Adopting the notion of “pragmatic information” as interpreted by Roederer and granted that understanding arises from genuine information processing, I show that Searle’s “Chinese Room Argument” in rejecting the thesis of Strong Artificial Intelligence and his responses to critics are sound and acceptable. The paper is a safe and secure translation of Searle’s argument into a language of information. According to the notion of information that I adopt, information and information processing are exclusive attributes of biological and artificial systems. However, (...) I will show that information-theoretically, biological brains are fundamentally different from their artificial simulations. I will argue that only biological systems, or their duplicates, are genuine information processing systems and thus capable of understanding Chinese. “Understanding”, in my proposal, denotes “phenomenal consciousness” in the context of Chalmers’ thesis of naturalistic dualism. (shrink)

Ita La recensione presenta la prospettiva enattivista difesa da Alva Noë, e ne discute alcuni aspetti specifici. Il pensiero, la coscienza e la cognizione non sono pienamente comprensibili, secondo l’enattivismo di Noë, senza un’adeguata considerazione del ruolo ricoperto dal corpo e dall’ambiente. Sarebbe quindi sbagliato continuare a pensare che il cervello da solo sia responsabile dei processi cognitivi umani: il programma che ricerca i correlati neurali della coscienza sarebbe quindi destinato al fallimento dal principio, perché tralascia in partenza corpo e (...) ambiente; i programmi di ricerca nel campo dell’intelligenza artificiale sarebbero ugualmente compromessi, non solo con una concezione computazionalista della cognizione, ma anche con la vecchia idea che un cervello artificiale sia sufficiente alla cognizione quanto un cervello naturale. Di seguito si prendono in esame due prospettive sperimentali su cui Noë fa grande affidamento: gli studi sulla plasticità neurale dei furetti svolti da Mriganka Sur e altri, e la realizzazione di un sistema di sostituzione visuo-tattile da parte di Paul Bach-Y-Rita. Per Noë si tratta di elementi cruciali in supporto alla propria prospettiva: in realtà, come emerge dalla breve analisi, l’attribuire loro un significato così chiaro e univoco pro-enattivismo è qualcosa di molto meno scontato. Infine, segue un breve bilancio del libro di Noë, dove gli elementi d’interesse del volume sono presentati insieme all’individuazione di alcuni punti deboli, che il testo condivide con la gran parte degli studi che in vario modo fanno leva sull’intuizione legata all’embodiement: da un lato una difficoltà strutturale riguarda la possibilità di identificare con chiarezza gli elementi costitutivi del mentale; dall’altro lato, il riferimento a forme di esternalismo radicale sembra ugualmente comportare diversi problemi. -/- Abstract Eng The review presents Alva Noë’s enactivist view, and aims at discussing some of its ideas. Thought, consciousness, and cognition are not understandable, Noë claims, without taking into proper consideration the role played by the body and the environment. It would be wrong indeed to go on thinking that our brain alone is responsible for human cognitive processes: the program searching for the neural correlates of consciousness is hopeless in principle, because it neglects from the beginning the body and the environment; research programs in artificial intelligence are as well compromised, not only with a computationalist view of cognition, but also with the old idea that an artificial brain will suffice for cognition just like a natural one. Thereafter, two experimental perspectives, that Noë uses to support his view, are examined: Mriganka Sur’s studies on neural plasticity of ferrets, and Paul Bach-Y-Rita’s tactile-visual substitution system. Noë argues that these results are crucial in supporting his view, but, as the discussion highlights, their pro-enactivism meaning is not so clear and unambiguous. Finally, a balance of the book follows, where the many elements of interest are presented together with the acknowledgment of some weak points, that the book shares with the majority of the proposals that deal with the embodiment’s insight: on the one hand, a structural difficulty concerns the possibility to identify clearly the constitutive elements of the mental; on the other hand, the reference to radical versions of externalism seems to entail many difficulties as well. (shrink)

