For many years now, Harnad has argued that transduction is special among cognitive capacities -- special enough to block Searle's Chinese Room Argument. His arguments (as well as Searle's) have been important and useful, but not correct, it seems to me. Their arguments have provided the modern impetus for getting clear about computationalism and the nature of computing. This task has proven to be quite difficult. Which is simply to say that dealing with Harnad's arguments (as well as Searle's) has (...) been difficult. Turing, it turns out, only got us started. But Harnad's (and Searle's) arguments ultimately fail. Turing, it turns out, was on the right track. (shrink)

When Charles Darwin died in April, 1882, he left behind a world changed forever. Because of his writings, most notably, of course, The Origin of Species, by 1882, evolution was an almost universally acknowledged fact. What remained in dispute, however, was how evolution occurred. So because of Darwin’s work, everyone accepted that new species emerge over time, yet few agreed with him that it was natural selection that powered the change, as Darwin hypothesized. Chalmers’ book, The Conscious Mind , reminds (...) me of The Origin of Species . I have talked t o many people about The Conscious Mind and watched many philosophy students read it, and after they are done, they all take consciousness more seriously than they did before and they are all struck by its deep mystery, yet few accept Chalmers’ specific theory of consciousness and his explanation of its odd nature. (shrink)

The new kid on the block in cognitive science these days is dynamic systems. This way of thinking about the mind is, as usual, radically opposed to computationalism - - the hypothesis that thinking is computing. The use of dynamic systems is just the latest in a series of attempts, from Searle's Chinese Room Argument, through the weirdnesses of postmodernism, to overthrown computationalism, which as we all know is a perfectly nice hypothesis about the mind that never hurt anyone.

Bill Joy’s deep pessimism is now famous. Why the Future Doesn’t Need Us, his defense of that pessimism, has been read by, it seems, everyone—and many of these readers, apparently, have been converted to the dark side, or rather more accurately, to the future-is-dark side. Fortunately (for us; unfortunately for Joy), the defense, at least the part of it that pertains to AI and robotics, fails. Ours may be a dark future, but we cannot know that on the basis of (...) Joy’s reasoning. On the other hand, we ought to fear a good deal more than fear itself: we ought to fear not robots, but what some of us may do with robots. (shrink)

The philosophy of information (PI) is a new area of research with its own field of investigation and methodology. This article, based on the Herbert A. Simon Lecture of Computing and Philosophy I gave at Carnegie Mellon University in 2001, analyses the eighteen principal open problems in PI. Section 1 introduces the analysis by outlining Herbert Simon's approach to PI. Section 2 discusses some methodological considerations about what counts as a good philosophical problem. The discussion centers on Hilbert's famous analysis (...) of the central problems in mathematics. The rest of the article is devoted to the eighteen problems. These are organized into five sections: problems in the analysis of the concept of information, in semantics, in the study of intelligence, in the relation between information and nature, and in the investigation of values. (shrink)

Understanding humans requires viewing them as mechanisms of some sort, since understanding anything requires seeing it as a mechanism. It is science’s job to reveal mechanisms. But science reveals much more than that: it also reveals enduring mystery—strangeness in the proportion. Concentrating just on the scientific side of Selinger’s and Engström’s call for a moratorium on cyborg discourse, I argue that this strangeness prevents cyborg discourse from diminishing us.

This paper challenges two orthodox theses: (a) that computational processes must be algorithmic; and (b) that all computed functions must be Turing-computable. Section 2 advances the claim that the works in computability theory, including Turing's analysis of the effective computable functions, do not substantiate the two theses. It is then shown (Section 3) that we can describe a system that computes a number-theoretic function which is not Turing-computable. The argument against the first thesis proceeds in two stages. It is first (...) shown (Section 4) that whether a process is algorithmic depends on the way we describe the process. It is then argued (Section 5) that systems compute even if their processes are not described as algorithmic. The paper concludes with a suggestion for a semantic approach to computation. (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)

Advocates of dynamic systems have suggested that higher mental processes are based on continuous representations. In order to evaluate this claim, we first define the concept of representation, and rigorously distinguish between discrete representations and continuous representations. We also explore two important bases of representational content. Then, we present seven arguments that discrete representations are necessary for any system that must discriminate between two or more states. It follows that higher mental processes require discrete representations. We also argue that discrete (...) representations are more influenced by conceptual role than continuous representations. We end by arguing that the presence of discrete representations in cognitive systems entails that computationalism (i.e., the view that the mind is a computational device) is true, and that cognitive science should embrace representational pluralism. (shrink)

This chapter reviews some interesting research trends in the philosophy of information (PI). First, PI is defined; then, a series of open problems in PI on which philosophers are currently working is considered. The conclusion highlights the innovative character of this new area of research.

