Computationalism

This article focuses on the interaction between man and machine, AI specifically, to analyse how these systems are slowly taking over roles that hitherto were thought ‘only’ for humans. More recent, as AI has stepped up in ability to learn without supervision, to recognize patterns, and to solve problems, it adopted characteristics like creativity, novelty, intentionality. These events take one to the heart of what it is to be human, and the emerging definitions of self that are increasingly central to (...) post humanist discourses. The discussion in these two threads is in philosophy of AI and is concerned with issues of consciousness, intentionality and creativity. Al as a result of causing a shift in the current anthropocentric perceptions resulted in portrayal of humans as special beings. Secondly, this exploration responds to important questions related to AI application, such as ethical, social, and existential ones. The article emphasizes a necessity to define the role of the advent of AI and its influence on the interaction between people and technology as well as the role of the social individuality in the wake of intelligent machines that mimic thinking and creativity. It seeks to prompt more specific analysis of how or why AI reduces the differences between artificial and human intelligence or increases the prospects for options expanding the notion of consciousness beyond the human-centric one. (shrink)

This article draws on Bernard Stiegler and Gilbert Simondon’s work to further interrogate the psychic, social, and political problems raised by the development of Artificial Intelligence. Stiegler’s political philosophy of time-consciousness reveals three concomitants urgencies: human memory is conditioned by industrial supplements that are increasingly disruptive, capitalism has produced an entropic condition where life on earth is threaten by toxic systems, the deployment of technologies of spirits has striped individuals of their psychic and collective individuation. I read media theory along (...) with Simondon’s philosophy to elucidate the question of mediation in today’s reticulated AI-driven society. (shrink)

Sideloading is the creation of a digital model of a person during their life via iterative improvements of this model based on the person's feedback. The progress of LLMs with large prompts allows the creation of very large, book-size prompts which describe a personality. We will call mind-models created via sideloading "sideloads"; they often look like chatbots, but they are more than that as they have other output channels, like internal thought streams and descriptions of actions. -/- By arranging the (...) predictive power of the facts about a person, we can write down the most important facts first. This allows us to get a high level of fidelity in a person's model by writing down a finite number of facts and running it as an LLM prompt with instructions to create a person's chatbot. This instruction also includes an extensive general sideload prompt (we will call it a prompt-loader), which explains how the person's sideload should work, but it is universal and works as an operating system for different persons. -/- I created my personal sideload to test all this and it started working surprisingly quickly. We found that there are three main levels of facts about a person: core facts, long-term memory, and historical facts. Core facts should go to the main prompt of the LLM, long-term memory belongs to RAG, and historical facts – which a person often does not remember – should be only used to extract new relevant data. The most important thing in preparing data for sideloading is to get a full list of the core facts, and not to mix it with long-term memory and historical facts. The LLM-sideload project is inherently future-oriented. New state-of-the-art LLMs with large prompt capabilities emerge quarterly. This allows for the preparation of prompts that exceed current LLM capabilities, anticipating smooth execution in the following year. Below we present the key findings of this work in the form of an executive summary: Quality of sideloads is evaluated using three metrics: • Facts: Accuracy of the answers about the person's life • Vibe: How well the sideload captures the person's style and personality, as judged by the person themselves and their acquaintances • Brilliant insights: Ability to contribute unique, valuable ideas beyond the training data, while maintaining the author's style • Coarseness: Levels of details as percent of the total of recordable textual information from memory estimated to be 10000 pages. Similar to the jpeg level of compression. Estimated performance of Alexey Turchin's sideload: • Facts: 70% correct • Vibe: 20% accurate • Brilliant insights: near zero • Coarseness: 10% The project achieved these results in about one month of work. Vibe is harder to evaluate and regulate than facts. This approach focuses on informational personal identity, setting aside issues of observer identity and internal qualia. (shrink)

Can AI systems such as ChatGPT think? We present an argument from rationality for the negative answer to this question. The argument is founded on two central ideas. The first is that if ChatGPT thinks, it is not rational, in the sense that it does not respond correctly to its evidence. The second idea, which appears in several different forms in philosophical literature, is that thinkers are by their nature rational. Putting the two ideas together yields the result that ChatGPT (...) is not a thinker. The paper sets out the argument in detail and shows how it can withstand numerous objections. We end the paper by contrasting the argument from rationality with a more well-known argument often taken to show that ChatGPT doesn’t think, due to Bender and Koller. (shrink)

