Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!eecae!netnews.upenn.edu!rutgers!elbereth.rutgers.edu!harnad From: harnad@elbereth.rutgers.edu (Stevan Harnad) Newsgroups: comp.ai Subject: Re: Question on Chinese Room Argument Summary: Summary of the argument, as requested Message-ID: Date: 22 Feb 89 20:20:27 GMT References: <4298@pt.cs.cmu.edu> <563@aipna.ed.ac.uk> Organization: Rutgers Univ., New Brunswick, N.J. Lines: 140 rjc@aipna.ed.ac.uk (Richard Caley) of Dept. of AI, Edinburgh, UK asks: " If the argument ["showing that it's only the symbolic approach to " modeling the mind that's vulnerable to Searle's Argument"] could be " truncated to a reasonable length, then I would be interested if you " posed it. I don't see why, say, searle in a room pulling strings and " waving springs (or doing something else equally non-symbolic ) which " happens to produce behaviour like a chinese speaker would not be the " basis for a precicely parallel argument. I'm not saying you are wrong, " just that it is not obvious. Here it is, pp. 20-21 from Harnad, S. (1989) Minds, Machines and Searle. Journal of Experimental and Theoretical Artificial Intelligence 1: 5-25. See especially points (7) and (8): Searle's provocative "Chinese Room Argument" attempted to show that the goals of "Strong AI" are unrealizable. Proponents of Strong AI are supposed to believe that (i) the mind is a computer program, (ii) the brain is irrelevant, and (iii) the Turing Test is decisive. Searle's argument is that since the programmed symbol-manipulating instructions of a computer capable of passing the Turing Test for understanding Chinese could always be performed instead by a person who could not understand Chinese, the computer can hardly be said to understand Chinese. Such "simulated" understanding, Searle argues, is not the same as real understanding, which can only be accomplished by something that "duplicates" the "causal powers" of the brain. The following points have been made in this paper: (1) Simulation versus Implementation: Searle fails to distinguish between the simulation of a mechanism, which is only the formal testing of a theory, and the implementation of a mechanism, which does duplicate causal powers. Searle's "simulation" only simulates simulation rather than implementation. It can no more be expected to understand than a simulated airplane can be expected to fly. Nevertheless, a successful simulation must capture formally all the relevant functional properties of a successful implementation. (2) Theory-Testing versus Turing-Testing: Searle's argument conflates theory-testing and Turing-Testing. Computer simulations formally encode and test models for human perceptuomotor and cognitive performance capacities; they are the medium in which the empirical and theoretical work is done. The Turing Test is an informal and open-ended test of whether or not people can discriminate the performance of the implemented simulation from that of a real human being. In a sense, we are Turing-Testing one another all the time, in our everyday solutions to the "other minds" problem. (3) The Convergence Argument: Searle fails to take underdetermination into account. All scientific theories are underdetermined by their data; i.e., the data are compatible with more than one theory. But as the data domain grows, the degrees of freedom for alternative (equiparametric) theories shrink. This "convergence" constraint applies to AI's "toy" linguistic and robotic models too, as they approach the capacity to pass the Total (asymptotic) Turing Test. Toy models are not modules. (4) Brain Modeling versus Mind Modeling: Searle also fails to appreciate that the brain itself can be understood only through theoretical modeling, and that the boundary between brain performance and body performance becomes arbitrary as one converges on an asymptotic model of total human performance capacity. (5) The Modularity Assumption: Searle implicitly adopts a strong, untested "modularity" assumption to the effect that certain functional parts of human cognitive performance capacity (such as language) can be be successfully modeled independently of the rest (such as perceptuomotor or "robotic" capacity). This assumption may be false for models approaching the power and generality needed to pass the Turing Test. (6) The Teletype Turing Test versus the Robot Turing Test: Foundational issues in cognitive science depend critically on the truth or falsity of such modularity assumptions. For example, the "teletype" (linguistic) version of the Turing Test could in principle (though not necessarily in practice) be implemented by formal symbol-manipulation alone (symbols in, symbols out), whereas the robot version necessarily calls for full causal powers of interaction with the outside world (seeing, doing AND linguistic competence). (7) The Transducer/Effector Argument: Prior "robot" replies to Searle have not been principled ones. They have added on robotic requirements as an arbitrary extra constraint. A principled "transducer/effector" counterargument, however, can be based on the logical fact that transduction is necessarily nonsymbolic, drawing on analog and analog-to-digital functions that can only be simulated, but not implemented, symbolically. (8) Robotics and Causality: Searle's argument hence fails logically for the robot version of the Turing Test, for in simulating it he would either have to USE its transducers and effectors (in which case he would not be simulating all of its functions) or he would have to BE its transducers and effectors, in which case he would indeed be duplicating their causal powers (of seeing and doing). (9) Symbolic Functionalism versus Robotic Functionalism: If symbol-manipulation ("symbolic functionalism") cannot in principle accomplish the functions of the transducer and effector surfaces, then there is no reason why every function in between has to be symbolic either. Nonsymbolic function may be essential to implementing minds and may be a crucial constituent of the functional substrate of mental states ("robotic functionalism"): In order to work as hypothesized (i.e., to be able to pass the Turing Test), the functionalist "brain-in-a-vat" may have to be more than just an isolated symbolic "understanding" module -- perhaps even hybrid analog/symbolic all the way through, as the real brain is, with the symbols "grounded" bottom-up in nonsymbolic representations. (10) "Strong" versus "Weak" AI: Finally, it is not at all clear that Searle's "Strong AI"/"Weak AI" distinction captures all the possibilities, or is even representative of the views of most cognitive scientists. Much of AI is in any case concerned with making machines do intelligent things rather than with modeling the mind. Hence, most of Searle's argument turns out to rest on unanswered questions about the modularity of language and the scope and limits of the symbolic approach to modeling cognition. If the modularity assumption turns out to be false, then a top-down symbol-manipulative approach to explaining the mind may be completely misguided because its symbols (and their interpretations) remain ungrounded -- not for Searle's reasons (since Searle's argument shares the cognitive modularity assumption with "Strong AI"), but because of the transdsucer/effector argument (and its ramifications for the kind of hybrid, bottom-up processing that may then turn out to be optimal, or even essential, in between transducers and effectors). What is undeniable is that a successful theory of cognition will have to be computable (simulable), if not exclusively computational (symbol-manipulative). Perhaps this is what Searle means (or ought to mean) by "Weak AI." -- Stevan Harnad INTERNET: harnad@confidence.princeton.edu harnad@princeton.edu srh@flash.bellcore.com harnad@elbereth.rutgers.edu harnad@princeton.uucp BITNET: harnad@pucc.bitnet CSNET: harnad%princeton.edu@relay.cs.net (609)-921-7771