The following looks at several problems and questions concerning our understanding of the word ‘intelligence’ and the phrase ‘artificial intelligence’ (AI), including: how to define these terms; whether intelligence can exist in AI; if artificial intelligence in AI is identifiable; and what (if any) kind of intelligence is important to AI.

Dullest book by a major scientist I have ever read. I suppose if you know almost nothing about cognition or AI research you might find this book useful. For anyone else it is a horrific bore. There are hundreds of books in cog sci, robotics, AI, evolutionary psychology and philosophy offering far more info and insight on cognition than this one. Minsky is a top rate senior scientist but it barely shows here. He has alot of good references but they (...) are seldom discussed in any depth and there is lots more left out than included on the subject of AI, cognition and the mind. -/- Those wishing a comprehensive up to date framework for human behavior from the modern two systems view may consult my book ‘The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle’ 2nd ed (2019). Those interested in more of my writings may see ‘Talking Monkeys--Philosophy, Psychology, Science, Religion and Politics on a Doomed Planet--Articles and Reviews 2006-2019 3rd ed (2019), The Logical Structure of Human Behavior (2019), and Suicidal Utopian Delusions in the 21st Century 4th ed (2019). (shrink)

This chapter treats Hubert Dreyfus’ account of skilled coping as part of his wider project of demonstrating the sovereignty of practical intelligence over all other forms of intelligence. In contrast to the standard picture of human beings as essentially rational, individual agents, Dreyfus argued powerfully on phenomenological and empirical grounds that humans are fundamentally embedded, absorbed, and embodied. These commitments are present throughout Dreyfus’ philosophical writings, from his critique of Artificial Intelligence research in the 1970s and 1980s to (...) his rejection of John McDowell’s conceptualism in his 2005 APA Presidential Address. The present chapter articulates Dreyfus’ proposal for a contentless, non-mentalistic form of intentionality by contrasting his position with that of his U.C. Berkeley colleague John Searle and defending it as a plausible alternative to the so-called “Standard Story” of intentional action as the effect of an agent’s mental states. (shrink)

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind (...) of possibility by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is. (shrink)

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact (...) that the classical problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

‘There is no place in the phenomenology of fully absorbed coping’, writes Hubert Dreyfus, ‘for mindfulness. In flow, as Sartre sees, there are only attractive and repulsive forces drawing appropriate activity out of an active body’1. Among the many ways in which history animates dynamical systems at a range of distinctive timescales, the phenomena of embodied human habit, skilful movement, and absorbed coping are among the most pervasive and mundane, and the most philosophically puzzling. In this essay we examine (...) both habitual and skilled movement, sketching the outlines of a multidimensional framework within which the many differences across distinctive cases and domains might be fruitfully understood. Both the range of movement phenomena which can plausibly be seen as instances of habit or skill, and the space of possible theories of such phenomena are richer and more disparate than philosophy easily encompasses. We seek to bring phenomenology into contact with relevant movements in psychological theories of skilful action, in the belief that phenomenological philosophy and cognitive science can be allies rather than antagonists. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that (...) three alternative supervised learning methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

Ich habe viele kürzliche Diskussionen über die Grenzen der Berechnung und das Universum als Computer gelesen, in der Hoffnung, einige Kommentare über die erstaunliche Arbeit des Polymath Physikers und Entscheidungstheoretikers David Wolpert zu finden, aber habe kein einziges Zitat gefunden und so präsentiere ich diese sehr kurze Zusammenfassung. Wolpert bewies einige verblüffende Unmöglichkeit oder Unvollständigkeit Theoreme (1992 bis 2008-siehe arxiv dot org) über die Grenzen der Schlussfolgerung (Berechnung), die so allgemein sind, dass sie unabhängig von dem Gerät, das die Berechnung, (...) und sogar unabhängig von den Gesetzen der Physik, so dass sie für Computer, Physik und menschliches Verhalten gelten. Sie nutzen Cantors Diagonalisierung, das Lügner-Paradoxon und die Weltlinien, um das vielleicht ultimative Theorem in turing Machine Theory zu liefern, und geben scheinbar Einblicke in Unmöglichkeit, Unvollständigkeit, die Grenzen der Berechnung und das Universum als Computer, in alle möglichen Universen und alle Wesen oder Mechanismen, was unter anderem ein nicht quantenmechanisches Unsicherheitsprinzip und einen Beweis für Monotheismus erzeugt. Es gibt offensichtliche Verbindungen zum klassischen Werk von Chaitin, Solomonoff, Komolgarov und Wittgenstein und zu der Vorstellung, dass kein Programm (und damit kein Gerät) eine Sequenz (oder ein Gerät) mit größerer Komplexität erzeugen kann, als es besitzt. Man könnte sagen, dass dieses Werk den Atheismus impliziert, da es keine Entität geben kann, die komplexer ist als das physikalische Universum, und aus Wittgensteins Sicht ist "komplexer" bedeutungslos (hat keine Bedingungen der Befriedigung, d.h. Wahrheitsmacher oder Test). Selbst ein "Gott" (das heist ein "Gerät" mit unbegrenzter Zeit/Raum und Energie) kann weder bestimmen, ob eine bestimmte "Zahl" "zufällig"ist, noch einen bestimmten Weg finden, um nachzuweisen, dass eine bestimmte "Formel", "Satz" oder "Satz" oder "Gerät" (alles komplexe Sprachspiele) Teil eines bestimmten "Systems" ist. Wer aus der modernen zweisystems-Sichteinen umfassenden, aktuellen Rahmen für menschliches Verhalten wünscht, kann mein Buch "The Logical Structure of Philosophy, Psychology, Mindand Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019) konsultieren. Diejenigen,die sich für mehr meiner Schriften interessieren, können Talking Monkeys--Philosophie, Psychologie, Wissenschaft, Religion und Politik auf einem verdammten Planeten --Artikel und Rezensionen 2006-2019 2nd ed (2019) und Suicidal Utopian Delusions in the 21st Century 4th ed (2019) und andere sehen. (shrink)