Computational complexity has often been ignored in the philosophy of mind, in philosophical artificial intelligence studies. The purpose of this paper is threefold. First and foremost, to show the importance of complexity rather than computability in philosophical and AI problems. Second, to rephrase the notion of computability in terms of solvability, i.e., treating computability as non-sufficient for establishing intelligence. The Church-Turing thesis is therefore revisited and rephrased in order to capture the ontological background of spatial and temporal complexity. Third, to (...) emphasize ontological differences between different time complexities, which seem to provide a solid base for a better understanding of artificial intelligence in general. (shrink)

There are two main approaches within classical cognitive science to explaining how humans can entertain mental states that integrate contents across domains. The language-based framework states that this ability arises from higher cognitive domain-specific systems that combine their outputs through the language faculty, whereas the language-independent framework holds that it comes from non-language-involving connections between such systems. This article turns on its head the most influential empirical argument for the language-based framework, an argument that originates from research on spatial reorientation. (...) I make the case that neuroscientific findings about spatial reorientation in rodents and humans bolster the language-independent framework instead. (shrink)

Recent years have seen a surge of interest in applying mechanistic thinking to computational accounts of implementation and individuation. One recent extension of this work involves so-called ‘wide’ approaches to computation, the view that computational processes spread out beyond the boundaries of the individual. These ‘mechanistic accounts of wide computation’ maintain that computational processes are wide in virtue of being part of mechanisms that extend beyond the boundary of the individual. This paper aims to further develop the mechanistic account of (...) wide computationalism by responding to two outstanding worries, what are called the parsimony and testability challenges. The first is based on considerations about wide computation’s ontological cost; the second the view’s experimental testability. The argument is that wide mechanistic computationalism can gain the necessary conceptual resources to address the challenges by embracing two further aspects of the mechanistic approach: i) the structural aspect of mechanistic explanation and ii) the notion of constitutive relevance. (shrink)

Even if political theorists rarely read him, Italian political thinker, Marsilius of Padua, presents one of the most radical theories of the multitude prior to Machiavelli and Spinoza. This article reconstructs Marsilius of Padua's political theory of the multitude in his Defender of Peace and pays special attention to two main sources from which Marsilius frames his theory: Aristotle and Ibn Rushd. Compared to Aristotle, Marsilius advances a more epistemic view of the multitude as a lawmaker. Marsilius’ ideas on the (...) multitude also depend on Ibn Rushd's theory of collective knowledge and, to a certain extent, on his position on natural law. (shrink)

This book surveys and examines the most famous philosophical arguments against building a machine with human-level intelligence. From claims and counter-claims about the ability to implement consciousness, rationality, and meaning, to arguments about cognitive architecture, the book presents a vivid history of the clash between the philosophy and AI. Tellingly, the AI Wars are mostly quiet now. Explaining this crucial fact opens new paths to understanding the current resurgence AI (especially, deep learning AI and robotics), what happens when philosophy meets (...) science, and the role of philosophy in the culture in which it is embedded. -/- Organising the arguments into four core topics - 'Is AI possible', 'Architectures of the Mind', 'Mental Semantics and Mental Symbols' and 'Rationality and Creativity' - this book shows the debate that played out between the philosophers on both sides of the question, and, as well, the debate between philosophers and AI scientists and engineers building AI systems. Up-to-date and forward-looking, the book is packed with fresh insights and supporting material, including: -/- - Accessible introductions to each war, explaining the background behind the main arguments against AI - Each chapter details what happened in the AI wars, the legacy of the attacks, and what new controversies are on the horizon. - Extensive bibliography of key readings. (shrink)

Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...) in each context, has been adopted as a guide. By using it, we’ve been able to describe a fundamental tension between context sensitivity and the mental representations used in cognition theories. The first chapter discusses the nature of the frame problem,as well as the reasons for its persistence. In the second and third chapters, the problem is used as a measure tool in order to inquiry a few representational approaches and check how well suited they are to deal with context dependencies. The problems found are then correlated with the frame problem. Throughout the discussion, we try to show that 1) none of the evaluated approaches is capable of dealing with context sensitivity in a proper manner, but 2) that’s not a reason to think that the frame problem constitutes an argument against representational approaches in general, and 3) that it constitutes a fundamental conceptual tool in contemporary research -/- A sensibilidade ao contexto é uma das marcas distintivas da inteligência humana. Compreender o modo flexível como o ser humano pensa e age em função de um número potencialmente infinito de circunstâncias, ainda que munido de recursos finitos e limitados, é um desafio central para a filosofia da mente e para a ciência cognitiva, em particular aos que fazem uso de teorias representacionalistas. Nesse trabalho, adotou-se como fio condutor o modo como isso se manifesta no "frame problem": a dificuldade em explicar como a cognição humana reconhece, de maneira eficiente, o que é ou não relevante em cada contexto. A partir dele, buscou-se caracterizar uma tensão fundamental entre a sensibilidade ao contexto e o uso de representações mentais em teorias da cognição. O primeiro capítulo discute a natureza do frame problem, bem como as razões de sua resiliência. No segundo e terceiro capítulos, faz-se uso do problema como métrica para investigar o quão adequado é o tratamento das dependências contextuais no âmbito de várias abordagens representacionais. No decorrer da discussão, realiza-se um esforço argumentativo para mostrar que 1) nenhuma das estratégias abordadas é capaz tratar adequadamente da sensibilidade ao contexto, mas que 2) apesar disso, o frame problem não constitui argumento fatal para teorias representacionalistas em geral, e que 3) ele constitui uma ferramenta conceitual fundamental para pesquisas contemporâneas. (shrink)

The article presents preliminary results of the conceptual analysis of the mechanistic profile of the computer metaphor. Mechanic reductionism is a special direction of computer metaphor rooted in various historical forms of word usage. Here we trace the stages of formation of the principles of transferring the properties of a mechanical computer to the properties of the human body and mind. We are also trying to identify the basic principles of semantic transfer, which have survived to this day in the (...) discourse of modern computationalism. The reasons are analyzed due to which the metaphor «Human (body) is machine», traditional for the Modern Age, was transformed into a more complex version of «Mind is machine». What happened to the concepts of «mind» and «machine»? How have ideas about the properties of computational procedures changed? What keeps the counterintuitive computer metaphor viable today? The answers to this series of questions and theoretical ways of solving these problems are contained in this paper. (shrink)

In this article, we propose an initial formal model of computationalism based on mathematical relations between cognition and computation. More specifically, based on a set of cognitive constituents as a domain, and a set of computational implementations as a range, we define two relations of transformation over these sets. Moreover, we define the principles of implementability, describability, and phenomena correspondence, and we conjecture that full computationalism does not hold since these principles are not fulfilled. Particularly, many cognitively-tied phenomena fail to (...) respect the describability principle which is necessary for representing a cognitive state by a computational state. (shrink)

La inteligencia artificial (IA) es la capacidad de una máquina o sistema informático para simular y realizar tareas que normalmente requerirían inteligencia humana, como el razonamiento lógico, el aprendizaje y la resolución de problemas. La inteligencia artificial se basa en el uso de algoritmos y tecnologías de aprendizaje automático para dar a las máquinas la capacidad de aplicar ciertas habilidades cognitivas y realizar tareas por sí mismas de manera autónoma o semiautónoma. La inteligencia artificial se distingue por su grado de (...) capacidad cognitiva o por su grado de autonomía. Por capacidad puede ser débil o limitada, general o superlativa. Por su autonomía, puede ser reactiva, deliberativa, cognitiva o totalmente autónoma. A medida que la inteligencia artificial mejora, muchos procesos son cada vez más eficientes y, tareas que hoy parecen complicadas se estarán realizando con mayor rapidez y precisión. (shrink)

This is a review of Mind Design II: Philosophy, Psychology, and Artificial Intelligence, edited by John Haugeland and published by The MIT Press in 1997.

This essay examines the possibility that phenomenological laws might be implemented by a computational mechanism by carefully analyzing key passages from the Prolegomena to Pure Logic. Part I examines the famous Denkmaschine passage as evidence for the view that intuitions of evidence are causally produced by computational means. Part II connects the less famous criticism of Avenarius & Mach on thought-economy with Husserl's 1891 essay 'On the Logic of Signs (Semiotic).' Husserl is shown to reaffirm his earlier opposition to associationist (...) (Humean) and adaptationist (Darwinian) explanations of our thought-machine on the ground that they cannot reconstruct the notion of truth and its syntactic mental preservation in symbolic thought-trains. Part III reveals Husserl's interesting commitment to the idea that descriptive sciences necessarily transform into explanatory sciences on pain of contradicting the essence of science as a rational ideal. Since explanation in relation to phenomenology means causal explanation, and causal explanation in the form of the Denkmaschine accounts for phenomenological intuition, it is inferred that the rationally compelling ideal of explanatory science requires a computationalist reading of the subsequent logical investigations. (shrink)

