John Mark Bishop

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.

This book analyzes the philosophical foundations of sensorimotor theory and discusses the most recent applications of sensorimotor theory to human computer interaction, child's play, virtual reality, robotics, and linguistics. Why does a circle look curved and not angular? Why doesn't red sound like a bell? Why, as I interact with the world, is there something it is like to be me? These are simple questions to pose but more difficult to answer. An analytic philosopher might respond to the first by merely observing, ``if we ponder the concept `circle' we find that it is the essence of a circle to be round''; but where does this definition come from? Was it set in stone by the gods, the divine arbiters of circle-ness (red-ness and conscious-ness)? It is a measure of the importance of Kevin O'Regan and Alva Noe's 2001 paper `A sensorimotor account of vision and visual consciousness' that it reveals deep scientific and philosophical insight on these foundational issues of perception. Opening with a chapter from Kevin O'Regan, this collection of new essays continues by presenting fifteen additional essays on as many developments achieved in recent years in this field. It provides readers with a critical review of the sensorimotor theory and in so doing introduces them to a radically new approach in cognitive science.

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.

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

A new book by Zenon Pylyshyn is always a cause for celebration among philosophers of psychology. While many hard-nosed experimental cognitive scientists are attentive to philosophers’ concerns, Pylyshyn stands alone in the extraordinary efforts he takes to understand, address, and struggle with the philosophical puzzles that the mind, and perception in particular, raises. Pylyshyn’s most recent work, Things and Places: How the Mind Connects with the World, does not disappoint. It is philosophically rich. Indeed, the approach to object perception that Pylyshyn develops in this book takes inspiration from Evans’s (1982) and Perry’s (1979) work on demonstratives and indexicals, draws on Dretskean (1981, 1986, 1988) ideas about representation, and tangles with Strawson (1959), Quine (1992), and Clark (2000, 2004) over how to understand the role of concepts in perception. In short, it is just the kind of book philosophers of psychology should lavishly slather with clotted cream and joyously devour at their next tea party. The main focus of this review will be Pylyshyn’s theory of FINSTs (an acronym for Fingers of INSTantion, for reasons to be soon clarified). FINSTs are the primary subject of the first three chapters of Things and Places, after which they basically disappear for about eighty pages, to reappear in the final and lengthiest fifth chapter, where they are put to use in a speculative (and, to my mind, slightly incredible) explanation of data from mental imagery experiments. The fourth chapter is an engaging polemic against using subjective experience as a source of evidence about psychological processing and, in particular, the danger in assuming that because mental images appear to have spatial properties, they must be represented spatially. This chapter stands alone and would be of interest to followers of the imagery debate or, for that matter, to instructors looking for counter-examples when..

In the fourteenth chapter of The Philosophy of Information, Luciano Floridi puts forth a criticism of ‘digital ontology’ as a step toward the articulation of an ‘informational structural realism’. Based on the claims made in the chapter, the present paper seeks to evaluate the distinctly Kantian scope of the chapter from a rather unconventional viewpoint: while in sympathy with the author’s doubts ‘against’ digital philosophy, we follow a different route. We turn our attention to the concept of construction as used in the book with the hope of raising some additional questions that might contribute to a better understanding of what is at stake in Floridi’s experimental epistemological response to digital ontology

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.

The initial argument presented herein is not significantly original—it is a simple reflection upon a notion of computation originally developed by Putnam and criticised by Chalmers et al. . In what follows, instead of seeking to justify Putnam’s conclusion that every open system implements every Finite State Automaton and hence that psychological states of the brain cannot be functional states of a computer, I will establish the weaker result that, over a finite time window every open system implements the trace of FSA Q, as it executes program on input . If correct the resulting bold philosophical claim is that phenomenal states—such as feelings and visual experiences—can never be understood or explained functionally

Viewed in the light of the remarkable performance of ‘Watson’ - IBMs proprietary artificial intelligence computer system capable of answering questions posed in natural language - on the US general knowledge quiz show ‘Jeopardy’, we review two experiments on formal systems - one in the domain of quantum physics, the other involving a pictographic languaging game - whereby behaviour seemingly characteristic of domain understanding is generated by the mere mechanical application of simple rules. By re-examining both experiments in the context of Searle’s Chinese Room Argument, we suggest their results merely endorse Searle’s core intuition: that ‘syntactical manipulation of symbols is not sufficient for semantics’. Although, pace Watson, some artificial intelligence practitioners have suggested that more complex, higher-level operations on formal symbols are required to instantiate understanding in computational systems, we show that even high-level calls to Google translate would not enable a computer qua ‘formal symbol processor’ to understand the language it processes. We thus conclude that even the most recent developments in ‘quantum linguistics’ will not enable computational systems to genuinely understand natural language.