The paper outlines an analysis of two types of information - ordinary and subjective, consideration is given to the difference between the concepts of intelligence and perceiving mind. It also provides description of some logical functional features of consciousness. A technical approach is proposed to technical obtaining of subjective information by changing the signal’s time degree of freedom to the spatial one in order to obtain the "observer" function in the system and information signals appearing in relation to it, that (...) serve as subjective sensations. The solution to this problem may give a new approach to designing a strong artificial intelligence and shed light on the difficult problem of the mind. (shrink)

Why would we want to develop artificial human-like arithmetical intelligence, when computers already outperform humans in arithmetical calculations? Aside from arithmetic consisting of much more than mere calculations, one suggested reason is that AI research can help us explain the development of human arithmetical cognition. Here I argue that this question needs to be studied already in the context of basic, non-symbolic, numerical cognition. Analyzing recent machine learning research on artificial neural networks, I show how AI studies (...) could potentially shed light on the development of human numerical abilities, from the proto-arithmetical abilities of subitizing and estimating to counting procedures. Although the current results are far from conclusive and much more work is needed, I argue that AI research should be included in the interdisciplinary toolbox when we try to explain the development and character of numerical cognition and arithmetical intelligence. This makes it relevant also for the epistemology of mathematics. (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)

Vor einigen, Jahren habe ich den Punkt erreicht, an dem ich normalerweise aus dem Titel eines Buches oder zumindest aus den Kapiteltiteln erzähle, welche philosophischen Fehler gemacht werden und wie häufig. Bei nominell wissenschaftlichen Arbeiten können diese weitgehend auf bestimmte Kapitel beschränkt sein, die philosophisch werden oder versuchen, allgemeine Schlussfolgerungen über die Bedeutung oder langfristige-Bedeutung des Werkes zuziehen. Normalerweise sind die wissenschaftlichen Fakten jedoch großzügig mit philosophischem Kauderwelsch darüber, was diese Tatsachen bedeuten, verwogen. Die klaren Unterscheidungen, die Wittgenstein vor etwa (...) 80 Jahren zwischen wissenschaftlichen Fragen und deren Beschreibungen durch verschiedene Sprachspiele beschrieb, werden selten berücksichtigt, so dass man abwechselnd von der Wissenschaft begeistert und von ihrer inkohärenten Analyse bestürzt ist. So ist es mit diesem Band. Wenn man einen Geist mehr oder weniger wie unseren erschaffen soll, braucht man eine logische Struktur für Rationalität und ein Verständnis der beiden Denksysteme (Dual-Prozess-Theorie). Wenn man darüber philosophieren will, muss man die Unterscheidung zwischen wissenschaftlichen Faktenfragen und der philosophischen Frage verstehen, wie Sprache im streitigen Kontext funktioniert und wie man die Fallstricke von Reduktionismus und Scientismus vermeiden kann, aber Kurzweil ist, wie die meisten Verhaltensstudenten, weitgehend ahnungslos. Er ist verzaubert von Modellen, Theorien und Konzepten und dem Erklärungsdrang, während Wittgenstein uns gezeigt hat, dass wir nur beschreiben müssen, und dass Theorien, Konzepte usw. nur Wege der Verwendung von Sprache (Sprachspiele) sind, die nur einen Wert haben, da sie einen klaren Test haben (klare Wahrheitsmacher, oder wie John Searle (der berühmteste Kritiker der AI) gerne sagt, klare Bedingungen der Zufriedenheit (COS). Ich habe versucht, in meinen jüngsten Schriften damit einen Anfang zu machen. Wer aus der modernen zweisystems-Sichteinen umfassenden, aktuellen Rahmen für menschliches Verhalten wünscht, kann mein Buch "The Logical Structure of Philosophy, Psychology, Mindand Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019) konsultieren. Diejenigen,die sich für mehr meiner Schriften interessieren, können 'Talking Monkeys--Philosophie, Psychologie, Wissenschaft, Religion und Politik auf einem verdammten Planeten --Artikel und Rezensionen 2006-2019 3rd ed (2019) und Suicidal Utopian Delusions in the 21st Century 4th ed (2019) und andere sehen. (shrink)

Due to his significant role in the development of computer technology and the discipline of artificial intelligence, Alan Turing has supposedly subscribed to the theory of mind that has been greatly inspired by the power of the said technology which has eventually become the dominant framework for current researches in artificial intelligence and cognitive science, namely, computationalism or the computational theory of mind. In this essay, I challenge this supposition. In particular, I will try to (...) show that there is no evidence in Turing’s two seminal works that supports such a supposition. His 1936 paper is all about the notion of computation or computability as it applies to mathematical functions and not to the nature or workings of intelligence. On the other hand, while his 1950 work is about intelligence, it is, however, particularly concerned with the problem of whether intelligence can be attributed to computing machines and not of whether computationality can be attributed to human intelligence or to intelligence in general. (shrink)

Diabetes is one of the most common diseases worldwide where a cure is not found for it yet. Annually it cost a lot of money to care for people with diabetes. Thus the most important issue is the prediction to be very accurate and to use a reliable method for that. One of these methods is using artificial intelligence systems and in particular is the use of Artificial Neural Networks (ANN). So in this paper, we used (...) class='Hi'>artificial neural networks to predict whether a person is diabetic or not. The criterion was to minimize the error function in neural network training using a neural network model. After training the ANN model, the average error function of the neural network was equal to 0.01 and the accuracy of the prediction of whether a person is diabetics or not was 87.3%. (shrink)