This is a bibliography of books and articles on consciousness in philosophy, cognitive science, and neuroscience over the last 30 years. There are three main sections, devoted to monographs, edited collections of papers, and articles. The first two of these sections are each divided into three subsections containing books in each of the main areas of research. The third section is divided into 12 subsections, with 10 subject headings for philosophical articles along with two additional subsections for articles in cognitive (...) science and neuroscience. Of course the division is somewhat arbitrary, but I hope that it makes the bibliography easier to use. This bibliography has first been compiled by Thomas Metzinger and David Chalmers to appear in print in two philosophical anthologies on conscious experience . From 1995 onwards it has been continuously updated by Thomas Metzinger, and now is freely available as a PDF-, RTF-, or HTML-file. This bibliography mainly attempts to cover the Anglo-Saxon and German debates, in a non-annotated, fully formatted way that makes it easy to "cut and paste" from the original file. To a certain degree this bibliography also contains items in other languages than English and German - all submissions in other languages are welcome. Last update of current version: July 13th, 2001. (shrink)

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the 'other minds' problem, and similar topics in philosophy of mind are discussed. We (...) also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic. -/- (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...) We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines. (shrink)

This paper argues for a noncognitiveist computationalism in the philosophy of mind. It further argues that both humans and computers have intentionality, that is, their mental states are semantical -- they are about things in their worlds.

When certain formal symbol systems (e.g., computer programs) are implemented as dynamic physical symbol systems (e.g., when they are run on a computer) their activity can be interpreted at higher levels (e.g., binary code can be interpreted as LISP, LISP code can be interpreted as English, and English can be interpreted as a meaningful conversation). These higher levels of interpretability are called "virtual" systems. If such a virtual system is interpretable as if it had a mind, is such a "virtual (...) mind" real? This is the question addressed in this "virtual" symposium, originally conducted electronically among four cognitive scientists: Donald Perlis, a computer scientist, argues that according to the computationalist thesis, virtual minds are real and hence Searle's Chinese Room Argument fails, because if Searle memorized and executed a program that could pass the Turing Test in Chinese he would have a second, virtual, Chinese-understanding mind of which he was unaware (as in multiple personality). Stevan Harnad, a psychologist, argues that Searle's Argument is valid, virtual minds are just hermeneutic overinterpretations, and symbols must be grounded in the real world of objects, not just the virtual world of interpretations. Computer scientist Patrick Hayes argues that Searle's Argument fails, but because Searle does not really implement the program: A real implementation must not be homuncular but mindless and mechanical, like a computer. Only then can it give rise to a mind at the virtual level. Philosopher Ned Block suggests that there is no reason a mindful implementation would not be a real one. (shrink)

This article is an accompaniment to Anthony Freeman’s review of Views into the Chinese Room, reflecting on some pertinent outstanding questions about the Chinese room argument. Although there is general agreement in the artificial intelligence community that the CRA is somehow wrong, debate continues on exactly why and how it is wrong. Is there a killer counter-argument and, if so, what is it? One remarkable fact is that the CRA is prototypically a thought experiment, yet it has been very little (...) discussed from the perspective of thought experiments in general. Here, I argue that the CRA fails as a thought experiment because it commits the fallacy of undersupposing, i.e., it leaves too many details to be filled in by the audience. Since different commentators will often fill in details differently, leading to different opinions of what constitutes a decisive counter, the result is 21-plus years of inconclusive debate. (shrink)

John Searle’s Chinese room argument is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence scientists and philosophers of mind, that “the appropriately programmed computer really is a mind”. Since its publication in 1980, the CRA has evoked an enormous amount of debate about its implications for machine intelligence, the functionalist philosophy of mind, theories of consciousness, etc. Although the general consensus among commentators is that the CRA is flawed, and not withstanding the popularity of the (...) systems reply in some quarters, there is remarkably little agreement on exactly how and why it is flawed. A newcomer to the controversy could be forgiven for thinking that the bewildering collection of diverse replies to Searle betrays a tendency to unprincipled, ad hoc argumentation and, thereby, a weakness in the opposition’s case. In this paper, treating the CRA as a prototypical example of a ‘destructive’ thought experiment, I attempt to set it in a logical framework, which allows us to systematise and classify the various objections. Since thought experiments are always posed in narrative form, formal logic by itself cannot fully capture the controversy. On the contrary, much also hinges on how one translates between the informal everyday language in which the CRA was initially framed and formal logic and, in particular, on the specific conception of possibility that one reads into the logical formalism. (shrink)

This paper provides a survey of philosophical discussion of the "the Turing Test". In particular, it provides a very careful and thorough discussion of the famous 1950 paper that was published in Mind.

