Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...) intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy. (shrink)

A comprehensive introduction to the Language of Though Hypothesis (LOTH) accessible to general audiences. LOTH is an empirical thesis about thought and thinking. For their explication, it postulates a physically realized system of representations that have a combinatorial syntax (and semantics) such that operations on representations are causally sensitive only to the syntactic properties of representations. According to LOTH, thought is, roughly, the tokening of a representation that has a syntactic (constituent) structure with an appropriate semantics. Thinking thus consists in (...) syntactic operations defined over representations. Most of the arguments for LOTH derive their strength from their ability to explain certain empirical phenomena like productivity, systematicity of thought and thinking. (shrink)

W sytuacjach, gdy powinniśmy mieć do czynienia ze wzajemnym oświecaniem, w rzeczywistości często spotykamy się z obopólnym oporem między kognitywistyką a fenomenologią, gdzie ta druga rozumiana jest jako podejście metodologiczne, po raz pierwszy zarysowane przez Husserla. Filozofowie umysłu, z pierwszych szeregów kognitywistów, niejednokrotnie czynią lekceważące gesty w stosunku do fenomenologii, oparte na myleniu fenomenologii z niewykwalifikoną introspekcją psychologiczną (np. Dennett, 1991). Z kolei wielu fenomenologów podlega mylnemu wrażeniu, że kognitywistyce nie udało się wyjść poza tradycyjne modele komputacyjne (DSSI – „dobra (...) staromodna sztuczna inteligencja” (GOFAI - „good old fashioned artificial inteligence”)). Odrzucają oni kognitywistykę, zarzucając jej nadmierny redukcjonizm, uniemożliwiający wyjaśnienie doświadczenia czy świadomości. O ile taki opór nie dziwi w przypadku tych, którzy znają i bronią antynaturalistycznego stanowiska Husserla, o tyle jest niezwykle zaskakujący w przypadku tych, którzy znają dzieła Merleau-Ponty’ego. Badania tego ostatniego łączyły bowiem analizy fenomenologiczne z rozważaniami pochodzącymi z nauk empirycznych, takich jak psychologia i neurologia, i to na długo, zanim kognitywistyka została skonstruowana jako model do uwzględniania tylko tych aspektów psychologii i neurologii, które skupiają się na doświadczeniu poznawczym. Zostawiając przykład Merleau-Ponty’ego, filozofowie po obu stronach stopniowo zdali sobie sprawę, że fenomenologia może być bezpośrednio przydatna do naukowego zrozumienia poznania. Wcześniej niejednokrotnie nawet sami naukowcy empiryczni dochodzili do tego wniosku i to pomimo filozofów. Dobry przykład stanowią tutaj badania Franisco Vareli z neurofenomenologii (Varela, 1996). (shrink)

Clark ends his appendix with a description of what he calls "dynamic computationalism", which he describes as an interesting hybrid between DST and GOFAI. My 'horseLISP" example could be described as an example of dynamic computationalism. It is clearly not as eliminativist as Van Gelder's computational governor example, for I am trying to come up with something like identities between computational entities and dynamic ones. Thus unlike other dynamicists, I am not doing what Clark calls "embracing a different vocabulary (...) for the understanding and analysis of brain events". I think we probably can keep much of the computational vocabulary, although the meanings of many of its terms will probably shift as much as the meaning of 'atom' has shifted since Dalton's time. The label of "dynamic computationalism" is perhaps as good a description of my position as any, but I think I would mean something slightly different by it than Clark would. (For the following, please insert the mantra "of course, this is an empirical question" (OCTEQ) every paragraph or so.). (shrink)

Solipsism, Individualism and Cognitive Science [1] "Artificial Intelligence cannot ignore philosophy" - John McCarthy I shall challenge the claim that Good Old-Fashioned Artificial Intelligence, or GOFAI is solipsistic while more recent neural or "brain-style" approaches to AI are not. After distinguishing GOFAI from connectionism, I will first show that GOFAI is not committed to solipsism but rather to what is more properly called individualism.

My claim is clear and unambiguous: no machine will pass a well-designed Turing Test unless we find some means of embedding it in lived social life. We have no idea how to do this but my argument, and all our evidence, suggests that it will not be a necessary condition that the machine have more than a minimal body. Exactly how minimal is still being worked out.