Experts in Artificial Intelligence (AI) development predict that advances in the dvelopment of intelligent systems and agents will reshape vital areas in our society. Nevertheless, if such an advance isn't done with prudence, it can result in negative outcomes for humanity. For this reason, several researchers in the area are trying to develop a robust, beneficial, and safe concept of artificial intelligence. Currently, several of the open problems in the field of AI research arise from the difficulty of (...) avoiding unwanted behaviors of intelligent agents, and at the same time specifying what we want such systems to do. It is of utmost importance that artificial intelligent agents have their values aligned with human values, given the fact that we cannot expect an AI to develop our moral preferences simply because of its intelligence, as discussed in the Orthogonality Thesis. Perhaps this difficulty comes from the way we are addressing the problem of expressing objectives, values, and ends, using representational cognitive methods. A solution to this problem would be the dynamic cognitive approach proposed by Dreyfus, whose phenomenological philosophy defends that the human experience of being-in-the-world cannot be represented by the symbolic or connectionist cognitive methods. A possible approach to this problem would be to use theoretical models such as SED (situated embodied dynamics) to address the values learning problem in AI. (shrink)

In this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for (...) PP. Indeed, some of the issues it presents seem more acute for PP, insofar as it seeks to unify all cognition and intelligence, and aims to do so without admitting any cognitive processes or mechanisms outside of the scope of the theory. I contend, however, that there is an unresolved problem for PP concerning whether it can both explain all cognition and intelligent behavior as minimizing prediction error with just the core formal elements of the PP toolbox, and also adequately comprehend (or explain away) some of the apparent cognitive differences between biological and prediction-based artificial intelligence, notably in regard to establishing relevance and flexible context-switching, precisely the features of interest to Dreyfus’ work on embodied indexicality, habits/skills, and abductive inference. I address several influential philosophical versions of PP, including the work of Jakob Hohwy and Andy Clark, as well as more enactive-oriented interpretations of active inference coming from a broadly Fristonian perspective. (shrink)

Computer assisted theorem proving is an increasingly important part of mathematical methodology, as well as a long-standing topic in artificial intelligence (AI) research. However, the current generation of theorem proving software have limited functioning in terms of providing new proofs. Importantly, they are not able to discriminate interesting theorems and proofs from trivial ones. In order for computers to develop further in theorem proving, there would need to be a radical change in how the software functions. Recently, machine learning (...) results in solving mathematical tasks have shown early promise that deep artificial neural networks could learn symbolic mathematical processing. In this paper, I analyze the theoretical prospects of such neural networks in proving mathematical theorems. In particular, I focus on the question how such AI systems could be incorporated in practice to theorem proving and what consequences that could have. In the most optimistic scenario, this includes the possibility of autonomous automated theorem provers (AATP). Here I discuss whether such AI systems could, or should, become accepted as active agents in mathematical communities. (shrink)

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps (...) mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software. (shrink)

The book is thought-provoking and informative, wide in scope while also being technically detailed, and still relevant to modem AI [at least as of 1998, the time of writing this review, but probably also at the time of posting this entry here, 2023] even though it was published in 1989. This relevance lies mainly in the book’s advocacy of distributed computation at multiple levels of description, its combining of neural networks and other techniques, its emphasis on the interplay between (...) action and perception, and its particular approach to natural language processing. The criticisms I have occasionally made above are relatively minor, and one of the major shortcomings of schema theory at least at the time of the book’s publication -- namely the lack of a detailed, preferably neural, mechanism for schema instantiation -- is one of which Arbib is well aware. The book is well produced, for the most part clearly written, and has useful summaries at the beginning of each chapter. Both general readers and AI researchers (mainly but not exclusively those working on robotics, vision and neural networks) could benefit from reading the book. (shrink)

Henry Shevlin’s paper—“How could we know when a robot was a moral patient?” – argues that we should recognize robots and artificial intelligence (AI) as psychological moral patients if they are cognitively equivalent to other beings that we already recognize as psychological moral patients (i.e., humans and, at least some, animals). In defending this cognitive equivalence strategy, Shevlin draws inspiration from the “behavioral equivalence” strategy that I have defended in previous work but argues that it is flawed in (...) crucial respects. Unfortunately—and I guess this is hardly surprising—I cannot bring myself to agree that the cognitive equivalence strategy is the superior one. In this article, I try to explain why in three steps. First, I clarify the nature of the question that I take both myself and Shevlin to be answering. Second, I clear up some potential confusions about the behavioral equivalence strategy, addressing some other recent criticisms of it. Third, I will explain why I still favor the behavioral equivalence strategy over the cognitive equivalence one. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, (...) often appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Bayesian cognitive science is a research programme that relies on modelling resources from Bayesian statistics for studying and understanding mind, brain, and behaviour. Conceiving of mental capacities as computing solutions to inductive problems, Bayesian cognitive scientists develop probabilistic models of mental capacities and evaluate their adequacy based on behavioural and neural data generated by humans (or other cognitive agents) performing a pertinent task. The overarching goal is to identify the mathematical principles, algorithmic procedures, and causal (...) mechanisms that enable cognitive agents to take uncertainty into account and weigh it appropriately in producing adaptive behaviour. -/- The appeal of Bayesian cognitive science derives from its transparency, normativity, and unifying power. Resources from Bayesian statistics allow cognitive scientists to characterise transparently, in terms of random variables and probabilistic dependencies between them, the inductive problems that many mental capacities are presumed to solve. Given this characterisation, researchers can define an upper bound on one’s performance on that problem and use it as a benchmark for interpreting experimental results, formulating hypotheses about why and when deviations from optimal performance should be expected, and seeking explanations of mental capacities within one encompassing theoretical framework. -/- Although critics question the testability, explanatory power, and plausibility of Bayesian models of mental capacities, Bayesian cognitive science has proved itself to be an incredibly fruitful research programme. Fuelled by empirical and theoretical results from psychology, neuroscience, philosophy, computer science, artificial intelligence, and evolution, Bayesian cognitive science has: advanced our understanding of why cognitive systems should keep track of uncertainty and use it to facilitate adaptive behaviour; clarified how the brain might encode information about the uncertainty of its stimuli and perform computations with probability distributions; and crystallised the insight that cognitive agents’ management of uncertainty might be grounded in predictive processes aimed at avoiding surprising exchanges with the environment. (shrink)

