David Chalmers

This is just a beginning categorization. I claim no 'objective correctness' for it. And of course the categories can be fluid, and the same joke can be a member of more than one category (and perhaps it will be funnier if it is). But thinking about the jokes which I can recall from the Humour Weekend, most seem to fall squarely into one or another category, indicating that perhaps this is a useful way of dividing jokes. It seems to me that the "causes of humour" in all 4 classes are different, coming from different parts of the brain.

In Section 5 of his interesting article, Goldman suggests that the consideration of imaginary cases can be valuable in the analysis of our psychological concepts. In particular, he argues that we can imagine a system that is isomorphic to us under any functional description, but which lacks qualitative mental states, such as pains and color sensations. Whether or not such a being is empirically possible, it certainly seems to be logically possible, or conceptually coherent. Goldman argues from this possibility to the conclusion that our concepts of qualitative mental states cannot be analyzed entirely in functional terms

What are the philosophical views of contemporary professional philosophers? We surveyed many professional philosophers in order to help determine their views on 30 central philosophical issues. This article documents the results. It also reveals correlations among philosophical views and between these views and factors such as age, gender, and nationality. A factor analysis suggests that an individual's views on these issues factor into a few underlying components that predict much of the variation in those views. The results of a metasurvey also suggest that many of the results of the survey are surprising: philosophers as a whole have quite inaccurate beliefs about the distribution of philosophical views in the profession.

In Philosophy Without Intuitions, Herman Cappelen focuses on the metaphilosophical thesis he calls Centrality: contemporary analytic philosophers rely on intuitions as evidence for philosophical theories. Using linguistic and textual analysis, he argues that Centrality is false. He also suggests that because most philosophers accept Centrality, they have mistaken beliefs about their own methods.To put my own views on the table: I do not have a large theoretical stake in the status of intuitions, but unreflectively I find it fairly obvious that many philosophers, including myself, appeal to intuitions. Cappelen’s arguments make a provocative challenge to this unreflective background conception. So it is interesting to work through the arguments to see what they might and might not show.In what follows I aim to articulate a minimal notion of intuition that captures something of the core everyday philosophical usage of the term, and that captures the sense ..

To make progress on the problem of consciousness, we have to confront it directly. In this paper, I first isolate the truly hard part of the problem, separating it from more tractable parts and giving an account of why it is so difficult to explain. I critique some recent work that uses reductive methods to address consciousness, and argue that such methods inevitably fail to come to grips with the hardest part of the problem. Once this failure is recognized, the door to further progress is opened. In the second half of the paper, I argue that if we move to a new kind of nonreductive explanation, a naturalistic account of consciousness can be given. I put forward my own candidate for such an account: a nonreductive theory based on principles of structural coherence and organizational invariance, and a double-aspect theory of information

First, I should clarify the notion of "taking consciousness seriously", which serves as a premise in my work. Mulhauser characterizes this as the assumption that no cognitive theory of consciousness will suffice. The latter assumption would indeed beg some crucial questions, but it is not the assumption that I make. I make an assumption about the problem of consciousness, not about any solution. To quote (p. xii): Throughout the book, I have assumed that consciousness exists, and that to redefine the problem as that of explaining how certain cognitive and behavioral functions are performed is unacceptable. This is what I mean by taking consciousness seriously. That is, the premise is simply that there is a phenomenon to be explained, and that the problems of explaining such functions as discrimination, integration, self monitoring, reportability, and so on do not exhaust all the problems in the vicinity. The deepest problem of consciousness, as I understand it, is not the problem of how all these functions are performed, but rather the problem of explaining how and why all this activity supports states of subjective experience.

It's very interesting to see neurophysiological evidence brought to bear on the puzzling question of conscious experience. Many have observed that information-processing models of cognition seem to leave consciousness untouched; it is natural to hope that turning to neurophysiology might lead us to the Holy Grail. Still, I think there are reasons to be skeptical. There are good reasons to suppose that neurophysiological investigation contributes to cognitive explanation at best in virtue of constraining the information-processing structure of cognition. Of course this is a very large and significant role for it to play, but it may be over-optimistic to suppose that it can play some further explanatory role, taking us where information-processing theories cannot. If so, then neurophysiological accounts will be no more and no less successful at dealing with consciousness than information-processing accounts are.

*[[I wrote this paper in January of 1990, but did not publish it because I was never entirely happy with it. My ideas on consciousness were in a state of flux, ultimately evolving into those represented in my book _The Conscious Mind_ (Oxford University Press, 1996). I now think that some parts of this paper are unsatisfactory, especially the positive theory outlined at the end, although a successor to that theory is laid out in the book. Nevertheless, I think the paper raises issues that need to be addressed. ]].

To clarify the notion of computation and its role in cognitive science, we need an account of implementation, the nexus between abstract computations and physical systems. I provide such an account, based on the idea that a physical system implements a computation if the causal structure of the system mirrors the formal structure of the computation. The account is developed for the class of combinatorial-state automata, but is sufficiently general to cover all other discrete computational formalisms. The implementation relation is non-vacuous, so that criticisms by Searle and others fail. This account of computation can be extended to justify the foundational role of computation in artificial intelligence and cognitive science.

