If I tought this course again…

  • I’d have more student debates to help develop argumentation skills;
  • I’d give more critical-thinking-oriented assignments;
  • I’d organize more peer- and team-assessment sessions for critical thinking;
  • I’d ask students to improve their peer’s assignments on the basis of their peer or team assessment

I have learn so much from my fellow teachers, but specially from our students that I want to teach this course again. The students’ assessment of the course was so positive and encouraging that I believe this was an eye-opening experience in critical thinking on important Science and Technology issues for all of us. I really hope the university administration works out with G-Providence and all of us, mechanisms to make this type of courses an important part of our academic offering so that there are many next times!

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Una mirada a inteligencia artificial para empezar…

El concepto de inteligencia artificial es del área de las ingeniería y la computación.  Ahora el tema de la inteligencia es tema de estudio de varias disciplina, entre ellas la Psicológica.  En este sentido es vía el abordaje que desde las Ciencias y Tecnologías de la Cognición (CTC) o  ciencias cognitivas, se ha hecho al concepto de inteligencia artificial que podemos encontrar un espacio de discusión y debate con respecto al tema en cuestión.  Es de todos sabido que Las CTC es un “junte” , que desde su origen ha sido interdisciplinario, para abordar el tema de la mente que ha tenido sus propias evoluciones.  Entre las disciplinas interdisciplinarias de este junte se encuentran la filosofía, la epistemología, la ingeniería AI, la neurociencia, la lingüística y la psicología cognitiva entre otras.  Cabe señalar que aunque hay intereses entre partidarios de estas disciplinas en el tema de la mente, este interés no es central a las disciplinas como tal.  Podría decir, que existen perspectivas discursivas insertadas en la visión cognitiva de la psicología para las cuales la idea de crear una ciencia de la mente no es el objetivo de la disciplina, pero para las ciencias cognitivas sí lo és.   Con esto quiero decir que es más bien el interese en el tema de la mente lo que une a grupos de profesionales de distintas disciplinas, que relaciones o parecidos entre sus disciplinas.

Planteado así no deja de parecerme interesante el trabajo con respecto al tema particular de AI que se maneja al interior de las CTC,  siendo ésta sólo una de las vertientes de las CTC aunque la más conocida. Me es fascinante la idea de ver cómo se logra reproducir algo que ni siquiera se comprende.  Por decir de alguna manera que está en estudio y en debate lo que sobre ésto se produce.  Sobre lo que existen si acuerdos, consensos, pero ciertamente no certezas.  Con eso crean una reproducción materializada de su versión de inteligenci; que aunque reduccionista en su visualización de la idea de inteligencia no dejan por ello de ser notoria .  Desde mi perspectiva el esfuerzo y su producto no deja de ser una propuesta interesante dentro del terreno académico.  Lo es para los discursos sobre AI  y  también, para aquellos que abordan el tema de la inteligencia aunque desde otras perspectivas.  Me explico, el hecho de generar tecnología llamada AI, al  materializar en un aparato un procedimiento conceptualizado como inteligente se añade otra perspectiva desde la cual pensar el tema de inteligencia, visto ahora en retrospectiva a través de los ojos de la inteligencia artificial.  Al dársele cuerpo a la conducta inteligente, al representarla en otro espacio como en el de la máquina  o el artefacto, tienen acceso a esta visión gran número de la población que a su vez queda marcada por esta mirada a la inteligencia y la conceptualización que de esta se hace.  Ante esta situación debatir sobre AI no se remite a cualificar el trabajo hecho por ellos, eso no es lo que está en esta ocasión sobre la mesa.  Me parece que lo que vale la pena reseñar es que AI es una visión particular acerca de conducta “ inteligente” en màquinas, creadas a partir de discursos sobre lo que se conoce y conceptualiza acerca de la inteligencia en algunos sectores del mundo del conocimiento.  Y que esta visón no incluye y excluye a la vez, mucho datos vinculados a una visión más compleja sobre el concepto de la inteligencia humana ya sea a conciencia o sin ella, pero sin duda en función de lograr su representación de inteligencia. Que en el caso de ingeniería AI queda configurado en su idea de una máquina inteligente o  de un programa inteligente.  Me pregunto si ellos se preguntaran dónde habrán creado la inteligencia; en el programa que corre la máquina o si la inteligente es la maquina que corre con el programa.  También me pregunto si así como yo, otros profesionales en el campo se preguntarán cuál es la visión de inteligencia que nuestro conocimiento sobre ella ha generado en la población.   AI solo ha puesto el espejo frente a nosotros, miremos ahí lo que hemos dicho. En clase suelo instigar a mis estudiantes con la pregunta acerca de: quien es inteligente el cerebro o el sujeto? ; Y porque buscamos mejorar la inteligencia del sujeto tratando su cerebro?  Cuál es entonces el discurso sobre inteligencia que predomina en nuestros espacios?