Hume once argued the basic science to be not physics but “the science of man” and the foundation of this science to be the empiricist mechanism of association governed by the law of similarity in appearance—now more popular than ever in the form of artificial neural networks. I update Hume’s picture by showing phenomenology to be centrally concerned with providing a unifying basis for all the sciences (including physics) by going beyond the psychology of associationism (...) (passive synthesis) to reveal phenomena that are irreducibly syntactic (not associative) in structure. I therefore argue that the language of thought (LOT) is the necessary mechanism at the basis of these descriptive phenomena. I conclude by sketching a new picture of all the sciences unified by LOT based on Husserl’s opposition to Galilean “physicomathematical” science vis-à-vis the life-world (Lebenswelt). (shrink)

Review of Mitchell, Melanie. Artificial Intelligence: A guide for thinking humans. New York: Macmillan, 2019.

My primary goal in this paper is to demonstrate the inadequacy of Hubert Dreyfus’ use of understanding (Verstehen) for Artificial Intelligence (AI). My complementary goal is to provide a principled account of Martin Heidegger’s concept of understanding (Verstehen). Dreyfus and other verificationists argue that understanding (Verstehen) is socially purposive action and skillful embodied coping. Understanding (Verstehen), conceived of in this way, purportedly challenges cognitive models of Artificial Intelligence (AI) that rely on formal rules, ‘rational’ decisionmaking, and (...) the explicit representation of knowledge. This account is unsatisfactory for two reasons. First, it maintains an extrinsic, goal-oriented intentionality that is susceptible to the success of Artificial Intelligence (AI). Second, it ignores the systematic and constitutive analysis of self-understanding (Seinsverständnis) that is fundamental to Heidegger’s ontology. Recent exegetical work replicates these inadequacies and fails to improve discussions on Heidegger’s relationship to Artificial Intelligence (AI). To resolve this oversight, I bridge the gap between Heidegger’s concept of understanding and disclosedness (Erschlossenheit) (SZ §44 / 256-278). I argue that understanding characterizes the pre-theoretical grasp of entities and the pre-ontological structure that initiates the question of self-understanding (Seinsverständnis). (shrink)

The representations of deep convolutional neural networks (CNNs) are formed from generalizing similarities and abstracting from differences in the manner of the empiricist theory of abstraction (Buckner, Synthese 195:5339–5372, 2018). The empiricist theory of abstraction is well understood to entail infinite regress and circularity in content constitution (Husserl, Logical Investigations. Routledge, 2001). This paper argues these entailments hold a fortiori for deep CNNs. Two theses result: deep CNNs require supplementation by Quine’s “apparatus of identity and quantification” in order to (...) (1) achieve concepts, and (2) represent objects, as opposed to “half-entities” corresponding to similarity amalgams (Quine, Quintessence, Cambridge, 2004, p. 107). Similarity amalgams are also called “approximate meaning[s]” (Marcus & Davis, Rebooting AI, Pantheon, 2019, p. 132). Although Husserl inferred the “complete abandonment of the empiricist theory of abstraction” (a fortiori deep CNNs) due to the infinite regress and circularity arguments examined in this paper, I argue that the statistical learning of deep CNNs may be incorporated into a Fodorian hybrid account that supports Quine’s “sortal predicates, negation, plurals, identity, pronouns, and quantifiers” which are representationally necessary to overcome the regress/circularity in content constitution and achieve objective (as opposed to similarity-subjective) representation (Burge, Origins of Objectivity. Oxford, 2010, p. 238). I base myself initially on Yoshimi’s (New Frontiers in Psychology, 2011) attempt to explain Husserlian phenomenology with neural networks but depart from him due to the arguments and consequently propose a two-system view which converges with Weiskopf’s proposal (“Observational Concepts.” The Conceptual Mind. MIT, 2015. 223–248). (shrink)

Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with the notion of representation in contemporary NLP practice. This paper attempts to fill that lacuna: drawing on ideas from cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using (...) probing classifiers, a popular analysis technique in NLP (and deep learning more broadly). The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners. It affords philosophers a novel testing-ground for claims about the nature of representation, and helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research. (shrink)