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.

This issue of the Kybernetes journal is concerned with the philosophical question- Can a Machine Think? Famously, in his 1950 paper `Computing Machinery andIntelligence' [9], the British mathematician Alan Turing suggested replacing this question - which he found \too meaningless to deserve discussion" - with a simple -behavioural - test based on an imagined `Victorianesque' pastime he entitled the`imitation game'. In this special issue of Kybernetes a selection of authors with a special interest in Turing's work (including those who participated in the 2008 AISB Turing Symposium) have been invited to explore and clarify issues arising from Turing's 1950 paper on the Imitation Game; now more widely known as the Turing test.

The argument presented in this paper is not a direct attack or defence of the Chinese Room Argument (CRA), but relates to the premise at its heart, that syntax is not sufficient for semantics, via the closely associated propositions that semantics is not intrinsic to syntax and that syntax is not intrinsic to physics. However, in contrast to the CRA’s critique of the link between syntax and semantics, this paper will explore the associated link between syntax and physics. The main argument presented here is not significantly original – it is a simple reflection upon that originally given by Hilary Putnam (Putnam 1988) and criticised by David Chalmers and others: instead of seeking to justify Putnam’s claim that, “every open system implements every Finite State Automaton (FSA)”, and hence that psychological states of the brain cannot be functional states of a computer, I will seek to establish the weaker result that, over a finite time window every open system implements the trace of a particular FSA Q, as it executes program (p) on input (x). That this result leads to panpsychism is clear as, equating Q (p, x) to a specific Strong AI program that is claimed to instantiate phenomenal states as it executes, and following Putnam’s procedure, identical computational (and ex hypothesi phenomenal) states (ubiquitous little ‘pixies’) can be found in every open physical system.

The Chinese Room Argument purports to show that‘ syntax is not sufficient for semantics’; an argument which led John Searle to conclude that ‘programs are not minds’ and hence that no computational device can ever exhibit true understanding. Yet, although this controversial argument has received a series of criticisms, it has withstood all attempts at decisive rebuttal so far. One of the classical responses to CRA has been based on equipping a purely computational device with a physical robot body. This response, although partially addressed in one of Searle’s original contra arguments - the ‘robot reply’ - more recently gained friction with the development of embodiment and enactivism1, two novel approaches to cognitive science that have been exciting roboticists and philosophers alike. Furthermore, recent technological advances - blending biological beings with computational systems - have started to be developed which superficially suggest that mind may be instantiated in computing devices after all. This paper will argue that (a) embodiment alone does not provide any leverage for cognitive robotics wrt the CRA, when based on a weak form of embodiment and that (b) unless they take the body into account seriously, hybrid bio-computer devices will also share the fate of their disembodied or robotic predecessors in failing to escape from Searle’s Chinese room.

In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between ‘Achilles’ and an anthill (the eponymous ‘Aunt Hillary’), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter’s work is notably short on detail suggesting how this magic might be achieved1. Conversely in this paper - finally reifying Hofstadter’s imagination - we demonstrate how populations of simple ant-like creatures can be organised to solve complex problems; problems that involve the use of forward planning and strategy. Specifically we will demonstrate that populations of such creatures can be configured to play a strategically strong - though tactically weak - game of HeX (a complex strategic game).We subsequently demonstrate how tactical play can be improved by introducing a form of forward planning instantiated via multiple populations of agents; a technique that can be compared to the dynamics of interacting populations of social insects via the concept of meta-population. In this way although, pace Hofstadter, we do not establish that a meta-population of ants could actually hold a conversation with Achilles, we do successfully introduce Aunt Hillary to the complex, seductive charms of HeX.