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing.

Turing's celebrated 1950 paper proposes a very general methodological criterion for modelling mental function: total functional equivalence and indistinguishability. His criterion gives rise to a hierarchy of Turing Tests, from subtotal ("toy") fragments of our functions (t1), to total symbolic (pen-pal) function (T2 -- the standard Turing Test), to total external sensorimotor (robotic) function (T3), to total internal microfunction (T4), to total indistinguishability in every empirically discernible respect (T5). This is a "reverse-engineering" hierarchy of (decreasing) empirical underdetermination of the theory (...) by the data. Level t1 is clearly too underdetermined, T2 is vulnerable to a counterexample (Searle's Chinese Room Argument), and T4 and T5 are arbitrarily overdetermined. Hence T3 is the appropriate target level for cognitive science. When it is reached, however, there will still remain more unanswerable questions than when Physics reaches its Grand Unified Theory of Everything (GUTE), because of the mind/body problem and the other-minds problem, both of which are inherent in this empirical domain, even though Turing hardly mentions them. (shrink)

Socially assistive robotics is a growing area of research. Evaluating SAR systems presents novel challenges. Using a robot for a socially assistive task can have various benefits and ethical implications. Many questions are important to understanding whether a robot is effective for a given application domain. This paper describes several benchmarks for evaluating SAR systems. There exist numerous methods for evaluating the many factors involved in a robot’s design. Benchmarks from psychology, anthropology, medicine, and human–robot interaction are proposed as measures (...) of success in evaluating a given SAR system and its impact on the user and broader population. (shrink)

How should biological behaviour be modelled? A relatively new approach is to investigate problems in neuroethology by building physical robot models of biological sensorimotor systems. The explication and justification of this approach are here placed within a framework for describing and comparing models in the behavioural and biological sciences. First, simulation models – the representation of a hypothesis about a target system – are distinguished from several other relationships also termed “modelling” in discussions of scientific explanation. Seven dimensions on which (...) simulation models can differ are defined and distinctions between them discussed: 1. Relevance: whether the model tests and generates hypotheses applicable to biology. 2. Level: the elemental units of the model in the hierarchy from atoms to societies. 3. Generality: the range of biological systems the model can represent. 4. Abstraction: the complexity, relative to the target, or amount of detail included in the model. 5. Structural accuracy: how well the model represents the actual mechanisms underlying the behaviour. 6. Performance match: to what extent the model behaviour matches the target behaviour. 7. Medium: the physical basis by which the model is implemented. No specific position in the space of models thus defined is the only correct one, but a good modelling methodology should be explicit about its position and the justification for that position. It is argued that in building robot models biological relevance is more effective than loose biological inspiration; multiple levels can be integrated; that generality cannot be assumed but might emerge from studying specific instances; abstraction is better done by simplification than idealisation; accuracy can be approached through iterations of complete systems; that the model should be able to match and predict target behaviour; and that a physical medium can have significant advantages. These arguments reflect the view that biological behaviour needs to be studied and modelled in context, that is, in terms of the real problems faced by real animals in real environments. Key Words: animal behaviour; levels; models; neuroethology; realism; robotics; simulation. (shrink)