The article considers the problem of images and the role they play in our reflection turning to evidence provided by two seemingly very distant theories of mind together with two sorts of corresponding visions: dreams as analyzed by Freud who claimed that they are pictures of our thoughts, and their mechanical counterparts produced by neural networks designed for object recognition and classification. Freud’s theory of dreams has largely been ignored by philosophers interested in cognition, most of whom focused solely (...) on the linguistic incarnation of thoughts, but even though commonly neglected by respectable theories of thinking, dream images may prove to be the key to the black box of thought. As argued in this article, when seen from the right perspective and approached with the right set of questions, oneiric visions yield groundbreaking insights into the mechanism of how concepts and judgments are formed. Support for this conclusion comes from a tradition seemingly remote, if not opposed to psychoanalysis: the mechanical model of the mind conceived by information sciences and embodied in the form of artificial neural nets which mirror the activity of human visual cortex producing visions strikingly similar to those created by the sleeping mind. These purely mechanical, artificial ‘dreams’ constitute byproducts of algorithms designed for object recognition and classification, which, as I shall argue, reveals the true purpose of our own oneiric visual formations. Just like their mechanical counterparts produced by neural networks, condensations and displacements encountered in dreams are in fact visual universals. (shrink)

Online collection of papers by Devitt, Dretske, Guarino, Hochberg, Jackson, Petitot, Searle, Tye, Varzi and other leading thinkers on philosophy and the foundations of cognitive Science. Topics dealt with include: Wittgenstein and Cognitive Science, Content and Object, Logic and Foundations, Language and Linguistics, and Ontology and Mereology.

The topic of this dissertation is the nature of linguistic competence, the capacity to understand and produce sentences of natural language. I defend the empiricist account of linguistic competence embedded in the connectionist cognitive science. This strand of cognitive science has been opposed to the traditional symbolic cognitive science, coupled with transformational-generative grammar, which was committed to nativism due to the view that human cognition, including language capacity, should be construed in terms of symbolic (...) representations and hardwired rules. Similarly, linguistic competence in this framework was regarded as being innate, rule-governed, domain-specific, and fundamentally different from performance, i.e., idiosyncrasies and factors governing linguistic behavior. I analyze state-of-the-art connectionist, deep learning models of natural language processing, most notably large language models, to see what they can tell us about linguistic competence. Deep learning is a statistical technique for the classification of patterns through which artificial intelligence researchers train artificial neural networks containing multiple layers that crunch a gargantuan amount of textual and/or visual data. I argue that these models suggest that linguistic competence should be construed as stochastic, pattern-based, and stemming from domain-general mechanisms. Moreover, I distinguish syntactic from semantic competence, and I show for each the ramifications of the endorsement of a connectionist research program as opposed to the traditional symbolic cognitive science and transformational-generative grammar. I provide a unifying front, consisting of usage-based theories, a construction grammar approach, and an embodied approach to cognition to show that the more multimodal and diverse models are in terms of architectural features and training data, the stronger the case is for the connectionist linguistic competence. I also propose to discard the competence vs. performance distinction as theoretically inferior so that a novel and integrative account of linguistic competence originating in connectionism and empiricism that I propose and defend in the dissertation could be put forward in scientific and philosophical literature. (shrink)

This new anthology will serve as an ideal introduction to phenomenology for analytic philosophers, both as a text and as the single most useful source book on Husserl for cognitive scientists.

Writing within and against the set critical practices of psychoanalytic-deconstructive-Foucauldian-feminist cultural theory, Elizabeth Wilson demonstrates, in this provocative and original book, the productivity and the pleasure of direct, complicitous engagement with the contemporary cognitive sciences. Wilson forges an eclectic method in reaction to the 'zealous but disavowed moralism' of those high cultural Theorists whose 'disciplining compulsion' concocts a monolithic picture of science in order to keep their 'sanitizing critical practice' untainted by its sinister reductionism. Her unsettling accounts of (...) texts by, for example, Karl Popper, Judith Butler, Derrida, Turing, Ebbinghaus and Freud will send many readers back to the sources. It is no surprise that such a broad and ambitious project leaves many threads loose, and no criticism at all that it succeeds more in hinting at the promise of a new connectionist politics than in offering up such a hybrid fully-formed. (shrink)

“Could a machine think?” asks John R. Searle in his paper Minds, Brains, and Programs. He answers that “only a machine could think1, and only very special kinds of machines, namely brains.”2 The subject of this paper is the analysis of the aforementioned question through presentation of the symbol manipulation approach to intelligence and Searle's well-known criticism to this approach, namely the Chinese room argument. The examination of these issues leads to the systems reply of the Chinese room argument (...) and tries to illustrate that Searle's response to the systems reply does not detract from the symbol manipulation approach. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed a Logistic (...) relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, (...) convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics. (shrink)

What characterizes most technical or theoretical accounts of memory is their reliance upon an internal storage model. Psychologists and neurophysiologists have suggested neural traces (either dynamic or static) as the mechanism for this storage, and designers of artificial intelligence have relied upon the same general model, instantiated magnetically or electronically instead of neurally, to do the same job. Both psychology and artificial intelligence design have heretofore relied, without much question, upon the idea that memory is to be (...) understood as a matter of internal storage. In what follows, I shall first sketch the most important reasons for skepticism about this model, and I shall then propose an outline of an alternative way of talking about memory. This will provide an appropriate framework for suggesting a few implications for future work in artificial intelligence. (shrink)

