Artificial Neural Networks have reached “grandmaster” and even “super-human” performance across a variety of games, from those involving perfect information, such as Go, to those involving imperfect information, such as “Starcraft”. Such technological developments from artificial intelligence (AI) labs have ushered concomitant applications across the world of business, where an “AI” brand-tag is quickly becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong—an autonomous vehicle crashes, a chatbot exhibits “racist” behavior, automated credit-scoring processes (...) “discriminate” on gender, etc.—there are often significant financial, legal, and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “... all the impressive achievements of deep learning amount to just curve fitting.” The key, as Pearl suggests, is to replace “reasoning by association” with “causal reasoning” —the ability to infer causes from observed phenomena. It is a point that was echoed by Gary Marcus and Ernest Davis in a recent piece for theNew York Times: “we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets—often using an approach known as ‘Deep Learning’—and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space, and causality.” In this paper, foregrounding what in 1949 Gilbert Ryle termed “a category mistake”, I will offer an alternative explanation for AI errors; it is not so much that AI machinery cannot “grasp” causality, but that AI machinery (qua computation) cannot understand anything at all. (shrink)

The mental realm seems different to the physical realm; the mental is thought to be dependent on, yet distinct from the physical. But how, exactly, are the two realms supposed to be different, and what, exactly, creates the seemingly insurmountable juxtaposition between the mental and the physical? This review identifies and discusses five marks of the mental, features that set characteristically mental phenomena apart from the characteristically physical phenomena. These five marks (intentionality, consciousness, free will, teleology, and normativity) are not (...) presented as a set of features that define mentality. Rather, each of them is something we seem to associate with phenomena we consider mental, and each of them seems to be in tension with the physical view of reality in its own particular way. It is thus suggested how there is no single mind-body problem, but a set of distinct but interconnected problems. Each of these separate problems is analyzed, and their differences, similarities and connections are identified. This provides a useful basis for future theoretical work on psychology and philosophy of mind, that until now has too often suffered from unclarities, inadequacies, and conflations. (shrink)

A computer can come to understand natural language the same way Helen Keller did: by using “syntactic semantics”—a theory of how syntax can suffice for semantics, i.e., how semantics for natural language can be provided by means of computational symbol manipulation. This essay considers real-life approximations of Chinese Rooms, focusing on Helen Keller’s experiences growing up deaf and blind, locked in a sort of Chinese Room yet learning how to communicate with the outside world. Using the SNePS computational knowledge-representation system, (...) the essay analyzes Keller’s belief that learning that “everything has a name” was the key to her success, enabling her to “partition” her mental concepts into mental representations of: words, objects, and the naming relations between them. It next looks at Herbert Terrace’s theory of naming, which is akin to Keller’s, and which only humans are supposed to be capable of. The essay suggests that computers at least, and perhaps non-human primates, are also capable of this kind of naming. (shrink)

The journal of Cognitive Computation is defined in part by the notion that biologically inspired computational accounts are at the heart of cognitive processes in both natural and artificial systems. Many studies of various important aspects of cognition (memory, observational learning, decision making, reward prediction learning, attention control, etc.) have been made by modelling the various experimental results using ever-more sophisticated computer programs. In this manner progressive inroads have been made into gaining a better understanding of the many components of (...) cognition. Concomitantly in both science and science fiction the hope is periodically re-ignited that a manmade system can be engineered to be fully cognitive and conscious purely in virtue of its execution of an appropriate computer program. However, whilst the usefulness of the computational metaphor in many areas of psychology and neuroscience is clear, it has not gone unchallenged and in this article I will review a group of philosophical arguments that suggest either such unequivocal optimism in computationalism is misplaced—computation is neither necessary nor sufficient for cognition—or panpsychism (the belief that the physical universe is fundamentally composed of elements each of which is conscious) is true. I conclude by highlighting an alternative metaphor for cognitive processes based on communication and interaction. (shrink)

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988 ) monograph, “Representation & Reality”, which if correct, has (...) important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set ( x )”. Then, equating Q ( x ) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere. (shrink)

Molyneux’s question, whether the newly sighted might immediately recognize tactilely familiar shapes by sight alone, has produced an array of answers over three centuries of debate and discussion. I propose the first pluralist response: many different answers, both yes and no, are individually sufficient as an answer to the question as a whole. I argue that this is possible if we take the question to be cluster concept of sub-problems. This response opposes traditional answers that isolate specific perceptual features as (...) uniquely applicable to Molyneux’s question and grant viability to only one reply. Answering Molyneux’s question as a cluster concept may also serve as a methodology for resolving other philosophical problems. (shrink)

The representational nature of human cognition and thought in general has been a source of controversies. This is particularly so in the context of studies of unconscious cognition, in which representations tend to be ontologically and structurally segregated with regard to their conscious status. However, it appears evolutionarily and developmentally unwarranted to posit such segregations, as,otherwise, artifact structures and ontologies must be concocted to explain them from the viewpoint of the human cognitive architecture. Here, from a by-and-large Classical cognitivist viewpoint, (...) I show why this segregation is wrong, and elaborate on the need to postulate an ontological and structural continuity between unconscious and conscious representations. Specifically, I hypothesize that this continuity is to be found in the symbolic-based interplay between the syntax and the semantics of thought, and I propose a model of human information processing characterized by the integration of syntactic and semantic representations. (shrink)