What follows are compressed versions of three lectures on the subject of "Mind and Modality", given at Princeton University the week of October 12-16, 1998. The first two form a series; the third stands alone to some extent. All are philosophically technical, and probably of interest mainly to philosophers. I hope that they make sense, at least to those familiar with my book _The Conscious Mind_ . Lecture 1 recapitulates some of the material in the book in a somewhat different form, and adds some further material on conditionals and on Kripke. Note that section has a more or less definitive formalization of the anti-materialist argument from the book . Lecture 2 pushes deeper into the heart of modality, further investigating the conceivability/possibility relationship and the epistemology of modality , and arguing for a sort of modal rationalism. Lecture 3 gives an analysis of the content of beliefs about experiences, and applies this to a number of epistemological issues, including incorrigibility and the dialectic on "The Myth of the Given".

Computation is central to the foundations of modern cognitive science, but its role is controversial. Questions about computation abound: What is it for a physical system to implement a computation? Is computation sufficient for thought? What is the role of computation in a theory of cognition? What is the relation between different sorts of computational theory, such as connectionism and symbolic computation? In this paper I develop a systematic framework that addresses all of these questions. Justifying the role of computation requires analysis of implementation, the nexus between abstract computations and concrete physical systems. I give such an analysis, based on the idea that a system implements a computation if the causal structure of the system mirrors the formal structure of the computation. This account can be used to justify the central commitments of artificial intelligence and computational cognitive science: the thesis of computational sufficiency, which holds that the right kind of computational structure suffices for the possession of a mind, and the thesis of computational explanation, which holds that computation provides a general framework for the explanation of cognitive processes. The theses are consequences of the facts that computation can specify general patterns of causal organization, and mentality is an organizational invariant, rooted in such patterns. Along the way I answer various challenges to the computationalist position, such as those put forward by Searle. I close by advocating a kind of minimal computationalism, compatible with a very wide variety of empirical approaches to the mind. This allows computation to serve as a true foundation for cognitive science.

As I see it, the science of consciousness is all about relating _third-person data_ - about brain processes, behavior, environmental interaction, and the like - to _first-person data_ about conscious experience. I take it for granted that there are first-person data. It's a manifest fact about our minds that there is something it is like to be us - that we have subjective experiences - and that these subjective experiences are quite different at different times. Our direct knowledge of subjective experiences stems from our first-person access to them. And subjective experiences are arguably the central data that we want a science of consciousness to explain.

What happens when machines become more intelligent than humans? One view is that this event will be followed by an explosion to ever-greater levels of intelligence, as each generation of machines creates more intelligent machines in turn. This intelligence explosion is now often known as the “singularity”. The basic argument here was set out by the statistician I.J. Good in his 1965 article “Speculations Concerning the First Ultraintelligent Machine”: Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an “intelligence explosion”, and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make. The key idea is that a machine that is more intelligent than humans will be better than humans at designing machines. So it will be capable of designing a machine more intelligent than the most intelligent machine that humans can design. So if it is itself designed by humans, it will be capable of designing a machine more intelligent than itself. By similar reasoning, this next machine will also be capable of designing a machine more intelligent than itself. If every machine in turn does what it is capable of, we should expect a sequence of ever more intelligent machines. This intelligence explosion is sometimes combined with another idea, which we might call the “speed explosion”. The argument for a speed explosion starts from the familiar observation that computer processing speed doubles at regular intervals. Suppose that speed doubles every two years and will do so indefinitely. Now suppose that we have human-level artificial intelligence 1 designing new processors. Then faster processing will lead to faster designers and an ever-faster design cycle, leading to a limit point soon afterwards. The argument for a speed explosion was set out by the artificial intelligence researcher Ray Solomonoff in his 1985 article “The Time Scale of Artificial Intelligence”.1 Eliezer Yudkowsky gives a succinct version of the argument in his 1996 article “Staring at the Singularity”: “Computing speed doubles every two subjective years of work..

Hilary Putnam has argued that computational functionalism cannot serve as a foundation for the study of the mind, as every ordinary open physical system implements every finite-state automaton. I argue that Putnam's argument fails, but that it points out the need for a better understanding of the bridge between the theory of computation and the theory of physical systems: the relation of implementation. It also raises questions about the class of automata that can serve as a basis for understanding the mind. I develop an account of implementation, linked to an appropriate class of automata, such that the requirement that a system implement a given automaton places a very strong constraint on the system. This clears the way for computation to play a central role in the analysis of mind.

In article <[email protected]> [email protected] writes: Reminds me of a friend of mine who claims that the number 17 is "the most random" number. His proof ran as follows: pick a number. It's not really as good a random number as 17, is it? (Invariable Answer: "Umm, well, no...") This reminds me of a little experiment I did a couple of years ago. I stood on a busy street corner in Oxford, and asked passers by to "name a random number between zero and infinity." I was wondering what this "random" distribution would look like.

The Matrix presents a version of an old philosophical fable: the brain in a vat. A disembodied brain is floating in a vat, inside a scientist’s laboratory. The scientist has arranged that the brain will be stimulated with the same sort of inputs that a normal embodied brain receives. To do this, the brain is connected to a giant computer simulation of a world. The simulation determines which inputs the brain receives. When the brain produces outputs, these are fed back into the simulation. The internal state of the brain is just like that of a normal brain, despite the fact that it lacks a body. From the brain’s point of view, things seem very much as they seem to you and me.

It is fairly well-known that certain hard computational problems (that is, 'difficult' problems for a digital processor to solve) can in fact be solved much more easily with an analog machine. This raises questions about the true nature of the distinction between analog and digital computation (if such a distinction exists). I try to analyze the source of the observed difference in terms of (1) expanding parallelism and (2) more generally, infinite-state Turing machines. The issue of discreteness vs continuity will also be touched upon, although it is not so important for analyzing these particular problems.