This volume celebrates the various facets of Alan Turing (1912–1954), the British mathematician and computing pioneer, widely considered as the father of computer science. It is aimed at the general reader, with additional notes and references for those who wish to explore the life and work of Turing more deeply. -/- The book is divided into eight parts, covering different aspects of Turing’s life and work. -/- Part I presents various biographical aspects of Turing, some from a personal (...) point of view. -/- Part II presents Turing’s universal machine (now known as a Turing machine), which provides a theoretical framework for reasoning about computation. His 1936 paper on this subject is widely seen as providing the starting point for the field of theoretical computer science. -/- Part III presents Turing’s working on codebreaking during World War II. While the War was a disastrous interlude for many, for Turing it provided a nationally important outlet for his creative genius. It is not an overstatement to say that without Turing, the War would probably have lasted longer, and may even have been lost by the Allies. The sensitive nature of Turning’s wartime work meant that much of this has been revealed only relatively recently. -/- Part IV presents Turing’s post-War work on computing, both at the National Physical Laboratory and at the University of Manchester. He made contributions to both hardware design, through the ACE computer at the NPL, and software, especially at Manchester. Part V covers Turing’s contribution to machine intelligence (now known as Artificial Intelligence or AI). Although Turing did not coin the term, he can be considered a founder of this field which is still active today, authoring a seminal paper in 1950. -/- Part VI covers morphogenesis, Turing’s last major scientific contribution, on the generation of seemingly random patterns in biology and on the mathematics behind such patterns. Interest in this area has increased rapidly in recent times in the field of bioinformatics, with Turing’s 1952 paper on this subject being frequently cited. -/- Part VII presents some of Turing’s mathematical influences and achievements. Turing was remarkably free of external influences, with few co-authors – Max Newman was an exception and acted as a mathematical mentor in both Cambridge and Manchester. -/- Part VIII considers Turing in a wider context, including his influence and legacy to science and in the public consciousness. -/- Reflecting Turing’s wide influence, the book includes contributions by authors from a wide variety of backgrounds. Contemporaries provide reminiscences, while there are perspectives by philosophers, mathematicians, computer scientists, historians of science, and museum curators. Some of the contributors gave presentations at Turing Centenary meetings in 2012 in Bletchley Park, King’s College Cambridge, and Oxford University, and several of the chapters in this volume are based on those presentations – some through transcription of the original talks, especially for Turing’s contemporaries, now aged in their 90s. Sadly, some contributors died before the publication of this book, hence its dedication to them. -/- For those interested in personal recollections, Chapters 2, 3, 11, 12, 16, 17, and 36 will be of interest. For philosophical aspects of Turing’s work, see Chapters 6, 7, 26–31, and 41. Mathematical perspectives can be found in Chapters 35 and 37–39. Historical perspectives can be found in Chapters 4, 8, 9, 10, 13–15, 18, 19, 21–25, 34, and 40. With respect to Turing’s body of work, the treatment in Parts II–VI is broadly chronological. We have attempted to be comprehensive with respect to all the important aspects of Turing’s achievements, and the book can be read cover to cover, or the chapters can be tackled individually if desired. There are cross-references between chapters where appropriate, and some chapters will inevitably overlap. -/- We hope that you enjoy this volume as part of your library and that you will dip into it whenever you wish to enter the multifaceted world of Alan Turing. (shrink)

Avant de commenter en détail sur Making the Social World (MSW) Je vais d’abord offrir quelques commentaires sur la philosophie (psychologie descriptive) et son rapport à la recherche psychologique contemporaine comme illustré dans les œuvres de Searle (S) et Wittgenstein (W), puisque je pense que c’est la meilleure façon de placer Searle ou tout commentateur sur le comportement, en perspective appropriée. Il aidera grandement à voir mes critiques de PNC, TLP, PI, OC, TARW et d’autres livres par ces deux génies (...) de la psychologie descriptive. S ne fait aucune référence à la déclaration prémonitoire de L’esprit de W comme mécanisme dans TLP, et sa destruction de celui-ci dans son travail ultérieur. Depuis W, S est devenu le principal déconstructeur de ces vues mécaniques du comportement, et le psychologue descriptif le plus important (philosophe), mais ne se rend pas compte à quel point W l’a anticipé complètement ni, dans l’ensemble, faire d’autres (mais voir les nombreux papiers et livres de Proudfoot et Copeland sur W, Turing et AI). Le travail de S est beaucoup plus facile à suivre que celui de W, et bien qu’il y ait un certain jargon, il est surtout spectaculairement clair si vous l’approchez de la bonne direction. Voir mes commentaires de W S et d’autres livres pour plus de détails. Dans l’ensemble, MSW est un bon résumé des nombreux progrès substantiels sur Wittgenstein résultant du demi-siècle de travail de S, mais à mon avis, W est toujours inégalé pour la psychologie de base une fois que vous saisissez ce qu’il dit (voir mes commentaires). Idéalement, ils devraient être lus ensemble: Searle pour la prose clairement cohérente et les généralisations sur le fonctionnement de S2/S3, illustré avec les exemples perspicaces de W de l’opération de S1/S2, et ses aphorismes brillants. Si j’étais beaucoup plus jeune, j’écrirais un livre faisant exactement cela. Ceux qui souhaitent un cadre complet à jour pour le comportement humain de la vue moderne de deuxemssyst peuvent consulter mon livre 'The Logical Structure of Philosophy, Psychology, Mind and Language in Ludwig Wittgenstein and John Searle' 2nd ed (2019). Ceux qui s’intéressent à plus de mes écrits peuvent voir «Talking Monkeys --Philosophie, Psychologie, Science, Religion et Politique sur une planète condamnée --Articles et revues 2006-2019 3e ed (2019) et Suicidal Utopian Delusions in the 21st Century 4th ed (2019) et autres. (shrink)

In this paper an Artificial Neural Network (ANN) model was used to help cars dealers recognize the many characteristics of cars, including manufacturers, their location and classification of cars according to several categories including: Make, Model, Type, Origin, DriveTrain, MSRP, Invoice, EngineSize, Cylinders, Horsepower, MPG_Highway, Weight, Wheelbase, Length. ANN was used in prediction of the number of miles per gallon when the car is driven in the city(MPG_City). The results showed that ANN model was able to predict MPG_City (...) with 97.50 % accuracy. The factor of DriveTrain has the most influence on MPG_City evaluation. Similar studies can be carried out for the evaluation of other characteristics of cars. (shrink)

