Artificial consciousness: Utopia or real possibility?

Since the beginnings of computer technology, researchers have speculated about the possibility of building smart machines that could compete with human intelligence. Given the current pace of advances in artificial intelligence and neural computing, such an evolution seems to be a more concrete possibility. Many people now believe that artificial consciousness is possible and that, in the future, it will emerge in complex computing machines. However, a discussion of artificial consciousness gives rise to several philosophical issues: can computers think or do they just calculate? Is consciousness a human prerogative? Does consciousness depend on the material that comprises the human brain, or can computer hardware replicate consciousness? Answering these questions is difficult because it requires combining information from many disciplines including computer science, neurophysiology, philosophy, and religion. Further, we must consider the influence of science fiction, especially science fiction films, when addressing artificial consciousness. As a product of the human imagination, such works express human desires and fears about future technologies and may influence the course of progress. At a societal level, science fiction simulates future scenarios that can help prepare us for crucial transitions by predicting the consequences of significant technological advances. The paper considers robots in science fiction, the Turing test, computer chess and artificial consciousness     

Effective procedures and computable functions

Horsten and Roelants have raised a number of important questions about my analysis of effective procedures and my evaluation of the Church-Turing thesis. They suggest that, on my account, effective procedures cannot enter the mathematical world because they have a built-in component of causality, and, hence, that my arguments against the Church-Turing thesis miss the mark. Unfortunately, however, their reasoning is based upon a number of misunderstandings. Effective mundane procedures do not, on my view, provide an analysis of ourgeneral concept of an effective procedure; mundane procedures and Turing machine procedures are different kinds of procedure. Moreover, the same sequence ofparticular physical action can realize both a mundane procedure and a Turing machine procedure; it is sequences of particular physical actions, not mundane procedures, which enter the world of mathematics. I conclude by discussing whether genuinely continuous physical processes can enter the world of real numbers and compute real-valued functions. I argue that the same kind of correspondence assumptions that are made between non-numerical structures and the natural numbers, in the case of Turing machines and personal computers, can be made in the case of genuinely continuous, physical processes and the real numbers.     

Is the Church-Turing thesis true?

The Church-Turing thesis makes a bold claim about the theoretical limits to computation. It is based upon independent analyses of the general notion of an effective procedure proposed by Alan Turing and Alonzo Church in the 1930's. As originally construed, the thesis applied only to the number theoretic functions; it amounted to the claim that there were no number theoretic functions which couldn't be computed by a Turing machine but could be computed by means of some other kind of effective procedure. Since that time, however, other interpretations of the thesis have appeared in the literature. In this paper I identify three domains of application which have been claimed for the thesis: (1) the number theoretic functions; (2) all functions; (3) mental and/or physical phenomena. Subsequently, I provide an analysis of our intuitive concept of a procedure which, unlike Turing's, is based upon ordinary, everyday procedures such as recipes, directions and methods; I call them mundane procedures. I argue that mundane procedures can be said to be effective in the same sense in which Turing machine procedures can be said to be effective. I also argue that mundane procedures differ from Turing machine procedures in a fundamental way, viz., the former, but not the latter, generate causal processes. I apply my analysis to all three of the above mentioned interpretations of the Church-Turing thesis, arguing that the thesis is (i) clearly false under interpretation (3), (ii) false in at least some possible worlds (perhaps even in the actual world) under interpretation (2), and (iii) very much open to question under interpretation (1).     

Neural and Super-Turing Computing

``Neural computing' is a research field based on perceiving the human brain as an information system. This system reads its input continuously via the different senses, encodes data into various biophysical variables such as membrane potentials or neural firing rates, stores information using different kinds of memories (e.g., short-term memory, long-term memory, associative memory), performs some operations called ``computation', and outputs onto various channels, including motor control commands, decisions, thoughts, and feelings. We show a natural model of neural computing that gives rise to hyper-computation. Rigorous mathematical analysis is applied, explicating our model's exact computational power and how it changes with the change of parameters. Our analog neural network allows for supra-Turing power while keeping track of computational constraints, and thus embeds a possible answer to the superiority of the biological intelligence within the framework of classical computer science. We further propose it as standard in the field of analog computation, functioning in a role similar to that of the universal Turing machine in digital computation. In particular an analog of the Church-Turing thesis of digital computation is stated where the neural network takes place of the Turing machine.     

Turing Test: 50 Years Later

The Turing Test is one of the most disputed topics in artificial intelligence, philosophy of mind, and cognitive science. This paper is a review of the past 50 years of the Turing Test. Philosophical debates, practical developments and repercussions in related disciplines are all covered. We discuss Turing's ideas in detail and present the important comments that have been made on them. Within this context, behaviorism, consciousness, the `other minds' problem, and similar topics in philosophy of mind are discussed. We also cover the sociological and psychological aspects of the Turing Test. Finally, we look at the current situation and analyze programs that have been developed with the aim of passing the Turing Test. We conclude that the Turing Test has been, and will continue to be, an influential and controversial topic.     

