Minds and Machines

  The concept of representation has been a key element in the scientific study of mental processes, ever since such studies
 commenced. However, usage of the term has been all but too liberal'if one were to adhere to common use it remains unclear
 if there are examples of physical systems which cannot be construed in terms of representation. The problem is considered
 afresh, taking as the staring point the notion of activity spaces'spaces of spatiotemporal events produced by dynamical systems.
 It is argued that representation can be analyzed in terms of the geometrical and topological properties of such spaces. Several
 attributes and processes associated with conceptual domains, such as logical structure, generalization and learning are considered,
 and given analogues in structural facets of activity spaces, as are misrepresentation and states of arousal. Based on this
 analysis, representational systems are defined, as is a key concept associated with such systems, the notion of representational
 capacity. According to the proposed theory, rather than being an all or none phenomenon, representation is in fact a matter
 of degree'that is can be associated with measurable quantities, as is behooving of a putative naturalistic construct.
 
	

  In Darwin's Dangerous Idea, Daniel Dennett claims that evolution is algorithmic. On Dennett's analysis, evolutionary processes are trivially algorithmic
 because he assumes that all natural processes are algorithmic. I will argue that there are more robust ways to understand
 algorithmic processes that make the claim that evolution is algorithmic empirical and not conceptual. While laws of nature
 can be seen as compression algorithms of information about the world, it does not follow logically that they are implemented
 as algorithms by physical processes. For that to be true, the processes have to be part of computational systems. The basic
 difference between mere simulation and real computing is having proper causal structure. I will show what kind of requirements
 this poses for natural evolutionary processes if they are to be computational.
 
	

  Computationalism, a specie of functionalism, posits that a mental state like pain is realized by a ˜core' computational state
 within a particular causal network of such states. This entails that what is realized by the core state is contingent on events
 remote in space and time, which puts computationalism at odds with the locality principle of physics. If computationalism
 is amended to respect locality, then it posits that a type of phenomenal experience is determined by a single type of computational
 state. But a computational state, considered by itself, is of no determinate type'it has no particular symbolic content, since
 it could be embedded in any of an infinite number of algorithms. Hence, if locality is respected, then the type of experience
 that is realized by a computational state, or whether any experience at all is realized, is under-determined by the computational
 nature of the state. Accordingly, Block's absent and inverted qualia arguments against functionalism find support in the locality
 principle of physics. If computationalism denies locality to avoid this problem, then it cannot be considered a physicalist
 theory since it would entail a commitment to phenomena, like teleological causation and action-at-a-distance, that have long
 been rejected by modern science. The remaining theoretical alternative is to accept the locality principle for macro events
 and deny that formal, computational operations are sufficient to realize a phenomenal mental state.
 
	

  Recent trends towards an e-Science offer us the opportunity to think about the specific epistemological changes created by
 computational empowerment in scientific practices. In fact, we can say that a computational epistemology exists that requires
 our attention. By ˜computational epistemology' I mean the computational processes implied or required to achieve human knowledge.
 In that category we can include AI, supercomputers, expert systems, distributed computation, imaging technologies, virtual
 instruments, middleware, robotics, grids or databases. Although several authors talk about the extended mind and computational
 extensions of the human body, most of these proposals don't analyze the deep epistemological implications of computer empowerment
 in scientific practices. At the same time, we must identify the principal concept for e-Science: Information. Why should we think about a new epistemology for e-Science? Because several processes exist around scientific information
 that require a good epistemological model to be understood.
 