Theoretiker der Künstlichen Intelligenz und deren Wegbegleiter in der Philosophie des Geistes haben auf unterschiedliche Weise auf Kritik am ursprünglichen Theorieziel der KI reagiert. Eine dieser Reaktionen ist die Zurücknahme dieses Theorieziels zugunsten der Verfolgung kleinerformatiger Projekte. Eine andere Reaktion ist die Propagierung konnektionistischer Systeme, die mit ihrer dezentralen Arbeitsweise die neuronalen Netze des menschlichen Gehirns besser simulieren sollen. Eine weitere ist die sogenannte robot reply. Die Roboterantwort besteht aus zwei Elementen. Sie enthält (a) das Zugeständnis, daß das Systemverhalten eines (...) wie auch immer programmierten konventionellen Digitalrechners mit von Neumann-Architektur nicht schon menschenähnliche Intelligenz aufweist, und (b) die Behauptung, daß es für bestimmte Arten von Maschinen doch zur Intelligenz reicht. In die Liga der intelligenten Wesen könnten Maschinen genau dann aufsteigen, wenn sie Roboter sind. Damit ist gemeint: wenn sie über Wahrnehmungskomponenten (Rezeptoren) und Handlungskomponenten (Effektoren) verfügen, mithilfe deren sie aktiv in kausale Interaktionen mit ihrer Umwelt eintreten können. Im Beitrag wird für die These argumentiert, daß der Roboterantwort eine richtige Intuition zugrunde liegt, von der die Roboterfreunde sich aber zu einer kurzschlüssigen Folgerung verleiten lassen. Es ist richtig, mentale Zustände und Handlungskompetenz eng aneinander zu binden. Einem Wesen, dem man Handlungsfähigkeit zuerkennt, kann man mentale Zustände nicht absprechen. Doch Handelnkönnen und Geisthaben sind nicht hinreichend unabhängig voneinander, als daß man das eine als Rechtfertigung für die Zuerkennung des anderen verwenden könnte. Man sollte Robotern beides absprechen. (shrink)

The papers you will find in this special issue of JoLLI develop letter and spirit of Turing’s original contributions. They do not lazily fall back into the same old sofa, but follow – or question – the inspiring ideas of a great man in the search for new, more precise, conclusions. It is refreshing to know that the fertile landscape created by Alan Turing remains a source of novel ideas.

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. -/- (This paper was reprinted in: The Turing Test: The Elusive Standard of Artificial Intelligence, J. H. Moor, ed., Kluwer Academic, pp. 23-78, 2003.). (shrink)

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. (shrink)

The representation of propositional attitudes (beliefs, desires, etc.) and the analysis of natural-language, propositional-attitude reports presents difficult problems for cognitive science and artificial intelligence. In particular, various representational approaches to attitudes involve the incorrect “imputation,” to cognitive agents, of the use of artificial theory-laden notions. Interesting cases of this problem are shown to occur in several approaches to attitudes. The imputation problem is shown to arise from the way that representational approaches explicate properties and relationships, (...) and in particular from the way they explicate propositional attitudes themselves. Another factor contributing to imputation is the compositional nature of typical semantic approaches to propositional-attitude reports. Some strategies for avoiding undesirable imputation are examined. One of the main conclusions is that the importance of imputations that arise in a representation scheme depends strongly on the use to which the scheme is put -- on whether, for instance, the scheme is used as part of a formal, objective account of natural language, or is used rather as a representational tool within an agent. (shrink)

The article deals with some ideas by Turing concerning the background and the birth of the well-known Turing Test, showing the evolution of the main question proposed by Turing on thinking machine. The notions he used, especially that one of imitation, are not so much exactly defined and shaped, but for this very reason they have had a deep impact in artificial intelligence and cognitive science research from an epistemological point of view. Then, it is suggested that (...) the fundamental concept involved in Turing’s imitation game, conceived as a test for detecting the presence of intelligence in an artificial entity, is the concept of interaction, that Turing adopts in a wider, more intuitive and more fruitful sense than the one that is proper to the current research in interactive computing. (shrink)

Abstract:This paper considers novel ethical issues pertaining to near-future artificial intelligence (AI) systems that seek to support, maintain, or enhance the capabilities of older adults as they age and experience cognitive decline. In particular, we focus on smart assistants (SAs) that would seek to provide proactive assistance and mediate social interactions between users and other members of their social or support networks. Such systems would potentially have significant utility for users and their caregivers if they could reduce the (...) cognitive load for tasks that help older adults maintain their autonomy and independence. However, proactively supporting even simple tasks, such as providing the user with a summary of a meeting or a conversation, would require a future SA to engage with ethical aspects of human interactions which computational systems currently have difficulty identifying, tracking, and navigating. If SAs fail to perceive ethically relevant aspects of social interactions, the resulting deficit in moral discernment would threaten important aspects of user autonomy and well-being. After describing the dynamic that generates these ethical challenges, we note how simple strategies for prompting user oversight of such systems might also undermine their utility. We conclude by considering how near-future SAs could exacerbate current worries about privacy, commodification of users, trust calibration and injustice. (shrink)