This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of artificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understanding of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so (...) we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

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. (shrink)

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)

A series of imitation games involving 3-participant (simultaneous comparison of two hidden entities) and 2-participant (direct interrogation of a hidden entity) were conducted at Bletchley Park on the 100th anniversary of Alan Turing’s birth: 23 June 2012. From the ongoing analysis of over 150 games involving (expert and non-expert, males and females, adults and child) judges, machines and hidden humans (foils for the machines), we present six particular conversations that took place between human judges and a hidden entity that produced (...) unexpected results. From this sample we focus on features of Turing’s machine intelligence test that the mathematician/code breaker did not consider in his examination for machine thinking: the subjective nature of attributing intelligence to another mind. (shrink)

(2005). A Philosophical Critique of Classical Cognitivism in Sport: From Information Processing to Bodily Background Knowledge. Journal of the Philosophy of Sport: Vol. 32, No. 2, pp. 155-183.

In 1950 Alan Turing asked whether machines could think. This question has been vigorously debated since, and its relevance for machine intelligence, or even agency, continues to provoke interdisciplinary debate. In fact, Turing’s next step in his paper is to ask a far more nuanced question about imitation, which, we suggest, assumes a number of connections between intelligence, agency and the possibility of imitation. This paper will offer three key arguments against these assumptions, and in so doing make the following (...) claims: (1) that intelligence is not a guarantee of personhood or agency, (2) that Turing’s claims for imitation neither resolve the issue of intelligence, nor of agency, and that (3) recognition between agents (or recognition of agency) centres on the interpersonal relationship between agents, which in turn centres on the possibility for communication with a shared language. Our paper does not seek to argue either for or against the possibility of machine thought. Instead we seek to question whether any such processing would be recognized (by other agents) as thought or agency, and what this recognition might involve. (shrink)

I want to suggest that the major influence of classical arguments for embodiment like "The Embodied Mind" by Varela, Thomson & Rosch (1991) has been a changing of positions rather than a refutation: Cognitivism has found ways to retreat and regroup at positions that have better fortification, especially when it concerns theses about artificial intelligence or artificial cognitive systems. For example: a) Agent-based cognitivism' that understands humans as taking in representations of the world, doing rule-based processing and then acting on (...) them (sense-plan-act) is often limited to conscious decision processes; and b) Purely syntactic cognition is compatible with embodiment, or supplemented by embodiment (e.g. for 'grounding'). While the empirical thesis of embodied cognition ('embodied cognitive science') is true and the practical engineering thesis ('morphological computation', 'cheap design') is often true, the conceptual thesis ('embodiment is necessary for cognition') is likely false - syntax is often enough for cognition, unless grounding is really necessary. I conclude that it has become more sensible to integrate embodiment with traditional approaches rather than "fight for embodiment" or "against cognitivism". (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)

From the height of his ninety years of experience, Robert G. Shulman is not just a veteran of World War II, but a world-class biophysicist with a distinguished research career spanning the California Institute of Technology, Bell Labs, and Yale University. A forerunner in the use of nuclear magnetic resonance, Shulman contributed to the study of biochemical processes, founded the Yale Magnetic Resonance Research Center, and shepherded functional Magnetic Resonance Imaging (fMRI) as a dominant tool of cognitive neuroscience. Together with (...) Gregory McCarthy et al., Shulman authored several of the earliest fMRI studies, including the first to explicitly manipulate cognitive processes (McCarthy, Blamire, Rothman, Gruetter, & Shulman, 1993; Shulman, Blamire, Rothman, & McCarthy, 1993). As such, his ideas concerning conscious research piqued our curiosity. We read Brain Imaging: What It Can (and Cannot) Tell Us About Consciousness with anticipation.Shulman comes full circle with his book on brain imaging: although his career spanned meaningful collaborations with behavioural scientists and interactions with researchers from preclinical and clinical fields, he retreats to the comfort of the biophysical sciences. Two prominent, but related, hesitations give us pause regarding his retreat. First, he claims that a behaviourist approach to brain imaging is reasonable and pragmatic for the science of consciousness. Second, he rebuffs subjective and phenomenal forms of evidence. In... (shrink)

This essay proposes an alternative way of studying video games: as thought experiments akin to the narrative thought experiments that are frequently used in philosophy. This perspective incorporates insights from the narratological and ludological perspectives in game studies and highlights the philosophical significance of games. Video game thought experiments are similar to narrative thought experiments in many respects and can perform the same functions. They also have distinctive advantages over narrative thought experiments, as they situate counterfactuals in more complex, developed (...) contexts and present them to players who are participants in game worlds, rather than simply observers. (shrink)