	

  What I call semiotic brains are brains that make up a series of signs and that are engaged in making or manifesting or reacting to a series of signs:
 through this semiotic activity they are at the same time engaged in being minds' and so in thinking intelligently. An important
 effect of this semiotic activity of brains is a continuous process of disembodiment of mind that exhibits a new cognitive
 perspective on the mechanisms underling the semiotic emergence of meaning processes. Indeed at the roots of sophisticated
 thinking abilities there is a process of disembodiment of mind that presents a new cognitive perspective on the role of external
 models, representations, and various semiotic materials. Taking advantage of Turing's comparison between unorganized' brains
 and logical' and practical' machines' this paper illustrates the centrality to cognition of the disembodiment of mind from
 the point of view of the interplay between internal and external representations, both mimetic and creative. The last part
 of the paper describes the concept of mimetic mind I have introduced to shed new cognitive and philosophical light on the role of computational modeling and on the decline
 of the so-called Cartesian computationalism.
 
	

Andy Clark: Supersizing the Mind
	

Jerry Fodor, LOT 2: The Language of Thought Revisited, New York: Oxford University Press, 2008, x+228, $37.95, ISBN 978-0-119-954877-4
	

  Hypercomputation'the hypothesis that Turing-incomputable objects can be computed through infinitary means'is ineffective,
 as the unsolvability of the halting problem for Turing machines depends just on the absence of a definite value for some paradoxical
 construction; nature and quantity of computing resources are immaterial. The assumption that the halting problem is solved
 by oracles of higher Turing degree amounts just to postulation; infinite-time oracles are not actually solving paradoxes,
 but simply assigning them conventional values. Special values for non-terminating processes are likewise irrelevant, since
 diagonalization can cover any amount of value assignments. This should not be construed as a restriction of computing power:
 Turing's uncomputability is not a ˜barrier' to be broken, but simply an effect of the expressive power of consistent programming
 systems.
 
	

  From an object-oriented perspective, this paper investigates the interdisciplinary aspects of problem representation as well
 the differences between representation of problems in the mind and that in the machine. By defining an object as a combination
 of a symbol-structure and its associated operations, it shows how the representation of problems can become related to control,
 which conducts the search in finding a solution. Different types of representation of problems in the machine are classified
 into four categories, and in a similar way four distinct models are distinguished for the representation of problems in the
 mind. The concept of layered hierarchies, as the main theme of the object-oriented paradigm, is used to examine the implications
 of problem representation in the mind for improving the representation of problems in the machine.
 
	

Paul A. Boghossian, Fear of Knowledge: Against Relativism and Constructivism
	

  This article deals with the links between the enaction paradigm and artificial intelligence. Enaction is considered a metaphor
 for artificial intelligence, as a number of the notions which it deals with are deemed incompatible with the phenomenal field
 of the virtual. After explaining this stance, we shall review previous works regarding this issue in terms of artificial life
 and robotics. We shall focus on the lack of recognition of co-evolution at the heart of these approaches. We propose to explicitly
 integrate the evolution of the environment into our approach in order to refine the ontogenesis of the artificial system,
 and to compare it with the enaction paradigm. The growing complexity of the ontogenetic mechanisms to be activated can therefore
 be compensated by an interactive guidance system emanating from the environment. This proposition does not however, resolve
 that of the relevance of the meaning created by the machine (sense-making). Such reflections lead us to integrate human interaction
 into this environment in order to construct relevant meaning in terms of participative artificial intelligence. This raises
 a number of questions with regards to setting up an enactive interaction. The article concludes by exploring a number of issues,
 thereby enabling us to associate current approaches with the principles of morphogenesis, guidance, the phenomenology of interactions
 and the use of minimal enactive interfaces in setting up experiments which will deal with the problem of artificial intelligence
 in a variety of enaction-based ways.
 