Lo que me recuerda que los intentos, aunque fascinante como son no están exentos de concepciones estereotipadas y reduccionistas con respectos al amplio debate que existe en relación al tema de la inteligencia.  AI estudia simulaciones de operaciones mentales, trabaja su versión sobre la reproducción artificial de éstas y de ello genera artefactos que  encarnan dichas visiones. Ese es su campo y lo respeto.  No obstante, hablar de inteligencia al interior de la disciplina de la Psicología es otra mirada.  Con ciertas tangencias, en unas posturas más que en otras, a esta mirada más joven y vieja a la vez de la AI. Es por ello que respeto la presentación de AI desde los que la trabajan y me parece interesante ver como utilizando sintácticamente hablando, el mismo vocabulario conceptualmente nos referimos a cosas distintas.  Para un ejemplo con un botón basta dice el refrán popular.  Digamos que el caso queda radiografiado entre otros en el tema de procesamiento de información por ejemplo o en el tema de lenguaje por mencionar sólo algunos.

Sería entonces la agenda mía acentuar los discursos que desde una mirada particular de la psicología se trabajan con respecto a: Inteligencia, aprendizaje, memoria, lenguaje, pensamiento, procesos mentales, mente y consciencia y como estos difieren y convergen, aunque circunstancialmente, con otros discursos disciplinarios como el del campo de AI.

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If the students have to define intelligence, so do we

By J. Fernando Vega Riveros

I’ll jump start our written dialogs by trying to find a definition of intelligence from the artificial intelligence (AI) point of view, so I will present a few definitions found in several textbooks of AI or related subjects.

Winston [1] jumps right in with a definition of artificial intelligence without much regard for natural intelligence by defining AI as “the study of the computations that make it possible to perceive, reason, and act.”  He goes on by establishing the difference with psychology because of the greater emphasis on computation. Then, he contrasts AI with computer science because of the emphasis on perception, reasoning, and action. This differentiation seems to establish AI as a middle ground between psychology and computer science, or as a third territory which is neither, and apparently avoids discussions. Not much further down the text, Winston titles a section as “Artificial Intelligence Helps Us to Become More Intelligent”, which suggests his position of AI as a complement to human intelligence: AI as human intelligence-inspired science and technology. The diversity of topics and approaches in the book seem to reflect that vision. Topics range from traditional problem-solving approaches based on graph search, and logic knowledge representation and processing, which take up the largest chunk of the book, but adding towards the end connectionist and evolutionary models.

Jackson [2] defines artificial intelligence as “the part of computer science concerned with designing intelligent computer systems, that is, systems that exhibit the characteristics we associate with intelligence in human behavior – understanding language, learning, reasoning, solving problems, and so on.” Jackson in his definition states a position with a commitment to computer science, which at the outset contrasts with Winston’s middle ground position. The question that comes to mind is whether they hold different philosophical positions, or it is just a way to avoid clashes in subjects like psychology in which they may not be fully versed. However, by resorting to terms with strong ties with psychology and cognitive science like perception, language understanding, learning, and even reasoning the question arises: what do they mean by them?

