The paper presents a paradoxical feature of computational systems that suggests that computationalism cannot explain symbol grounding. If the mind is a digital computer, as computationalism claims, then it can be computing either over meaningful symbols or over meaningless symbols. If it is computing over meaningful symbols its functioning presupposes the existence of meaningful symbols in the system, i.e. it implies semantic nativism. If the mind is computing over meaningless symbols, no intentional cognitive processes are available prior to symbol grounding. (...) In this case, no symbol grounding could take place since any grounding presupposes intentional cognitive processes. So, whether computing in the mind is over meaningless or over meaningful symbols, computationalism implies semantic nativism. (shrink)

John Searle’s Chinese room argument is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence scientists and philosophers of mind, that “the appropriately programmed computer really is a mind”. Since its publication in 1980, the CRA has evoked an enormous amount of debate about its implications for machine intelligence, the functionalist philosophy of mind, theories of consciousness, etc. Although the general consensus among commentators is that the CRA is flawed, and not withstanding the popularity of the (...) systems reply in some quarters, there is remarkably little agreement on exactly how and why it is flawed. A newcomer to the controversy could be forgiven for thinking that the bewildering collection of diverse replies to Searle betrays a tendency to unprincipled, ad hoc argumentation and, thereby, a weakness in the opposition’s case. In this paper, treating the CRA as a prototypical example of a ‘destructive’ thought experiment, I attempt to set it in a logical framework, which allows us to systematise and classify the various objections. Since thought experiments are always posed in narrative form, formal logic by itself cannot fully capture the controversy. On the contrary, much also hinges on how one translates between the informal everyday language in which the CRA was initially framed and formal logic and, in particular, on the specific conception of possibility that one reads into the logical formalism. (shrink)

Preston, John, and Mark Bishop, eds., Views into the Chinese Room: New Essays on Searle and Artificial Intelligence, Oxford: Clarendon Press, 2002, pp. xvi + 410, US$99.00, US$39.95...

I have argued elsewhere that non-sentential representations that are the close kin of scale models can be, and often are, realized by computational processes. I will attempt here to weaken any resistance to this claim that happens to issue from those who favor an across-the-board computational theory of cognitive activity. I will argue that embracing the idea that certain computers harbor nonsentential models gives proponents of the computational theory of cognition the means to resolve the conspicuous disconnect between the sentential (...) character of the data structures they posit and the nonsentential qualitative character of our perceptual experiences of corporeal (i.e., spatial, kinematic, and dynamic) properties. Along the way, I will question the viability of some externalist remedies for this disconnect, and I will explain why the computational theory put forward here falls quite clearly beyond the useful bounds of the Chinese-Room argument. (shrink)

Most, if not all, philosophers agree that computers cannot learn what words refers to from raw text alone. While many attacked Searle’s Chinese Room thought experiment, no one seemed to question this most basic assumption. For how can computers learn something that is not in the data? Emily Bender and Alexander Koller ( 2020 ) recently presented a related thought experiment—the so-called Octopus thought experiment, which replaces the rule-based interlocutor of Searle’s thought experiment with a neural language model. The Octopus (...) thought experiment was awarded a best paper prize and was widely debated in the AI community. Again, however, even its fiercest opponents accepted the premise that what a word refers to cannot be induced in the absence of direct supervision. I will argue that what a word refers to _is_ probably learnable from raw text alone. Here’s why: higher-order concept co-occurrence statistics are stable across languages and across modalities, because language use (universally) reflects the world we live in (which is relatively stable). Such statistics are sufficient to establish what words refer to. My conjecture is supported by a literature survey, a thought experiment, and an actual experiment. (shrink)

John Searle’s Chinese Room Argument purports to demonstrate that syntax is not sufficient for semantics, and, hence, because computation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal computations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes to blurring the divide (...) between biological and artificial systems; so-called animats, autonomous robots that are controlled by biological neural tissue and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at first sight, these chimeric systems may seem to escape the CRA, on closer analysis, they do not. We conclude by discussing the role of the body–brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science. (shrink)