	

  Darwin's insight that species are mutable, and descent, and origin by means of natural selection is one of the most widely
 acknowledged strategies for the origin of species and their survival in nature. In his famous contribution, however, Darwin
 also writes that he is convinced that ... Natural Selection has been the main but not exclusive means of modification' (Darwin in The origin of species. Oxford Univeristy Press, Oxford, p. 7, 1996). This research suggests robustness as another fundamental strategy for survival in nature. The paper does not contradict
 the popular view, which usually sees robustness as a feature making systems fault-tolerant, thereby focusing on the identification
 of strategies and techniques for making systems robust (i.e., how to achieve robustness). The paper rather extends this view
 with an interpretation resting on the question'WHY is robustness omnipresent in the world around us? From this point of view,
 robustness is interpreted as a fundamental mechanism that is in place because of another fundamental feature in nature'the
 design and use of sub-optimal systems. The paper argues that, in a sense, nature under-specifies systems but compensates for
 this by providing systems with various degrees of robustness. We believe that this interpretation may lead to fundamentally
 new design approaches and insights in several fields.
 
	

  That the successful development of fully autonomous artificial moral agents (AMAs) is imminent is becoming the received view
 within artificial intelligence research and robotics. The discipline of Machines Ethics, whose mandate is to create such ethical
 robots, is consequently gaining momentum. Although it is often asked whether a given moral framework can be implemented into
 machines, it is never asked whether it should be. This paper articulates a pressing challenge for Machine Ethics: To identify
 an ethical framework that is both implementable into machines and whose tenets permit the creation of such AMAs in the first
 place. Without consistency between ethics and engineering, the resulting AMAs would not be genuine ethical robots, and hence
 the discipline of Machine Ethics would be a failure in this regard. Here this challenge is articulated through a critical
 analysis of the development of Kantian AMAs, as one of the leading contenders for being the ethic that can be implemented
 into machines. In the end, however, the development of Kantian artificial moral machines is found to be anti-Kantian. The
 upshot of all this is that machine ethicists need to look elsewhere for an ethic to implement into their machines.
 
	

  The term uncanny valley' goes back to an article of the Japanese roboticist Masahiro Mori (Mori 1970, 2005). He put forward the hypothesis that humanlike objects like certain kinds of robots elicit emotional responses similar to
 real humans proportionate to their degree of human likeness. Yet, if a certain degree of similarity is reached emotional responses
 become all of a sudden very repulsive. The corresponding recess in the supposed function is called the uncanny valley. The
 present paper wants to propose a philosophical explanation why we feel empathy with inanimate objects in the first place,
 and why the uncanny valley occurs when these objects become very humanlike. The core of this explanation'which is informed
 by the recently developing empirical research on the matter'will be a form of empathy involving a kind of imaginative perception.
 However, as will be shown, imaginative perception fails in cases of very humanlike objects.
 
	

  According to Ramsey (Representation reconsidered, Cambridge University Press, New York, 2007), only classical cognitive science, with the related notions of input“output and structural representations, meets the job
 description challenge (the challenge to show that a certain structure or process serves a representational role at the subpersonal
 level). By contrast, connectionism and other nonclassical models, insofar as they exploit receptor and tacit notions of representation,
 are not genuinely representational. As a result, Ramsey submits, cognitive science is taking a U-turn from representationalism
 back to behaviourism, thus presupposing that (1) the emergence of cognitivism capitalized on the concept of representation,
 and that (2) the materialization of nonclassical cognitive science involves a return to some form of pre-cognitivist behaviourism.
 We argue against both (1) and (2), by questioning Ramsey's divide between classical and representational, versus nonclassical
 and nonrepresentational, cognitive models. For, firstly, connectionist and other nonclassical accounts have the resources
 to exploit the notion of a structural isomorphism, like classical accounts (the beefing-up strategy); and, secondly, insofar
 as input“output and structural representations refer to a cognitive agent, classical explanations fail to meet the job description
 challenge (the deflationary strategy). Both strategies work independently of each other: if the deflationary strategy succeeds,
 contra (1), cognitivism has failed to capitalize on the relevant concept of representation; if the beefing-up strategy is
 sound, contra (2), the return to a pre-cognitivist era cancels out.
 
