "what I resent about this was the RAND name attached to that garbage".[23]
Dreyfus, who taught at MIT, remembers that his colleagues working in AI "dared not be seen having lunch with me."[24] Joseph Weizenbaum, the author of ELIZA, felt his colleagues' treatment of Dreyfus was unprofessional and childish. Although he was an outspoken critic of Dreyfus' positions, he recalls "I became the only member of the AI community to be seen eating lunch with Dreyfus. And I deliberately made it plain that theirs was not the way to treat a human being."[25]
The paper was the subject of a short in The New Yorker magazine on June 11, 1966. The piece mentioned Dreyfus' contention that, while computers may be able to play checkers, no computer could yet play a decent game of chess. It reported with wry humor (as Dreyfus had) about the victory of a ten-year-old over the leading chess program, with "even more than its usual smugness."[20]
In hopes of regaining AI's reputation, Seymour Papert arranged a chess match between Dreyfus and Richard Greenblatt's Mac Hack program. Dreyfus lost, much to Papert's satisfaction.[26] An Association for Computing Machinery bulletin[27] used the headline:
"A Ten Year Old Can Beat the Machine— Dreyfus: But the Machine Can Beat Dreyfus"[28]
Dreyfus complained in print that he hadn't said a computer will never play chess, to which Herbert A. Simon replied: "You should recognize that some of those who are bitten by your sharp-toothed prose are likely, in their human weakness, to bite back ... may I be so bold as to suggest that you could well begin the cooling---a recovery of your sense of humor being a good first step."[29]
Vindicated
By the early 1990s several of Dreyfus' radical opinions had become mainstream.
Failed predictions. As Dreyfus had foreseen, the grandiose predictions of early AI researchers failed to come true. Fully intelligent machines (now known as "strong AI") did not appear in the mid-1970s as predicted. HAL 9000 (whose capabilities for natural language, perception and problem solving were based on the advice and opinions of Marvin Minsky) did not appear in the year 2001. "AI researchers", writes Nicolas Fearn, "clearly have some explaining to do."[30] Today researchers are far more reluctant to make the kind of predictions that were made in the early days. (Although some futurists, such as Ray Kurzweil, are still given to the same kind of optimism.)
The biological assumption, although common in the forties and early fifties, was no longer assumed by most AI researchers by the time Dreyfus published What Computers Can't Do.[13] Although many still argue that it is essential to reverse-engineer the brain by simulating the action of neurons (such as Ray Kurzweil[31] or Jeff Hawkins[32]), they don't assume that neurons are essentially digital, but rather that the action of analog neurons can be simulated by digital machines to a reasonable level of accuracy.[31] (Alan Turing had made this same observation as early as 1950.)[33]
The psychological assumption and unconscious skills. Many AI researchers have come to agree that human reasoning does not consist primarily of high-level symbol manipulation. In fact, since Dreyfus first published his critiques in the 60s, AI research in general has moved away from high level symbol manipulation or "GOFAI", towards new models that are intended to capture more of our unconscious reasoning. Daniel Crevier writes that by 1993, unlike 1965, AI researchers "no longer made the psychological assumption",[13] and had continued forward without it. These new "sub-symbolic" approaches include:
  • Computational intelligence paradigms, such as neural nets, evolutionary algorithms and so on are mostly directed at simulated unconscious reasoning. Dreyfus himself agrees that these sub-symbolic methods can capture the kind of "tendencies" and "attitudes" that he considers essential for intelligence and expertise.[34]
  • Research into commonsense knowledge has focussed on reproducing the "background" or context of knowledge.
  • Robotics researchers like Hans Moravec and Rodney Brooks were among the first to realize that unconscious skills would prove to be the most difficult to reverse engineer. (See Moravec's paradox.) Brooks would spearhead a movement in the late 80s that took direct aim at the use of high-level symbols, called Nouvelle AI. The situated movement in robotics research attempts to capture our unconscious skills at perception and attention.[35]
  • Statistical AI use techniques related to economics and statistics to allow machines to "guess" – to make inexact, probabilistic decisions and predictions based on experience and learning. These highly successful techniques are similar to what Dreyfus called "sizing up the situation and reacting", but here the "situation" consists of vast amounts of numerical data.
This research has gone forward without any direct connection to Dreyfus' work.[36]
Knowing-how and knowing-that. Research in psychology and economics has been able to show that Dreyfus' (and Heidegger's) speculation about the nature of human problem solving was essentially correct. Daniel Kahnemann and Amos Tversky collected a vast amount of hard evidence that human beings use two very different methods to solve problems, which they named "system 1" and "system 2". System one, also known as the adaptive unconscious, is fast, intuitive and unconscious. System 2 is slow, logical and deliberate. Their research was collected in the book Thinking, Fast and Slow, and inspired Malcolm Gladwell's popular book Blink. As with AI, this research was entirely independent of both Dreyfus and Heidegger.[36]
Ignored
Although clearly AI research has come to agree with Dreyfus, McCorduck writes that "my impression is that this progress has taken place piecemeal and in response to tough given problems, and owes nothing to Dreyfus."[36]
The AI community, with a few exceptions, chose not to respond to Dreyfus directly. "He's too silly to take seriously" a researcher told Pamela McCorduck.[29] Marvin Minsky said of Dreyfus (and the other critiques coming from philosophy) that "they misunderstand, and should be ignored."[37] When Dreyfus expanded Alchemy and AI to book length and published it as What Computers Can't Do in 1972, no one from the AI community chose to respond (with the exception of a few critical reviews). McCorduck asks "If Dreyfus is so wrong-headed, why haven't the artificial intelligence people made more effort to contradict him?"[29]

Part of the problem was the kind of philosophy that Dreyfus used in his critique. Dreyfus was an expert in modern European philosophers (like Heidegger and Merleau-Ponty).[38] AI researchers of the 1960s, by contrast, based their understanding of the human mind on engineering principles and efficient problem solving techniques related to management science. On a fundamental level, they spoke a different language. Edward Feigenbaum complained "What does he offer us? Phenomenology! That ball of fluff. That cotton candy!"[39] In 1965, there was simply too huge a gap between European philosophy and artificial intelligence, a gap that has since been filled by cognitive science, connectionism and robotics research. It would take many years before artificial intelligence researchers were able to address the issues that were important to continental philosophy, such as situatedness, embodiment, perception and gestalt.