It has been apparent to me over the years that the definition of artificial intelligence is taken lightly in the introductory chapters of some textbooks on the subject, disregarding the philosophical implications of such definitions. Giarratano and Riley [3] present what they call a popular definition of AI as “making computers think like humans”. The quotation marks are not mine but appear in their text, leading me to question why the quotation marks? Is it because that is not their definition? Since they do not have a bibliographic reference, I wonder whose definition that might be? Or is it that they do not want to commit to that definition? They claim this definition has its roots in the Turing test in which a human interrogator poses questions to a human and a machine trying to distinguish between them based on the answers provided by both. If the interrogator cannot tell apart the human from the machine, the machine passes the test [4] (Turing proposed this as the “imitation game” [5]). One could argue that the Turing test only tests that the machine produces answers indistinguishable from a human, but that would not imply that the machine thinks like a human; it acts like a human. After all, do we really know how humans think? This is why I say that the definitions of AI are taken lightly. And the gross interpretations and misunderstandings in this chapter continue to pile up. Giarratano and Riley claim that Steven [sic] Weizembaum’s program Eliza passed the Turing test in 1967, something hardly defensible taking into account what this software did, the techniques it used, and even the statements of its author [6].  Besides, the Turing test set up has been greatly simplified in the Loebner contest, and many people claim that several programs have passed the Turing test simply because they have won this contest. This is not so; the Loebner contest, bronze medal, is won by the “most human-like computer” each year [7], not necessarily passing the Turing test. Only the gold medal would be given to a machine passing the test. Has any program received the gold medal yet? I could not find any specific information about that, and even then, the contest is strongly criticized for lack of scientific rigor.

The prologue in [8] states a more cautious position by defining Distributed Artificial Intelligence (DAI) as “the study, construction, and application of multiagent systems, that is, systems in which several interacting intelligent agents pursue some set of goals or perform some set of tasks.” Digging further into the prologue, we find a definition of an agent as a computational entity that perceives and acts upon the environment, and is autonomous in the sense that its behavior depends partially on its own experience. Worth noticing is the meaning of “intelligent” which they refer to as the ability of the agents to pursue their goals and execute their tasks in such a way to optimize some performance measures. What captures the attention in the subject of DAI are the topics related to the social aspects that may arise from the interaction among agents, where there is no centralized control, synchronization, or unique designer. The need for communication that encompasses protocols (social communication rules, agent etiquette?), shared knowledge representations (meanings?), and organization are a necessity. One expects a minimal set of agreements for DAI to function, but emergent behaviors beyond the direct control of the designers are expected. This possibility opens the door for some interesting discussions…

Russell and Norvig [4] instead of presenting a unique definition of AI resort to present multiple definitions of AI along two dimensions. One dimension is concerned with reasoning vs. behavior. The other dimension presents rationality vs. fidelity to human performance (the italics are Russell and Norvig’s). This results in four visions or approaches to AI. If we concern ourselves with reasoning only, Russell and Norvig describe two approaches: thinking rationally, and thinking humanly. Thinking rationally refers to what the authors call the “laws of thought” approach, mostly based on logic. The thinking humanly approach uses cognitive models, which according to the authors “is necessarily based on experimental investigation of actual humans or animals.” Analyzing AI approaches from the behavior perspective leaves two more combinations: acting rationally, and acting humanly. Acting rationally is based on the notion of rational agents which operate autonomously, perceive their environment, persist over a prolonged time period, adapt to change, and create and pursue goals. The description of this approach clearly shows strong similarities to the description of DAI [8]. When the authors discuss the acting humanly approach, they resort to speak about the Turing test instead of providing a definition, in contrast to the discussions on the other three approaches. One interesting feature of the discussion on acting humanly is that in this case, as the authors say, the computer needs to process natural language, represent knowledge to store what it knows or hears, automated reasoning to use the stored information to answer questions and draw new conclusions, and machine learning to adapt to new circumstances. In the discussion about acting humanly, two statements in this book result particularly thought-provoking: “the quest for artificial flight succeeded when the Wright brothers and others stopped imitating birds”, and “aeronautical engineering texts do not define the goal of their field as making machines that fly so exactly like pigeons that they can fool even other pigeons”. These statements insinuate that acting humanly does not require imitating human processes, but as they say devoting the attention to studying the underlying principles of intelligence, which brings us back to the main problem of this post: defining intelligence.