	

  Searle's Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence
 and cognitive science since its initial presentation in ˜Minds, Brains and Programs' in 1980. It is by no means an overstatement
 to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. It is then
 especially interesting to note that relatively little has been said about the detailed logic of the argument, whatever significance
 Searle intended CRA to have. The problem with the CRA is that it involves a very strong modal claim, the truth of which is
 both unproved and highly questionable. So it will be argued here that the CRA does not prove what it was intended to prove.
 
	

  John Searle believes that computational properties are purely formal and that consequently, computational properties are not
 intrinsic, empirically discoverable, nor causal; and therefore, that an entity's having certain computational properties could
 not be sufficient for its having certain mental properties. To make his case, Searle's employs an argument that had been used
 before him by Max Newman, against Russell's structuralism; one that Russell himself considered fatal to his own position.
 This paper formulates a not-so-explored version of Searle's problem with computational cognitive science, and refutes it by
 suggesting how our understanding of computation is far from implying the structuralism Searle vitally attributes to it. On
 the way, I formulate and argue for a thesis that strengthens Newman's case against Russell's structuralism, and thus raises
 the apparent risk for computational cognitive science too.
 
	

  Explanations of how psychological capacities are carried out often invoke functional brain areas. I argue that such explanations
 cannot succeed. Psychological capacities are carried out by identifiable entities and their activities in the brain, but functional
 brain areas are not the relevant entities. I proceed by assuming that if functional brain areas did carry out psychological
 capacities, then these brain areas could be included in descriptions of mechanisms. And if functional brain areas participate
 in mechanisms, then they must engage in activities. A number of ways in which we might understand the claim that functional
 brain areas engage in activities are examined. None are successful, and so one conclusion is that functional brain areas do
 not participate in mechanisms. Consequently, they are not the entities that carry out psychological capacities.
 
	

  Kripke (Wittgenstein on rules and private language: an elementary exposition. Harvard University Press, Cambridge Mass, 1982) rejected a naturalistic dispositional account of meaning (hereafter semantic dispositionalism) in a skeptical argument about
 rule-following he attributes to Wittgenstein (Philosophical investigation. Basil Blackwell, Oxford, 1958). Most philosophers who oppose Kripke's criticisms of semantic dispositionalism take the stance that the argument proves
 too much: semantic dispositionalism is similar to much of our respected science in some important aspects, and hence to discard
 the former would mean to give up the latter, which is obviously wrong. In this paper, I shall discuss and reject a recent
 defense of Kripke by Kusch (Analysis 65(2):156“163 2005; Sceptical guide to meaning and rules: defending Kripke's Wittgenstein. McGill-Queen's, London, 2006). Kusch attempts to show that semantic dispositionalism differs from the sciences, and consequently, Kripke's attack can
 only target semantic dispositionalism, but not the sciences. Specifically, Kusch identifies some important features of the
 sciences with regard to how it employs idealization and ceteris paribus clauses, and argues that the ways in which semantic dispositionalism uses them are dramatically different. I argue that,
 upon close examination, the two are more similar than otherwise in each of those features.
 
	

  Turing's Imitation Game is often viewed as a test for theorised machines that could ˜think' and/or demonstrate ˜intelligence'.
 However, contrary to Turing's apparent intent, it can be shown that Turing's Test is essentially a test for humans only. Such
 a test does not provide for theorised artificial intellects with human-like, but not human-exact, intellectual capabilities.
 As an attempt to bypass this limitation, I explore the notion of shifting the goal posts of the Turing Test, and related tests
 such as the Total Turing Test, away from the exact imitation of human capabilities, and towards communication with humans instead. While the continued philosophical relevance of such tests is open to debate, the outcome is a different
 class of tests which are, unlike the Turing Test, immune to failure by means of sub-cognitive questioning techniques. I suggest
 that attempting to instantiate such tests could potentially be more scientifically and pragmatically relevant to some Artificial
 Intelligence researchers, than instantiating a Turing Test, due to the focus on producing a variety of goal directed outcomes
 through communicative methods, as opposed to the Turing Test's emphasis on ˜fooling' an Examiner.