The computational paradigm, which has dominated psychology and artificial intelligence since the cognitive revolution, has been a source of intense debate. Recently, several cognitive scientists have argued against this paradigm, not by objecting to computation, but rather by objecting to the notion of representation. Our analysis of these objections reveals that it is not the notion of representation per se that is causing the problem, but rather specific properties of representations as they are used in various psychological theories. Our analysis (...) suggests that all theorists accept the idea that cognitive processing involves internal information-carrying states that mediate cognitive processing. These mediating states are a superordinate category of representations. We discuss five properties that can be added to mediating states and examine their importance in various cognitive models. Finally, three methodological lessons are drawn from our analysis and discussion. (shrink)

This paper explores the specific questions raised for social epistemology encountered in code and software. It does so because these technologies increasingly make up an important part of our urban environment, and stretch across all aspects of our lives. The paper introduces and explores the way in which code and software become the conditions of possibility for human knowledge, crucially becoming computational epistemes, which we share with non-human but crucially knowledge-producing actors. As such, we need to take account of this (...) new computational world and think about how we live today in a highly mediated code-based world. Nonetheless, here I want to understand software epistemes as a broad concept related to the knowledge generated by both human and non-human actors. The aim is to explore changes that are made possible by the installation of code/software via computational devices, streams, clouds or networks. This is what Mitcham calls a ?new ecology of artifice?. By exploring two case studies, the paper attempts to materialise the practice of software epistemologies through a detailed analysis. This analysis is then drawn together with a notion of compactants to explore how studying tracking software and streams is a useful means of uncovering the agency of software and code for producing these new knowledges. (shrink)

It is shown that the Fodor's interpretation of the frame problem is the central indication that his version of the Modularity Thesis is incompatible with computationalism. Since computationalism is far more plausible than this thesis, the latter should be rejected.

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)

Cognitive science, at least as done by many philosophers, seeks to develop what one might call a content-based theory of cognition. These theorists generally seek to predict/explain cognition by employing generalizations between contentful states like beliefs and desires. In his book, From Folk Psychology To Cognitive Science, Stephen Stich argues that cognitive science should not attempt to employ content-based theories in its explanations of human (and other) behavior. For the most part Stich directs his arguments towards belief/desire psychology. Some of (...) his arguments, however, are directed at content-based theories in general. In this paper, I will be concerned with one of these more general arguments—the principle of autonomy argument.The conclusion of the principle of autonomy argument is that the predictions and explanations generated by content-based theories, in so far as they are possible, do not fall within the domain of prediction and explanation in cognitive science. (shrink)

The semantic view of computation is the claim that semantic properties play an essential role in the individuation of physical computing systems such as laptops and brains. The main argument for the semantic view rests on the fact that some physical systems simultaneously implement different automata at the same time, in the same space, and even in the very same physical properties. Recently, several authors have challenged this argument. They accept the premise of simultaneous implementation but reject the semantic conclusion. (...) In this paper, I aim to explicate the semantic view and to address these objections. I first characterize the semantic view and distinguish it from other, closely related views. Then, I contend that the master argument for the semantic view survives the counter-arguments against it. One counter-argument is that computational individuation is not forced to choose between the implemented automata but rather always picks out a more basic computational structure. My response is that this move might undermine the notion of computational equivalence. Another counter-argument is that while computational individuation is forced to rely on extrinsic features, these features need not be semantic. My reply is that the semantic view better accounts for these extrinsic features than the proposed non-semantic alternatives. (shrink)

The dominant scientific and philosophical view of the mind – according to which, put starkly, cognition is computation – is refuted herein, via specification and defense of the following new argument: Computation is reversible; cognition isn't; ergo, cognition isn't computation. After presenting a sustained dialectic arising from this defense, we conclude with a brief preview of the view we would put in place of the cognition-is-computation doctrine.

Is it true that if zombies---creatures who are behaviorally indistinguishable from us, but no more conscious than a rock-are logically possible, the computational conception of mind is false? Are zombies logically possible? Are they physically possible? This paper is a careful, sustained argument for affirmative answers to these three questions.

Explaining the mind by building machines with minds runs into the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is (...) "everything" a body with a mind can do? Turing's original "pen-pal" version (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body. (shrink)

The traditional view of symbol grounding seeks to connect an a priori internal representation or ‘form’ to its external referent. But such a ‘form’ is usually itself systematically composed out of more primitive parts, so this view ignores its grounding in the physics of the world. Some previous work simulating multiple talking/listening agents has effectively taken this stance, and shown how a shared discrete speech code can emerge. Taking the earlier work of Oudeyer, we have extended his model to include (...) a dispersive force intended to account broadly for a speaker’s motivation to increase auditory distinctiveness. New simulations show that vowel systems result that are more representative of the range seen in human languages. These simulations make many profound abstractions and assumptions. Relaxing these by including more physically and physiologically realistic mechanisms for talking and listening is seen as the key to replicating more complex and dynamic aspects of speech, such as consonant-vowel patterning. (shrink)