With its use of the powerful technology of computer, the computational theory of mind or computationalism, which regards minds as computational systems, has been widely hailed as the most promising theory that will carry out the project of explaining the workings of the mind in purely scientific terms. While it continues to serve as the primary framework for scientifically inclined theorizing and investigations about the nature of minds, especially in the area of cognitive science, it, however, continues to face strong (...) objections from its critics. And with the growing complexity and sophistication of the arguments used to promote and reject the theory, the debate has become intractable. It has become quite difficult to assess which side of the dispute is gaining the upper hand. Such difficulty may be due to a variety of reasons. In this essay, I critically examine two of such reasons. The first concerns the ambiguity of the theory’s intended scope of application: whether it is limited to the mind’s cognitive features only or it also includes the mind’s phenomenal features. The second concerns the vagueness of how the so-called computer modelling of human cognitive processes is able to duplicate such processes. Accordingly, if insufficiently addressed, they remain as two roadblocks to the entire project of computationalism. (shrink)

Arguments for extended cognition and the extended mind are typically directed at human-centred forms of cognitive extension—forms of cognitive extension in which the cognitive/mental states/processes of a given human individual are subject to a form of extended or wide realization. The same is true of debates and discussions pertaining to the possibility of Web-extended minds and Internet-based forms of cognitive extension. In this case, the focus of attention concerns the extent to which the informational and technological elements of the online (...) environment form part of the machinery of the (individual) human mind. In this paper, we direct attention to a somewhat different form of cognitive extension. In particular, we suggest that the Web allows human individuals to be incorporated into the computational/cognitive routines of online systems. These forms of computational/cognitive extension highlight the potential of the Web and Internet to support bidirectional forms of computational/cognitive incorporation. The analysis of such bidirectional forms of incorporation broadens the scope of philosophical debates in this area, with potentially important implications for our understanding of the foundational notions of extended cognition and the extended mind. (shrink)

Recently, information retrieval is shown to be a science by mapping information retrieval scientific study to scientific study abstracted from physics. The exercise was rather tedious and lengthy. Instead of dealing with the nitty gritty, this paper looks at the insights into how computer science can be made into a science by using that methodology. That is by mapping computer science scientific study to the scientific study abstracted from physics. To show the mapping between computer science and physics, we need (...) to define what is engineering science which computer science belongs to. Some principles and assumptions of engineering science theory are presented. To show computer science is a science, we presented two approaches. Approach 1 considers computer science as simulation of human behaviour similar to the goal of artificial intelligence. Approach 2 is closely related to the actual activities in computer science, and this approach considers computer science based on the theory of computation. Finally, we answer some of the common outstanding issues about computer science to convince our reader that computer science is a science. (shrink)

According to a tradition that we hold variously today, the relational person lives most personally in affective and cognitive empathy, whereby we enter subjective communion with another person. Near future social AIs, including social robots, will give us this experience without possessing any subjectivity of their own. They will also be consumer products, designed to be subservient instruments of their users’ satisfaction. This would seem inevitable. Yet we cannot live as personal when caught between instrumentalizing apparent persons (slaveholding) or numbly (...) dismissing the apparent personalities of our instruments (mild sociopathy). This paper analyzes and proposes a step toward ameliorating this dilemma by way of the thought of a 5th century North African philosopher and theologian, Augustine of Hippo, who is among those essential in giving us our understanding of relational persons. Augustine’s semiotics, deeply intertwined with our affective life, suggest that, if we are to own persuasive social robots humanely, we must join our instinctive experience of empathy for them to an empathic acknowledgment of the real unknown relational persons whose emails, text messages, books, and bodily movements will have provided the training data for the behavior of near-future social AIs. So doing, we may see simulation as simulation (albeit persuasive), while expanding our empathy to include those whose refracted behavioral moments are the seedbed of this simulation. If we naïvely stop at the social robot as the ultimate object of our cognitive and affective empathy, we will suborn the sign to ourselves, undermining rather than sustaining a culture that prizes empathy and abhors the instrumentalization of persons. (shrink)

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...) boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain perceptual figure-ground grouping results. The FACADE and 3D LAMINART models are used to explain these data. Keywords: figure-ground separation, border ownership, perceptual grouping, surface filling-in, V2, V4, FACADE Theory. (shrink)

Искусственный интеллект – последняя, хотя и иллюзорная надежда продажных и провалившихся режимов как на Западе, так и на Востоке остаться на плаву: ведь тонущий хватается и за соломинку. Но всё течёт и всё изменяется, и никаким деспотиям и деспотам не удастся остановить ход истории, как бы они этого не желали и тому не противились. Хотя у истории нет конца, но их история и история совершённых ими предательств уже закончилась. Plaudite, cives, plaudite, amici, finita est comoedia: „Рукоплещите, граждане, друзья, комедия окончена.“.

Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate to link together (...) operations of various levels and functionalities, more specific or more general. The challenges are not met by an operating formal system which should have from the outset all the learning skills, but -for instance in simulation- by the dynamics of a succession of solutions open to adjustments as well as to reflexive repeats. (shrink)

This is an introduction to the 80 articles on philosophy in MITECS.

The areas of philosophy that contribute to the cognitive sciences are various, including the philosophy of mind, language, and science. This introduction to the 80 or so philosophy articles in MITECS provides an overview of that contribution.

Androides são "autômatos com forma humana" . Enquanto robôs são "aparelhos automáticos capazes de manipular objetos ou executar operações segundo um programa". Assim podemos dizer que um androide pode ser considerado um robô, mas nem todo robô é um androide. Devido à diversidade de gêneros, foi criado o termo ginóide, separando-se assim os androides de aparência masculina (andros) da feminina (ginos). A propagação destes se deu à ficção cientifica, em livros de Isaac Asimov, em seriados para televisão como Jornada nas (...) Estrelas, em filmes para o cinema, como Guerra nas Estrelas e A.I.: Inteligência Artificial, e diversos outros meios de cultura e entretenimento, teatro inclusive. As obras citadas possuem androides que sentem; que percebem o mundo, mesmo à sua maneira, e com isso eles interagem com o ambiente, e, logicamente, com os objetos à sua volta. Para este ensaio, o principal "androide de estudo" será Data, de Jornada nas Estrelas, A Nova Geração 3 4 , e também alguns filmes posteriores. Assim, este pequeno ensaio tem como objetivo verificar se, a percepção, a extensão sensorial e porque não, a relação entre Corpo e Espírito de Bergson, podem ser aplicados a robôs e androides da Ficção, e talvez os da não ficção. Para tanto, precisaremos percorre por todos os sentidos, pela memória, e talvez até pelos sonhos, pois como já perguntava Phillip K Dick em seu livro que inspirou Blade Runner: "Androides Sonham com Ovelhas Elétricas?". (shrink)

Computational approaches dominate contemporary cognitive science, promising a unified, scientific explanation of how the mind works. However, computational approaches raise major philosophical and scientific questions. In what sense is the mind computational? How do computational approaches explain perception, learning, and decision making? What kinds of challenges should computational approaches overcome to advance our understanding of mind, brain, and behaviour? The Routledge Handbook of the Computational Mind is an outstanding overview and exploration of these issues and the first philosophical collection of (...) its kind. Comprising thirty-five chapters by an international team of contributors from different disciplines, the Handbook is organised into four parts: History and future prospects of computational approaches Types of computational approach Foundations and challenges of computational approaches Applications to specific parts of psychology. Essential reading for students and researchers in philosophy of mind, philosophy of psychology, and philosophy of science, The Routledge Handbook of the Computational Mind will also be of interest to those studying computational models in related subjects such as psychology, neuroscience, and computer science. (shrink)

This is review of the book "Physical Computation and Cognitive Science" by Nir Fresco: http://www.springer.com/la/book/9783642413742.

We consider processes of emergence within the conceptual framework of the Information Loss principle and the concepts of systems conserving information; systems compressing information; and systems amplifying information. We deal with the supposed incompatibility between emergence and computability tout-court. We distinguish between computational emergence, when computation acquires properties, and emergent computation, when computation emerges as a property. The focus is on emergence processes occurring within computational processes. Violations of Turing-computability such as non-explicitness and incompleteness are intended to represent partially the (...) properties of phenomenological emergence, such as logical openness, given by the observer’s cognitive role; structural dynamics where change regards rules rather than only values; and multi-modelling where multiple non-equivalent models are required to model such structural dynamics. In this way, we validate, from an epistemological viewpoint, models and simulations of phenomenological emergence where the sequence of events constitutes the natural, analogical non-Turing computation which a cognitive complex system can reproduce through learning. Reproducibility through learning is different from Turing-like computational iteration. This paper aims to open a new, non-reductionist understanding of the conceptual relationship between emergence and computability. (shrink)