On the Claim that a Table-Lookup Program Could Pass the Turing Test

The claim has often been made that passing the Turing Test would not be sufficient to prove that a computer program was intelligent because a trivial program could do it, namely, the “Humongous-Table (HT) Program”, which simply looks up in a table what to say next. This claim is examined in detail. Three ground rules are argued for: (1) That the HT program must be exhaustive, and not be based on some vaguely imagined set of tricks. (2) That the HT program must not be created by some set of sentient beings enacting responses to all possible inputs. (3) That in the current state of cognitive science it must be an open possibility that a computational model of the human mind will be developed that accounts for at least its nonphenomenological properties. Given ground rule 3, the HT program could simply be an “optimized” version of some computational model of a mind, created via the automatic application of program-transformation rules [thus satisfying ground rule 2]. Therefore, whatever mental states one would be willing to impute to an ordinary computational model of the human psyche one should be willing to grant to the optimized version as well. Hence no one could dismiss out of hand the possibility that the HT program was intelligent. This conclusion is important because the Humongous-Table Program Argument is the only argument ever marshalled against the sufficiency of the Turing Test, if we exclude arguments that cognitive science is simply not possible.     

Turing-, Human- and Physical Computability: An Unasked Question

In recent years it has been convincingly argued that the Church-Turing thesis concerns the bounds of human computability: The thesis was presented and justified as formally delineating the class of functions that can be computed by a human carrying out an algorithm. Thus the Thesis needs to be distinguished from the so-called Physical Church-Turing thesis (or Thesis M), according to which all physically computable functions are Turing computable. The latter is often claimed to be false, or, if true, contingently so. On all accounts, though, thesis M is not easy to give counterexamples to, but it is never asked why—how come that a thesis that transfers a notion from the strictly human domain to the general physical domain just happens to be so difficult to falsify (or even to be true). In this paper I articulate this question and consider several tentative answers to it.

Problems for a Philosophy of Software Engineering

On the basis of an earlier contribution to the philosophy of computer science by Amnon Eden, this essay discusses to what extent Eden’s ‘paradigms’ of computer science can be transferred or applied to software engineering. This discussion implies an analysis of how software engineering and computer science are related to each other. The essay concludes that software engineering can neither be fully subsumed by computer science, nor vice versa. Consequently, also the philosophies of computer science and software engineering—though related to each other—are not identical branches of a general philosophy of science. This also implies that not all of Eden’s earlier arguments can be directly mapped from the domain of computer science into the domain of software science. After the discussion of this main topic, the essay also points to some further problems and open issues for future studies in the philosophy of software science and engineering.     

交互计算模型概述

由于计算机技术的发展日新月异，以算法为核心，以图灵机和Church论题等为理论依据的计算模型已无力继续成为今天计算科学的理论范式，介绍了一个崭新的计算模型－－交互计算模型的基本思想，它是对算法的扩展，并比算法具有更强的描述能力，一系列基本概念被扩展到交互。     

The Turing Test and the legal process

This paper proposes a novel thought-experiment, the ‘Turing litigation game’ – or ‘Turing game’ for short. Specifically, we propose replacing the existing arcane and archaic systems of civil and criminal procedure with a simple and probabilistic litigation game resembling the Turing Test from the world of computer science. The paper is divided into six sections. Section 1 provides a brief introduction. Section 2 provides some background by describing the original Turing Test and explaining how the Turing Test resembles the process of adjudication. Section 3 then describes our proposed Turing litigation game and identifies the conditions for implementing this alternative approach to litigation, while Section 4 introduces the possibility of probabilistic verdicts (as opposed to the traditional system of binary verdicts). Section 5 reviews (and refutes) several philosophical objections against our Turing-game concept. Section 6 concludes.     

The Church-Turing thesis and effective mundane procedures

We critically discuss Cleland's analysis of effective procedures as mundane effective procedures. She argues that Turing machines cannot carry out mundane procedures, since Turing machines are abstract entities and therefore cannot generate the causal processes that are generated by mundane procedures. We argue that if Turing machines cannot enter the physical world, then it is hard to see how Cleland's mundane procedures can enter the world of numbers. Hence her arguments against versions of the Church-Turing thesis for number theoretic functions miss the mark.The first author is a postdoctoral researcher of the Belgian National Fund for Scientific Research. The financial support of this organization is gratefully acknowledged.     

Levels of abstraction,emergentism and artificial life

I diagnose the current debate between epistemological and ontological emergentism as a Kantian antinomy, which has reasonable but irreconcilable thesis and antithesis. Kantian antinomies have recently returned to contemporary philosophy in part through the work of Luciano Floridi, and the method of levels of abstraction. I use a thought experiment concerning a computer simulation to show how to resolve the epistemological/ontological antinomy about emergence. I also use emergentism and simulations in artificial life to illuminate both levels of abstraction and theoretical challenge for building intelligent agents.     