Another problem was that he claimed (or seemed to claim) that AI would never be able to capture the human ability to understand context, situation or purpose in the form of rules. But (as Peter Norvig and Stuart Russell would later explain), an argument of this form can not be won: just because one can not imagine formal rules that govern human intelligence and expertise, this does not mean that no such rules exist. They quote Alan Turing's answer to all arguments similar to Dreyfus':
"we cannot so easily convince ourselves of the absence of complete laws of behaviour ... 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.'"[40][41]
Dreyfus did not anticipate that AI researchers would realize their mistake and begin to work towards new solutions, moving away from the symbolic methods that Dreyfus criticized. In 1965, he did not imagine that such programs would one day be created, so he claimed AI was impossible. In 1965, AI researchers did not imagine that such programs were necessary, so they claimed AI was almost complete. Both were wrong.
A more serious issue was the impression that Dreyfus' critique was incorrigibly hostile. McCorduck writes "His derisiveness has been so provoking that he has estranged anyone he might have enlightened. And that's a pity."[36] Daniel Crevier writes that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."[4]
See also
Notes
1.       
·  Note also that Dreyfus was one of the only non-computer scientists asked for a comment in IEEE's survey of AI's greatest controversies. (Hearst et al. 2000)
·  ·  McCorduck 2004, pp. 211–243.
·  ·  Crevier 1993, pp. 120–132.
·  ·  Crevier 1993, p. 125. Cite error: Invalid <ref> tag; name "FOOTNOTECrevier1993125" defined multiple times with different content (see the help page).
·  ·  Quoted in Fearn 2007, p. 51
·  ·  Newell & Simon 1963.
·  ·  McCorduck 2004, p. 212.
·  ·  Horst 2005.
·  ·  McCorduck 2004, p. 211.
·  ·  Dreyfus 1979, p. 157.
·  ·  McCorduck 2004, pp. 51–57, 88–94; Crevier 1993, p. 30; Russell & Norvig 2003, p. 15−16
·  ·  Dreyfus 1992, pp. 158–62.
·  ·  Crevier 1993, p. 126. Cite error: Invalid <ref> tag; name "FOOTNOTECrevier1993126" defined multiple times with different content (see the help page).
·  ·  Dreyfus & Dreyfus 1986 and see From Socrates to Expert Systems. The "knowing-how"/"knowing-that" terminology was introduced in the 1950s by philosopher Gilbert Ryle.
·  ·  Dreyfus 1992, p. 119.
·  ·  McCorduck 2004, p. 225.
·  ·  Paul Armer, quoted in McCorduck (2004, p. 226)
·  ·  McCorduck 2004, p. 225-227.
·  ·  McCorduck 2004, p. 238.
·  ·  McCorduck 2004, p. 230. Cite error: Invalid <ref> tag; name "FOOTNOTEMcCorduck2004230" defined multiple times with different content (see the help page).
·  ·  McCorduck 2004, pp. 227–228.
·  ·  McCorduck 2004, p. 228.
·  ·  Quoted in McCorduck (2004, p. 226)
·  ·  Quoted in Crevier 1993, p. 122
·  ·  Joseph Weizenbaum, quoted in Crevier 1993, p. 123.
·  ·  McCorduck 2004, p. 230-232.
·  ·  The bulletin was for the Special Interest Group in Artificial Intelligence. (ACM SIGART).
·  ·  Quoted in McCorduck (2004, p. 232)
·  ·  McCorduck 2004, p. 233.
·  ·  Fearn 2007, p. 40.
·  ·  Kurzweil 2005.
·  ·  Hawkins & Blakeslee 2005.
·  ·  Turing 1950 under "(7) Argument from Continuity in the Nervous System."
·  ·  Dreyfus 1992, pp. xiv-xvi.
·  ·  See Brooks 1990 or Moravec 1988
·  ·  McCorduck 2004, p. 236. Cite error: Invalid <ref> tag; name "FOOTNOTEMcCorduck2004236" defined multiple times with different content (see the help page).
·  ·  Crevier 1993, p. 143.
·  ·  McCorduck 2004, p. 213.
·  ·  Quoted in McCorduck (2004, pp. 229–230)
·  ·  Turing 1950 under "(8) The Argument from the Informality of Behavior"
41.  ·  Russell & Norvig 2003, p. 950-51.
References

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