‘The art of living with ICTs ’ today not only means finding new ways to cope, interact and create new lifestyles on the basis of the new digital technologies individually, as ‘consumer-citizens’. It also means inventing new modes of living, producing and, not in the least place, struggling collectively, as workers and producers. As the so-called digital revolution unfolds in the context of a neoliberal cognitive and consumerist capitalism, its ‘innovations’ are predominantly employed to modulate and control both production processes (...) and consumer behavior in view of the overall goal of extracting surplus value. Today, the digital networks overwhelmingly destroy social autonomy, instead engendering increasing social heteronomy and proletarianization. Yet it is these very networks themselves, as technical pharmaka in the sense of French ‘technophilosopher’ Bernard Stiegler, that can be employed as no other to struggle against this tendency. This paper briefly explores this possibility by reflecting upon current diagnoses of our ‘technological situation’ by some exemplary post-operaist Marxists from a Stieglerian, pharmacological perspective. (shrink)

A version of the Church-Turing Thesis states that every effectively realizable physical system can be simulated by Turing Machines (‘Thesis P’). In this formulation the Thesis appears to be an empirical hypothesis, subject to physical falsification. We review the main approaches to computation beyond Turing definability (‘hypercomputation’): supertask, non-well-founded, analog, quantum, and retrocausal computation. The conclusions are that these models reduce to supertasks, i.e. infinite computation, and that even supertasks are no solution for recursive incomputability. This yields that the realization (...) of hypercomputing devices is implausible, and that Thesis P is not essentially different from the standard Church-Turing Thesis. (shrink)

According to Church’s thesis, we can identify the intuitive concept of effective computability with such well-defined mathematical concepts as Turing computability and partial recursiveness. The almost universal acceptance of Church’s thesis among logicians and computer scientists is puzzling from some epistemological perspectives, since no formal proof is possible of a thesis that involves an informal concept such as effectiveness. Elliott Mendelson has recently argued, however, that equivalencies between intuitive notions and precise notions need not always be considered unprovable theses, and (...) that Church’s thesis should be accepted as true.I want to discuss a thesis that is nearly as important in current research in computer science as Church’s thesis. I call the newer thesis the tractability thesis, since it identifies the intuitive class of computationally tractable problems with a precise class of problems whose solutions can be computed in polynomial time. After briefly reviewing the theory of intractability, I compare the grounds for accepting the tractability thesis with the grounds for accepting Church's thesis. Intimately connected with the tractability thesis is the mathematical conjecture, whose meaning I shall shortly explain, that P≠NP. Unlike Church's thesis, this conjecture is precise enough to be capable of mathematical proof, but most computer scientists believe it even though no proof has been found. As we shall see below, understanding the grounds for acceptance of the conjecture that P≠NP has implications for general questions in the philosophy of mathematics and science, especially concerning the epistemological importance of explanatory and conceptual coherence. (shrink)

The term 'consciousness' has not been consistently used in the history of philosophy and psychology. It has been taken to stand for the mental activity in which all of us are engaged during our waking lives, whether absorbed in the solving of a task or in calm moments of contemplation. It has also been allied with the term 'introspection' to stand for a self-monitoring activity, one in which we are not simply engaged, but in which we aware of the succession (...) of mental items that constitute our experiences. In Consciousness and the Computational Mind, Ray Jackendoff opts for this second sense and exploits it to criticize the "central processing" theory of consciousness. The background assumption for all such theories is that the human mind is to be conceived on the model of a computer whose central processor controls in accordance with programmed instructions the reception of inputs, delegation of tasks to secondary processors carrying out sub-routines, storage and retrieval of information, and finally the production of outputs. The central processing theory identifies consciousness with the activities of those areas of the brain which function as central processor as defined by this model. Jackendoff's alternative theory correlates consciousness with intermediate-level informational structures operated on by secondary processors. Of the higher-level structures and the processes operating on them, the level we refer to with such terms as 'conceptualization' and 'understanding,' we are, he contends, unconscious. (shrink)

In this paper I argue for a computational theory of thinking that does not eliminate the mind. In doing so, I will defend computationalism against the arguments of John Searle and James Fetzer, and briefly respond to other common criticisms.