Quantum Theory, the Chinese Room Argument and the * Symbol Grounding Problem Ravi Gomatam † [email protected] Abstract: I o ffer an alternative to Searle’s original Chinese Room argument which I call the Sanskrit Room argument (SRA). SRA distinguishes between syntactic TOKEN and semantic SYMBOL manipulations and shows that both are involved in human language understanding. Within classical mechanics, which gives an adequate scientific account of TOKEN manipulation, a symbol remains a subjective construct. I describe how an objective, quantitative theory of semantic symbols could be developed by applying the Schrodinger equation directly to macroscopic objects independent of Born’s rule and hence independent of current statistical quantum mechanics. Such a macroscopic quantum mechanics opens the possibility for developing a new theory of computing wherein the Universal Turing Machine (UTM) performs semantic symbol manipulation and models macroscopic quantum computing. 1. Introduction This paper seeks to relate quantum mechanics to artificial intelligence, human language, and cognition. It is a position paper, reporting some relevant details of a long-term research program currently in progress. In his justly famous Chinese Room Argument (CRA), Searle argued that “we could not give the capacity to understand Chinese or English [that humans evidently have] to a machine where the operation of the machine is solely defined as the instantiation of a computer program.” ([1], p. 422) Searle avoided the need to positively define ‘human understanding’ by relying on an operator (such as himself) who does not understand Chinese in any sense. Skipping the details of this well-known paper, Searle’s main conclusions were as follows: 1. At present, digital computers only perform symbol manipulation. 2. The CRA shows that symbol manipulation alone will never lead the operator to understand *Originally published in Bruza, P. et al. (Eds.), Quantum Interaction 2009, Lecture Notes in Computer Science, Volume 5494, pp. 174-183, Springer-Verlag: Berlin, Heidelberg †Institute of Semantic Information Sciences and Technology, 2334 Stuart St, Berkeley, CA 94705; Juhu Road, Juhu, Mumbai 400 049 (www.isist.info) 1 Chinese; thus an equivalent digital computer cannot be said to understand Chinese. 3. Hence, the thesis of Strong AI is false. An appropriately programmed digital computer may be able to simulate human thinking and intelligence, but it cannot be said to literally possess cognitive states. Searle identifies the Chinese language letters only in terms of their shape, and refers to them as symbols. But the term “symbol” implies something more. Henceforth I shall distinguish between syntactic TOKENS (letters qua shapes) and semantic SYMBOLS (letters qua components of a language, generating meaning).‡ Under this terminology, Searle’s argument is that computers, by virtue of performing token manipulation alone, cannot demonstrate understanding of Chinese. I wish to show that the CRA does not preclude in any way a more general form of the strong-AI thesis, namely that present digital computers can be said to possess some portion of the abilities constituting human ‘understanding’. To this end, without much ado, I o ffer an alternative to the CRA, which I shall call the “Sanskrit Room argument”. 2. The Sanskrit Room Argument (SRA) The Sanskrit Room is similar to the Chinese Room in all respects but two. The language spoken is Sanskrit§, and the person inside (say, myself) understands Sanskrit. There are now two possibilities: Scenario 1: I ignore my ability to understand the questions, merely decomposing the queries into a sequence of shapes. I then string together replies from baskets containing a good supply of these same shapes, following a hypothetical program. As with the CRA, let us say that I pass the Turing test in this mode without di fficulty. Scenario 2: I discard the program, and use my Sanskrit skills to construct an appropriate response from the store of language symbols . In this case I should also pass the Turing test. To an outsider, my behavior is the same in both cases. However, I hold it as self-evident that I am doing something fundamentally di fferent in each scenario. In the first, I am mimicking Searle’s operator in the Chinese room (who doesn’t know the language) and am doing solely token manipulation. In the second, I understand the input and answer on my own (without reference to the program). I am therefore manipulating semantic symbols. But I am also doing token manipulation at the level of physically stringing together letters. Thus, unlike the CRA, the SRA gives some insight into the nature of human language processing. It involves both token and symbol manipulation at the level of the subject. ‡ TOKENS are also referred to in the literature as physical symbols. § Although for the purposes of this paper I could simply replace Searle by a native Chinese speaker, I shift to Sanskrit for a reason. It is a well-discussed topic in the field of natural language processing (NLP) that Panini’s grammar can generate semantically valid sentences in Sanskrit to an extent that has so far been impossible for other natural languages [2]. I expect the idea that the individual letters in Sanskrit carry semantic content, in a manner possibly unique to that language, to become relevant to the issue at hand in future publications. 