Computation is interpretable symbol manipulation. Symbols are objects that are manipulated on the basis of rules operating only on theirshapes, which are arbitrary in relation to what they can be interpreted as meaning. Even if one accepts the Church/Turing Thesis that computation is unique, universal and very near omnipotent, not everything is a computer, because not everything can be given a systematic interpretation; and certainly everything can''t be givenevery systematic interpretation. But even after computers and computation have been successfully distinguished (...) from other kinds of things, mental states will not just be the implementations of the right symbol systems, because of the symbol grounding problem: The interpretation of a symbol system is not intrinsic to the system; it is projected onto it by the interpreter. This is not true of our thoughts. We must accordingly be more than just computers. My guess is that the meanings of our symbols are grounded in the substrate of our robotic capacity to interact with that real world of objects, events and states of affairs that our symbols are systematically interpretable as being about. (shrink)

We introduce a construct called “humanness sensitivity,” which we define as the ability to recognize uniquely human characteristics. To evaluate the construct, we used a “concurrent study design” to conduct an internet-based study with a convenience sample of 42,063 people from 88 countries (52.4% from the U.S. and Canada).We sought to determine to what extent people could identify subtle characteristics of human behavior, thinking, emotions, and social relationships which currently distinguish humans from non-human entities such as bots. Many people were (...) surprisingly poor at this task, even when asked simple questions about human relationships or anatomy. Participants were best at identifying subtle aspects of human cognition and worst at identifying subtle aspects of human communication. Test scores were good predictors of whether someone was employed and modest predictors of other self-reported criterion measures. We also found that people identifying themselves in marginal societal categories (e.g., in the “other” category for gender or sexual orientation) identified themselves as less human and also scored lower on our test. As computers continue to become more human-like, our study suggests that the vast majority of humankind will likely have great difficulty distinguishing them from people. Can methods be devised for improving this ability? Might humanness sensitivity help people to make such distinctions? Will people who excel at differentiating humans and non-human entities—like the “blade runners” in the 1982 and 2017 feature films—someday hold a special place in society? (shrink)

This paper discusses recent research on humanoid robots and thought experiments addressing the question to what degree such robots could be expected to develop human-like cognition, if rather than being pre-programmed they were made to learn from the interaction with their physical and social environment like human infants. A question of particular interest, from both a semiotic and a cognitive scientific perspective, is whether or not such robots could develop an experiential Umwelt, i.e. could the sign processes they are involved (...) in become intrinsically meaningful to themselves? Arguments for and against the possibility of phenomenal artificial minds of different forms are discussed, and it is concluded that humanoid robotics still has to be considered “weak” rather than “strong AI”, i.e. it deals with models of mind rather than actual minds. (shrink)

This paper revisits the conception of intelligence and understanding as embodied in the Turing Test. It argues that a simple system of meaning relations drawn from words/lexical items in a natural language and framed in terms of syntax-free relations in linguistic texts can help ground linguistic inferences in a manner that can be taken to be 'understanding' in a mechanized system. Understanding in this case is a matter of running through the relevant inferences meaning relations allow for, and some of (...) these inferences are plain deductions and some can serve to act as abductions. Understanding in terms of meaning relations also supervenes on linguistic syntax because such understanding cannot be simply reduced to syntactic relations. The current approach to meaning and understanding thus shows that this is one way, if not the only way, of framing Alan Turing's original insight into the nature of thinking in computing systems. (shrink)

In their enthusiasm for programming, computational linguists have tended to lose sight of what humansdo. They have conceived of conversations as independent of sound and the bodies that produce it. Thus, implicit in their simulations is the assumption that the text is the essence of talk. In fact, unlike electronic mail, conversations are acoustic events. During everyday talk, human understanding depends both on the words spoken and on fine interpersonal vocal coordination. When utterances are analysed into sequences of word-based forms, (...) however, these prosodic aspects of language disappear. Therefore, to investigate the possibility that machines might talk, we propose acommunion game that includes this interpersonal patterning. Humans and machines would talk together and, based on recordings of them, a panel would appraise the relevant merit of each machine's simulation by how true to life it sounded. Unlike Turing's imitation game, the communion game overtly focuses attention, not on intelligence, but on language. It is designed to facilitate the development of social groups of adaptive robots that exploit complex acoustic signals in real time. We consider how the development of such machines might be approached. (shrink)

The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is (...) recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension of self-learning turns out as only one of a whole range of “self-x-capacities” (based on ideas of organic computing) that span the self-x-subspace of the full AI state space. (shrink)

A robot that is functionally indistinguishable from us may or may not be a mindless Zombie. There will never be any way to know, yet its functional principles will be as close as we can ever get to explaining the mind.