Theoretiker der Künstlichen Intelligenz und deren Wegbegleiter in der Philosophie des Geistes haben auf unterschiedliche Weise auf Kritik am ursprünglichen Theorieziel der KI reagiert. Eine dieser Reaktionen ist die Zurücknahme dieses Theorieziels zugunsten der Verfolgung kleinerformatiger Projekte. Eine andere Reaktion ist die Propagierung konnektionistischer Systeme, die mit ihrer dezentralen Arbeitsweise die neuronalen Netze des menschlichen Gehirns besser simulieren sollen. Eine weitere ist die sogenannte robot reply. Die Roboterantwort besteht aus zwei Elementen. Sie enthält (a) das Zugeständnis, daß das Systemverhalten eines (...) wie auch immer programmierten konventionellen Digitalrechners mit von Neumann-Architektur nicht schon menschenähnliche Intelligenz aufweist, und (b) die Behauptung, daß es für bestimmte Arten von Maschinen doch zur Intelligenz reicht. In die Liga der intelligenten Wesen könnten Maschinen genau dann aufsteigen, wenn sie Roboter sind. Damit ist gemeint: wenn sie über Wahrnehmungskomponenten (Rezeptoren) und Handlungskomponenten (Effektoren) verfügen, mithilfe deren sie aktiv in kausale Interaktionen mit ihrer Umwelt eintreten können. Im Beitrag wird für die These argumentiert, daß der Roboterantwort eine richtige Intuition zugrunde liegt, von der die Roboterfreunde sich aber zu einer kurzschlüssigen Folgerung verleiten lassen. Es ist richtig, mentale Zustände und Handlungskompetenz eng aneinander zu binden. Einem Wesen, dem man Handlungsfähigkeit zuerkennt, kann man mentale Zustände nicht absprechen. Doch Handelnkönnen und Geisthaben sind nicht hinreichend unabhängig voneinander, als daß man das eine als Rechtfertigung für die Zuerkennung des anderen verwenden könnte. Man sollte Robotern beides absprechen. (shrink)

[Müller, Vincent C. (ed.), (2013), Philosophy and theory of artificial intelligence (SAPERE, 5; Berlin: Springer). 429 pp. ] --- Can we make machines that think and act like humans or other natural intelligent agents? The answer to this question depends on how we see ourselves and how we see the machines in question. Classical AI and cognitive science had claimed that cognition is computation, and can thus be reproduced on other computing machines, possibly surpassing the abilities of human intelligence. This (...) consensus has now come under threat and the agenda for the philosophy and theory of AI must be set anew, re-defining the relation between AI and Cognitive Science. We can re-claim the original vision of general AI from the technical AI disciplines; we can reject classical cognitive science and replace it with a new theory (e.g. embodied); or we can try to find new ways to approach AI, for example from neuroscience or from systems theory. To do this, we must go back to the basic questions on computing, cognition and ethics for AI. The 30 papers in this volume provide cutting-edge work from leading researchers that define where we stand and where we should go from here. (shrink)

In their historical overview of cognitive science, Bechtel, Abraham- son and Graham (1999) describe the field as expanding in focus be- ginning in the mid-1980s. The field had spent the previous 25 years on internalist, high-level GOFAI (“good old fashioned artificial intelli- gence” [Haugeland 1985]), and was finally moving “outwards into the environment and downards into the brain” (Bechtel et al, 1999, p.75). One important force behind the downward movement was Patricia Churchland’s Neurophilosophy (1986). This book began a movement (...) bearing its name, one that truly came of age in 1999 when Kath- leen Akins won a million-dollar fellowship to begin the McDonnell Project in Philosophy and the Neurosciences. The McDonnell Project put neurophilosophy at the forefront of philosophy of mind and cogni- tive science, yielding proliferating articles, conferences, special journal issues and books. In two major new books, neurophilosophers Patricia Churchland (2002) and John Bickle (2003) clearly feel this newfound prominence: Churchland mocks those who do not apply findings in neuroscience to philosophical problems as “no-brainers”; Bickle mocks anyone with traditional philosophical concerns, including “naturalistic philosophers of mind” and other neurophilosophers. (shrink)

This book reports on the results of the third edition of the premier conference in the field of philosophy of artificial intelligence, PT-AI 2017, held on November 4 - 5, 2017 at the University of Leeds, UK. It covers: advanced knowledge on key AI concepts, including complexity, computation, creativity, embodiment, representation and superintelligence; cutting-edge ethical issues, such as the AI impact on human dignity and society, responsibilities and rights of machines, as well as AI threats to humanity and AI safety; (...) and cutting-edge developments in techniques to achieve AI, including machine learning, neural networks, dynamical systems. The book also discusses important applications of AI, including big data analytics, expert systems, cognitive architectures, and robotics. It offers a timely, yet very comprehensive snapshot of what is going on in the field of AI, especially at the interfaces between philosophy, cognitive science, ethics and computing. (shrink)

Just as AI has moved away from classical AI, human-computer interaction (HCI) must move away from what I call ‘good old fashioned HCI’ to ‘new HCI’ – it must become a part of cognitive systems research where HCI is one case of the interaction of intelligent agents (we now know that interaction is essential for intelligent agents anyway). For such interaction, we cannot just ‘analyze the data’, but we must assume intentions in the other, and I suggest these are largely (...) recognized through resistance to carrying out one’s own intentions. This does not require fully cognitive agents but can start at a very basic level. New HCI integrates into cognitive systems research and designs intentional systems that provide resistance to the human agent. (shrink)