A Dynamic Interaction Between Machine Learning and the Philosophy of Science

The relationship between machine learning and the philosophy of science can be classed as a dynamic interaction: a mutually beneficial connection between two autonomous fields that changes direction over time. I discuss the nature of this interaction and give a case study highlighting interactions between research on Bayesian networks in machine learning and research on causality and probability in the philosophy of science.     

设计艺术的哲学之旅

形而上者为之道,形而下者为之器。设计艺术在学科化的今天,更突出了指导设计的哲学体系的重要作用。我们在此,带着各位读者重走一次设计艺术的哲学之旅。我们会看到,人类设计史上的每一次变革都是设计哲学的转向使然。     

Physical Hypercomputation and the Church–Turing Thesis

We describe a possible physical device that computes a function that cannot be computed by a Turing machine. The device is physical in the sense that it is compatible with General Relativity. We discuss some objections, focusing on those which deny that the device is either a computer or computes a function that is not Turing computable. Finally, we argue that the existence of the device does not refute the Church–Turing thesis, but nevertheless may be a counterexample to Gandy's thesis.     

Computing as a Science: A Survey of Competing Viewpoints

Since the birth of computing as an academic discipline, the disciplinary identity of computing has been debated fiercely. The most heated question has concerned the scientific status of computing. Some consider computing to be a natural science and some consider it to be an experimental science. Others argue that computing is bad science, whereas some say that computing is not a science at all. This survey article presents viewpoints for and against computing as a science. Those viewpoints are analyzed against basic positions in the philosophy of science. The article aims at giving the reader an overview, background, and a historical and theoretical frame of reference for understanding and interpreting some central questions in the debates about the disciplinary identity of computer science. The article argues that much of the discussion about the scientific nature of computing is misguided due to a deep conceptual uncertainty about science in general as well as computing in particular.     

The Informational Turn in Philosophy1

This paper traces the application of information theory to philosophical problems of mind and meaning from the earliest days of the creation of the mathematical theory of communication. The use of information theory to understand purposive behavior, learning, pattern recognition, and more marked the beginning of the naturalization of mind and meaning. From the inception of information theory, Wiener, Turing, and others began trying to show how to make a mind from informational and computational materials. Over the last 50 years, many philosophers saw different aspects of the naturalization of the mind, though few saw at once all of the pieces of the puzzle that we now know. Starting with Norbert Wiener himself, philosophers and information theorists used concepts from information theory to understand cognition. This paper provides a window on the historical sequence of contributions made to the overall project of naturalizing the mind by philosophers from Shannon, Wiener, and MacKay, to Dennett, Sayre, Dretske, Fodor, and Perry, among others. At some time between 1928 and 1948, American engineers and mathematicians began to talk about `Theory of Information' and `Information Theory,' understanding by these terms approximately and vaguely a theory for which Hartley's `amount of information' is a basic concept. I have been unable to find out when and by whom these names were first used. Hartley himself does not use them nor does he employ the term `Theory of Transmission of Information,' from which the two other shorter terms presumably were derived. It seems that Norbert Wiener and Claude Shannon were using them in the Mid-Forties.(Yehoshua Bar-Hillel, 1955)     

Introduction: Machine Learning as Philosophy of Science

I consider three aspects in which machine learning and philosophy of science can illuminate each other: methodology, inductive simplicity and theoretical terms. I examine the relations between the two subjects and conclude by claiming these relations to be very close.     

Some Philosophical Issues in Computer Science

The essays included in the special issue dedicated to the philosophy of computer science examine new philosophical questions that arise from reflection upon conceptual issues in computer science and the insights such an enquiry provides into ongoing philosophical debates.     

Other bodies,other minds: A machine incarnation of an old philosophical problem

Any attempt to explain the mind by building machines with minds must confront the other-minds problem: How can we tell whether any body other than our own has a mind when the only way to know is by being the other body? In practice we all use some form of Turing Test: If it can do everything a body with a mind can do such that we can't tell them apart, we have no basis for doubting it has a mind. But what is “everything” a body with a mind can do? Turing's original “pen-pal” version of the Turing Test (the TT) only tested linguistic capacity, but Searle has shown that a mindless symbol-manipulator could pass the TT undetected. The Total Turing Test (TTT) calls instead for all of our linguistic and robotic capacities; immune to Searle's argument, it suggests how to ground a symbol manipulating system in the capacity to pick out the objects its symbols refer to. No Turing Test, however, can guarantee that a body has a mind. Worse, nothing in the explanation of its successful performance requires a model to have a mind at all. Minds are hence very different from the unobservables of physics (e.g., superstrings); and Turing Testing, though essential for machine-modeling the mind, can really only yield an explanation of the body.