The central claim of computationalism is generally taken to be that the brain is a computer, and that any computer implementing the appropriate program would ipso facto have a mind. In this paper I argue for the following propositions: (1) The central claim of computationalism is not about computers, a concept too imprecise for a scientific claim of this sort, but is about physical calculi (instantiated discrete formal systems). (2) In matters of formality, interpretability, and so forth, analog computation and (...) digital computation are not essentially different, and so arguments such as Searle''s hold or not as well for one as for the other. (3) Whether or not a biological system (such as the brain) is computational is a scientific matter of fact. (4) A substantive scientific question for cognitive science is whether cognition is better modeled by discrete representations or by continuous representations. (5) Cognitive science and AI need a theoretical construct that is the continuous analog of a calculus. The discussion of these propositions will illuminate several terminology traps, in which it''s all too easy to become ensnared. (shrink)

It is “easy” to explain doing, “hard” to explain feeling. Turing has set the agenda for the easy explanation (though it will be a long time coming). I will try to explain why and how explaining feeling will not only be hard, but impossible. Explaining meaning will prove almost as hard because meaning is a hybrid of know-how and what it feels like to know how.

Harnad''s proposed robotic upgrade of Turing''s Test (TT), from a test of linguistic capacity alone to a Total Turing Test (TTT) of linguisticand sensorimotor capacity, conflicts with his claim that no behavioral test provides even probable warrant for attributions of thought because there is no evidence of consciousness besides private experience. Intuitive, scientific, and philosophical considerations Harnad offers in favor of his proposed upgrade are unconvincing. I agree with Harnad that distinguishing real from as if thought on the basis of (...) (presence or lack of) consciousness (thus rejecting Turing (behavioral) testing as sufficient warrant for mental attribution)has the skeptical consequence Harnad accepts — there is in factno evidence for me that anyone else but me has a mind. I disagree with hisacceptance of it! It would be better to give up the neo-Cartesian faith in private conscious experience underlying Harnad''s allegiance to Searle''s controversial Chinese Room Experiment than give up all claim to know others think. It would be better to allow that (passing) Turing''s Test evidences — evenstrongly evidences — thought. (shrink)

In this essay we try an answer to the question has intentionality to be reduced to anything? We propose that it is possible to reduce any variety of intentionality to a specification of mechanisms (internal organization of the items involved in a given intentional phenomenon) and a historical pattern of interaction (structure of mutual significant relations historically acquired by different items involved in the same intentional phenomenon). We first clarify the meaning of this proposal having recourse to the Ruth Millikan’s (...) teleosemantics. Then, we assess the relevance and feasibility of our proposal, considering, in a succession, the case of animals and humans, of machines, and of sophisticated human collectives. We conclude arguing the heuristic nature of the proposed reduction. DOI:10.5007/1808-1711.2010v14n2p255. (shrink)

The use of the computer metaphor has led to the proposal of mind architecture (Pylyshyn 1984; Newell 1990) as a model of the organization of the mind. The dualist computational model, however, has, since the earliest days of psychological functionalism, required that the concepts mind architecture and brain architecture be remote from each other. The development of both connectionism and neurocomputational science, has sought to dispense with this dualism and provide general models of consciousness – a uniform cognitive architecture –, (...) which is in general reductionist, but which retains the computer metaphor. This paper examines, in the first place, the concepts of mind architecture and brain architecture, in order to evaluate the syntheses which have recently been offered. It then moves on to show how modifications which have been made to classical functionalist mind architectures, with the aim of making them compatible with brain architectures, are unable to resolve some of the most serious problems of functionalism. Some suggestions are given as to why it is not possible to relate mind structures and brain structures by using neurocomputational approaches, and finally the question is raised of the validity of reductionism in a theory which sets out to unite mind and brain architectures. (shrink)