Intelligence and its underlying principles are elusive matters of study no matter if we want to understand natural intelligence, or design artificially intelligent artifacts. Terms like perception, understanding, reasoning, knowledge, and learning pervade the definitions found in this sample of textbooks, and much of the literature on AI. I find that in general these terms are listed without regard for their full meaning outside computing.

The definition of intelligence has evolved over the years, and its underlying principles are not only a moving but a changing target of study. Probably the only sure thing we can assume is that humans are intelligent since we invented the term to describe ourselves. Nowadays we accept that other species may be intelligent. The question that the discussion about AI brings forward is whether human-designed artifacts may be or become intelligent. But to answer that question we need agreements among the many disciplines concerned with this problem.

References

[1]      Winston, P.H. Artificial Intelligence. Addison-Wesley Pub. Comp. 3rd Ed. 1993.

[2]      Jackson. P. Introduction to Expert Systems. Addison-Wesley Pub. Comp. 3rd Ed. 1998.

[3]      Giarratano, J. C. and G. D. Riley. Expert Systems – Principles and Programming. Thomson Course Technology. 4th Ed. 2005.

[4]      Russell, S. and P. Norvig. Artificial Intelligence – A Modern Approach. Prentice Hall. 3rd Ed. 2010.

[5]      Turing, A. Computing machinery and intelligence. In Mind. October 1950. Vol. 59, No. 236, pp 433-460. (available at http://mind.oxfordjournals.org/content/LIX/236/433. Visited October 9, 2012).

[6]      Weizembaum, J. Eliza – A computer program for the study of natural language communication between man and machine. In Communications of the ACM. Vol. 9 No. 1. January 1966. Pp 36-45. (available at http://www.cse.buffalo.edu/~rapaport/572/S02/weizenbaum.eliza.1966.pdf. Visited October 10, 2012).

[7]      Home Page of the Loebner Competition in Artificial Intelligence – “The first Turing test” http://www.loebner.net/Prizef/loebner-prize.html.(Visited October 9, 2012).

[8]      Weiss. G. editor. Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence. The MIT Press. 1999.

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y ¿por qué ese nombre para el blog?

Viviendo en Puerto Rico no es fácil sustraerse de la mezcla de idiomas español e inglés. Quizás en Puerto Rico puede ser más acentuado este fenómeno que en otros países de habla hispana por la cercanía política con los Estados Unidos de América, pero la inserción de palabras y expresiones en inglés parece ser inevitable por la fuerte influencia de la cultura americana en todo el mundo.

Y ¿de dónde vino ese título? No alcanzo a recordar si fue Ana o fui yo quien salió con el término “after class” en uno de los interesantes diálogos que surgen al terminar oficialmente a la 1:50 las clases del curso INTD3990, “Mind, Consciousness and Machines”. Pero me pareció muy apropiado y lo adopté. La otra parte, “el arte de apalabrar” sí se debe enteramente a Ana quien, según mi entender, usa el término “apalabrar” significando el uso de palabras apropiadas en oraciones claras para expresar las ideas que tenemos sobre los temas que se discuten en la clase, lo cual resulta en un ejercicio difícil, pero divertido y necesario cuando se trabaja multi e interdisciplinariamente.

Y ¿quienes somos? Somos un grupo conformado por una psicóloga puertorriqueña, Ana Nieves; un filósofo americano, Anderson Brown; y un ingeniero colombiano, Fernando Vega, quines nos embarcamos en la aventura de dar un curso interdisciplinario que inicialmente se llamó “Inteligencia Artificial: Tres Perspectivas” (Artificial Intelligence: Three Perspectives), pero que evolucionó hacia uno más ambicioso pero mejor estructurado: “Mente, Conciencia y Máquinas” (Mind, Consciousness and Machines).

Este curso hace parte de un proyecto titulado “Convergence of Culture and Science: Expanding the Humanities Curriculum at UPRM” subvencionado por el “National Endowment for the Humanities” (AC-50156-12). En este proyecto participamos profesores de múltiples nacionalidades por lo cual la mezcla de español e inglés resulta también necesaria.

 

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