Abstract
This paper seeks to understand machine cognition. The nature of machine cognition has been shrouded in incomprehensibility. We have often encountered familiar arguments in cognitive science that human cognition is still faintly understood. This paper will argue that machine cognition is far less understood than even human cognition despite the fact that a lot about computer architecture and computational operations is known. Even if there have been putative claims about the transparency of the notion of machine computations, these claims do not hold out in unraveling machine cognition, let alone machine consciousness (if there is any such thing). The nature and form of machine cognition remains further confused also because of attempts to explain human cognition in terms of computation and to model/simulate (aspects of) human cognitive processing in machines. Given that these problems in characterizing machine cognition persist, a view of machine cognition that aims to avoid these problems is outlined. The argument that is advanced is that something becomes a computation in machines only when a human interprets it, which is a kind of semiotic causation. From this it follows that a computing machine is not engaged in a computation unless a human interprets what it is doing; instead, it is engaged in machine cognition, which is defined as a member or subset of the set of all possible mappings of inputs to outputs. The human interpretation, which is a semiotic process, gives meaning to what a machine does, and then what it does becomes a computation.
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Notes
In this connection, one may also relate this question to Rosen’s (1991) notion of a complex system within which organization is significant insofar as organizational principles within a system as a whole causally determine the relations and processes that obtain in a system. In the current context, this means that the organizational principles pervade and encompass a systemic whole that connects machine cognition by way of computation to the human cognitive system at a more fundamental (ontological) level of organization.
Not everybody who believes in computationalism thinks that human cognition must be identified with computation, and thus the claim that human cognition is something that cannot be implemented or simulated in machines is a weak objection in this sense. For instance, it is possible to say, by following Rapaport (2012), that human cognition is computable, regardless of whether human cognition is computation or not. However, this does not affect the force of the arguments marshaled in this paper.
One needs to be cautious about relating this to the causal-informational view of semantics, as evident in Dretske (1981), Fodor (1998). The causal-informational view of semantics demands that a causal relation—direct or mediated—obtain between the objects and the concepts or expressions that refer to those objects. If the human interpretaion by virtue of humans’ intrinsic intentionality possesses causal powers, these causal powers must have derived from the human intrinsic intentionality which is a primitive concept and cannot be further decomposed (Jacquette 2011). Taken in this sense, neither expressions/signs nor objects can in themselves cause or causally determine anything in the mind (in contrast to the view espoused by the proponents of causal-informational semantics), since all relations—causal or otherwise—are distilled and derived from the the human intrinsic intentionality. And if this is so, the causality of the human interpretation process derived from humans’ intrinsic intentionality does not also need to be caused by anything else, mainly because humans’ intrinsic intentionality is primary and more fundamental than anything else in nature.
As has been pointed out by an anonmyous reviewer of this paper, if machine cognition is potential computation that is ‘harvested’ by human cognition through a semiotic interpretation process, it would be interesting to see why this could not change. One may thus wonder what would happen when a machine can repair itself or perhaps even ‘reproduce’. It needs to be made explicit that the current view does not say that machine cognition can be derived from human cognition. Rather, machine cognition exists in a different domain—more particularly, in a domain of possibilities of mapping from inputs at some level of a machine state to outputs at some level of description of any other physical system, whereas (machine) computation is a consequence of the human inetrpretation defining some relation (that may well be a function) on the abstract trajectory through s constituting machine cognition. In this sense, when a machine can repair itself or perhaps even ‘reproduce’, and if the repair and reproduction have been possible through the implementaion of some program(s) designed by humans, it is computations all the way repairing (or renovating) and reproducing computations further and further into a direction away from machine cognition. Therefore, machine cognition precedes any such implementaion of some program(s) designed by humans in a machine that can repair itself or perhaps even ‘reproduce’. And thus machine cognition may remain where it is, irrespective of whether the machine concerned repairs itself or even ‘reproduces’, or not.
References
Bishop, J. M. (2009). A cognitive computation fallacy? Cognition, computations and panpsychism. Cognitive Computation, 1, 221–233.
Chalmers, J. D. (2012). A computational foundation for the study of cognition. The Journal of Cognitive Science, 12(4), 323–357.
Deacon, T. (2012). Incomplete nature: How mind emerged from matter. New York: Norton.