This paper revisits the conception of intelligence and understanding as embodied in the Turing Test. It argues that a simple system of meaning relations drawn from words/lexical items in a natural language and framed in terms of syntax-free relations in linguistic texts can help ground linguistic inferences in a manner that can be taken to be 'understanding' in a mechanized system. Understanding in this case is a matter of running through the relevant inferences meaning relations allow for, and some of (...) these inferences are plain deductions and some can serve to act as abductions. Understanding in terms of meaning relations also supervenes on linguistic syntax because such understanding cannot be simply reduced to syntactic relations. The current approach to meaning and understanding thus shows that this is one way, if not the only way, of framing Alan Turing's original insight into the nature of thinking in computing systems. (shrink)

Can animals think? In this paper I address the proposal that many animals, including insects such as honeybees, have genuine thoughts. I consider one prominent version of this view that claims that honeybees can represent and process information about their environments in a way that satisfies the main hallmarks of human conceptual thought. I shall argue, however, that this view fails to provide convincing grounds for accepting that animals possess concepts. More precisely, I suggest that two important aspects of conceptual (...) thought, viz., concept individuation and the generality constraint, are not satisfied. (shrink)

Adopting the notion of “pragmatic information” as interpreted by Roederer and granted that understanding arises from genuine information processing, I show that Searle’s “Chinese Room Argument” in rejecting the thesis of Strong Artificial Intelligence and his responses to critics are sound and acceptable. The paper is a safe and secure translation of Searle’s argument into a language of information. According to the notion of information that I adopt, information and information processing are exclusive attributes of biological and artificial systems. However, (...) I will show that information-theoretically, biological brains are fundamentally different from their artificial simulations. I will argue that only biological systems, or their duplicates, are genuine information processing systems and thus capable of understanding Chinese. “Understanding”, in my proposal, denotes “phenomenal consciousness” in the context of Chalmers’ thesis of naturalistic dualism. (shrink)

In addition to his famous Chinese Room argument, John Searle has posed a more radical problem for views on which minds can be understood as programs. Even his wall, he claims, implements the WordStar program according to the standard definition of implementation because there is some ‘‘pattern of molecule movements’’ that is isomorphic to the formal structure of WordStar. Program implementation, Searle charges, is merely observer-relative and thus not an intrinsic feature of the world. I argue, first, that analogous charges (...) involving other concepts (motion and meaning) lead to consequences no one accepts. Second, I show that Searle’s treatment of computation is incoherent, yielding the consequence that nothing computes anything: even our standard personal computers fail to run any programs on this account. I propose an alternative account, one that accords with the way engineers, programmers, and cognitive scientists use the concept of computation in their empirical work. This alternative interpretation provides the basis of a philosophical analysis of program implementation, one that may yet be suitable for a computational theory of the mind. (shrink)

The extended mind hypothesis (EMH) is the claim that the mind can and does extend beyond the human body. Adams and Aizawa (A&A) contend that arguments for EMH commit a ‘coupling constitution fallacy’. We deny that the master argument for EMH commits such a fallacy. But we think that there is an important question lurking behind A&A's allegation: under what conditions is cognition spread across a tightly coupled system? Building on some suggestions from Haugeland, we contend that the system must (...) exhibit a distinctive sort of semantic activity, semantic activity that the system as a whole takes responsibility for. (shrink)

This article is an accompaniment to Anthony Freeman’s review of Views into the Chinese Room, reflecting on some pertinent outstanding questions about the Chinese room argument. Although there is general agreement in the artificial intelligence community that the CRA is somehow wrong, debate continues on exactly why and how it is wrong. Is there a killer counter-argument and, if so, what is it? One remarkable fact is that the CRA is prototypically a thought experiment, yet it has been very little (...) discussed from the perspective of thought experiments in general. Here, I argue that the CRA fails as a thought experiment because it commits the fallacy of undersupposing, i.e., it leaves too many details to be filled in by the audience. Since different commentators will often fill in details differently, leading to different opinions of what constitutes a decisive counter, the result is 21-plus years of inconclusive debate. (shrink)