2 I now turn to a key question: Are there any di fferences between token and symbol manipulation at the objective level? 3. TOKENS and Classical Physics I shall assume metaphysical realism to start with, and refer to the external identity of objects I manipulate as the Kantian things-in-themselves (or THINGS ). From this viewpoint, the terms token and symbol both refer to our subjective conceptions of the THING . In standard philosophical parlance, tokens and symbols are phenomenal objects given in our experience. We objectify phenomenal tokens as res extensa in everyday thinking and in physics. That is, we endow them with an absolute location, mass and derived extensional attributes such as velocity, acceleration, etc. We believe that there is an object out there with features that correspond in every detail to our conception of the phenomenal token. We might call this belief the classical version of direct realism. Classical direct realism is referred to as naive realism in everyday thinking, and as correspondence realism in physics. ** Let us refer to the phenomenal token objectified in classical realism as a TOKEN . It is a physical object defined entirely in terms of its primary physical properties. On this basis, we could say that I am manipulating objective TOKENS in Scenario-1. Classical physics su ffices to explain how I can recognize and manipulate objects qua TOKENS . The operation of the digital computers is presently understood in similar classical terms, rendering the computer a token manipulating system. From this perspective, any symbol manipulation (and thus ‘understanding’) attributed to the computer (or, to the human operator in Scenario-1 of SRA) becomes our interpretation of operations that objectively must be considered solely syntactic. However, this is more a limitation of our classical point of view than an intrinsic limitation of the computer (or the human operator). We must find a way to overcome this limitation, since classical physics itself has been superseded by quantum theory. This paper will propose that present-day computers can be understood as objectively performing, not just TOKEN manipulation but also SYMBOL manipulation, provided we apply the Schrodinger equation to macroscopic objects in a new manner. †† To develop this proposal further, we need to move toward a direct realist view of phenomenal symbols in everyday thinking and physics, one which is complementary to our direct realist view of the phenomenal tokens. That is to say, just as classical physics and its associated ordinary language-based thinking link our sense experiences to objective TOKENS , phenomenal symbols can be given objective counterparts in a new physics and its associated range of everyday thinking. I call these objective counterparts SYMBOLS . I will now argue that the quantum formalism, if properly applied to the macroscopic world, can serve as the basis for a scientific theory of SYMBOLS in physics. ** It is now recognized that a logical defense of scientific realism is highly problematic, even with regard to pre-quantum theories. (See reference [3] for an easy introduction to the topic.) The underdetermination of theory by data and pessimistic meta-induction are but two reasons. Nevertheless, physicists maintain a realist view of their theories based on pragmatic success. Likewise, it has been impossible to logically justify naive version of direct realism in everyday thinking too. Yet this viewpoint is embraced by all of us uncritically, again on pragmatic grounds. Physicists too rely on naive realism, at the level of reporting their laboratory observations using ordinary language. †† While it is true that present SQM has nothing to o ffer to neuroscience (see [4]), the move being outlined here would be of significance to neuroscience, and for our scientific understanding of symbol manipulation at the human level. 3 4. SYMBOLS and Macroscopic Quantum Mechanics Quantum theory arose out of the failure of classical physics to give an account of certain phenomena (blackbody radiation, the photoelectric e ffect, and atomic spectra). In statistical quantum mechanics, atomic objects acquire context sensitive properties reminiscent of symbols. This feature suggests that quantum mechanics, if it can be directly applied to the macroscopic world, could provide a basis for physically treating phenomenal symbols on a non-classical footing, i.e. as physical objects other than TOKENS . Indeed, Schrodinger’s equation in principle applies to the world of tables and chairs and measuring devices just as much as it applies to the world of atomic particles. The modern practice of statistical quantum mechanics (SQM), however, presupposes the classicality of the everyday world in general, and our measurement devices in particular.