Dennett, D. (1996). The intentional stance. Cambridge: MIT Press.
Dietrich, E., & Markman, A. (2003). Discrete thoughts: why cognition must use discrete representations. Mind and Language, 18(1), 95–119.
Dretske, F. (1981). Knowledge and the flow of information. Cambridge: MIT Press.
Dreysus, H. (1992). What computers still can’t do: A critique of artificial reason. Cambridge: MIT Press.
Fodor, J. (1975). The language of thought. Cambridge: Harvard University Press.
Fodor, J. (1998). Concepts: Where cognitive science went wrong. Oxford: Oxford University Press.
Fresco, N. (2011). Concrete digital computation: what does it take for a physical system to compute? Journal of Logic, Language and Information, 20, 513–537.
Fresco, N. (2012). The explanatory role of computation in cognitive science. To appear in Minds and Machines.
Gazzaniga, M. S. (2009). The cognitive neurosciences (4th ed.). Cambridge: MIT Press.
Goertzel, B. (2007). Human level artificial intelligence and the possibility of a technological singularity. Artificial Intelligence, 171, 1161–1173.
Haugeland, J. (1998). Having thought: Essays in the metaphysics of mind. Cambridge: Harvard University Press.
Hoffmeyer, J. (2007). Semiotic scaffolding of living systems. In M. Barbieri (Ed.), Introduction to biosemiotics (pp. 149–166). Berlin: Springer.
Jacquette, D. (2011). Intentionality as a conceptually primitive relation. Acta Analytica, 26, 15–35.
McCarthy, J. (2007). From here to human-level AI. Artificial Intelligence, 171, 1174–1182.
Minsky, M. (2006). The emotion machine. New York: Simon & Schuster.
Pattee, H. H. (2008). Physical and functional conditions for symbols, codes and languages. Biosemiotics, 1, 147–168.
Penrose, R. (1994). Shadows of the mind: A search for the missing science of consciousness. Oxford: Oxford University Press.
Piccinini, G., & Scarantino, A. (2011). Information processing, computation and cognition. Journal of Biological Physics, 37, 1–38.
Proudfoot, D. (2011). Anthropomorphism and AI: turing’s much misunderstood imitation game. Artificial Intelligence, 175, 950–957.
Putnam, H. (1988). Representation and reality. Cambridge: MIT Press.
Pylyshyn, Z. (1984). Computation and cognition: Toward a foundation for cognitive science. Cambridge: MIT Press.
Rapaport, W. J. (2012). Semiotic systems, computers, and the mind: how cognition could be computing. International Journal of Signs and Semiotic Systems, 2(1), 32–71.
Rosen, R. (1991). Life itself: A comprehensive inquiry into the nature, origin, and fabrication of life. New York: Columbia University Press.
Rosen, R. (2000). Essays on life itself. New York: Columbia University Press.
Searle, J. (1992). The rediscovery of the mind. Cambridge: MIT Press.
Shagrir, O. (2006). Why we view the brain as a computer. Synthese, 153, 393–416.
Shagrir, O. (2012). Computation, implementation, cognition. Minds and Machines, 2(22), 137–148.
Smith, B. C. (1996). On the origin of objects. Cambridge: MIT Press.
Starzyk, J. A., & Prasad, D. K. (2011). A computational model of machine consciousness. International Journal of Machine Consciousness, 3, 237–253.
Tallis, R. (2011). Aping mankind: Neuromania, darwinitis and the misrepresentation of humanity. Durham: Acumen.
Taylor, J. (1991). Can neural networks ever be made to think? Neural Network World, 1, 4–11.
Tønnessen, M. (2010). Steps to a semiotics of being. Biosemiotics, 3, 375–392.
Torey, Z. (2009). The crucible of consciousness: An integrated theory of mind and brain. Cambridge: MIT Press.
Acknowledgments
I am thankful to one anonymous reviewer of this paper for making significant comments on certain issues dealt with in this paper, and for drawing my attention to some points that I overlooked.
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Mondal, P. Does Computation Reveal Machine Cognition?. Biosemiotics 7, 97–110 (2014). https://doi.org/10.1007/s12304-013-9179-3
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DOI: https://doi.org/10.1007/s12304-013-9179-3