The content of this paper is an elaboration of Hubert L. Dreyfus’s philosophical critique of Artificial Intelligence (AI), computers and the internet. Hubert L. Dreyfus (1929-2017) is Ua SA philosopher and alumni of Harvard University who teach at the Massachusetts Institute of Technology (MIT) and University of California, Berkeley. He is a phenomenological philosopher who criticize computer researchers and the artificial intelligence community. In 1965, Dreyfus wrote an article for Rand Corporation titled “Alchemy and Artificial (...) Intelligence” which criticizes the masterminds of Artificial Intelligence. Dreyfus also criticized the order of computers via two books: (1) What Computers Can’t Do (1972) and (2) What Computers Stills Can’t Do (1992). He favored human intuition rather than the computer logic in his book Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer (1986). In 2001, Dreyfus wrote a book On the Internet, which considers the prominent phenomenon in the recent Industry 4.0. By elaborating on Dreyfus’s philosophy on the computer, artificial intelligence, and the internet, we will know the philosophical debate on the result of industry 3.0 (computer and artificial intelligence) and 4.0 (artificial intelligence and internet). Moreover, we will know the relation between humans and those industrial products. (shrink)

This dissertation aims to examine whether John Searle’s biological naturalism is a more viable alternative to current physicalist and functionalist positions in dealing with the issue of free will. Thus, my strategy is to identify the assumptions of these lines of thought and their philosophical consequences. In order to accomplish this goal the concept of intrinsic intentionality is taken as a guide. I begin by defining what is meant by free will and go on to broadly characterize physicalist and (...) functionalist positions in philosophy of mind. Then, I go on to show how the question of free will arises and can be crucial to such currents of thought. Subsequently, I summarize the biological naturalist position (especially regarding the ontology of consciousness and the question of intentionality) and oppose it to physicalism and functionalism in order to examine the possibility of free will. In this opposition, each theory is decomposed into its main tenets so that they can be critically analyzed. In this analysis, it appears that free will does not seem to find any room in the scenario presented by physicalism and functionalism. It is argued that Searlean biological naturalism is able to explain – better than the other two positions – how free action can be motivated by something that is external to the mental state which is itself performing the action. I then evaluate the ethical implications of these findings, articulating the issues of intrinsic intentionality, free will, strong artificial intelligence in order to conclude that current machines cannot be assigned moral responsibility, since they are not capable of intrinsic intentionality. Then, I argue for the evolutionary origin of intentionality and therefore morality. Finally, I argue that neuroscience does not eliminate moral responsibility since it does not prove that free will is an illusion, i.e., that this branch of science does not contradict John Searle’s biological naturalism. (shrink)

Revista Intelligence Info este o publicație trimestrială din domeniile intelligence, geopolitică și securitate, și domenii conexe de studiu și practică. -/- Cuprins: -/- EDITORIALE / EDITORIALS -/- Tiberiu TĂNASE Considerații privind necesitatea educării și formării resursei umane pentru intelligence–ul național Considerations regarding the need to educate and train human resources for national intelligence Nicolae SFETCU Epistemologia activității de intelligence Epistemology of intelligence Nicolae SFETCU Rolul serviciilor de informații în război The role of the intelligence agencies in a war Nicolae SFETCU (...) Evoluția inteligenței artificiale în domeniul securității naționale The evolution of artificial intelligence in the field of national security -/- INTELLIGENCE / INTELLIGENCE -/- Ovidiu BOUREANU O scurtă istorie a spionajului: spionajul modern de la Războiul Rece la Războiul împotriva terorii A Brief History of the Spy: Modern Spying from the Cold War to the War on Terror Ioana PĂIUȘ, Tiberiu TĂNASE Securitate și informații într-o lume în schimbare Security and intelligence in a changing world Nicolae SFETCU Activitatea de intelligence - Ciclul intelligence Intelligence Activity – Intelligence Cycle Tiberiu TĂNASE Controlul asupra Intelligence –ului Control over Intelligence Tiberiu TĂNASE Informația și nevoia de gestionare a resurselor informaționale Information and the need to manage information resources Tiberiu TĂNASE Controlul parlamentar al serviciilor de informații Parliamentary control of intelligence services Tiberiu TĂNASE Mecanisme de control democratic al activității serviciilor de informații Mechanisms of democratic control of the activity of intelligence agencies Nicolae SFETCU Analiza informațiilor Intelligence Analysis Bogdan-George RĂDULESCU Cum ar trebui să fie utilizată analiza intelligence pentru a contracara războiul cognitiv? How should intelligence analysis be utilized to counter cognitive warfare? -/- ISTORIE / HISTORY -/- Tiberiu TĂNASE Biroul de Informații al Secției Militare din Transilvania și rolul său în campania pentru apărarea României Mari The Intelligence Office of the Transylvanian Military Section and its role in the campaign for the defense of Greater Romania Stan PETRESCU Pacepa, Kuklinski, Pollard și românii Pacepa, Kuklinski, Pollard and the Romanians Tiberiu TĂNASE Contracararea de către SSI a activităților informative și acțiunilor Serviciilor Germane și Sovietice de Informații implicate în evenimentele din 21 - 23 ianuarie 1941 Countering by the SSI of the informational activities and actions of the German and Soviet Intelligence Services involved in the events of January 21-23, 1941 Tiberiu TĂNASE Alexandru Ioan Cuza, fondatorul Intelligence-lui statului modern românesc Alexandru Ioan Cuza, Founder of the Intelligence of the Modern Romanian State Tiberiu TĂNASE, Bogdan MUSCALU Puncte de vedere privind tradițiile romane în istoria intelligence-lui Viewpoints on Roman traditions in history intelligence Tiberiu TĂNASE Istoria Serviciului Român de Informații – serviciu de intelligence competitiv, și strategiile de intelligence History of the Romanian Intelligence Service – competitive intelligence service, and intelligence strategies Rodica LISEANU Reformele serviciilor secrete de la Mihail Moruzov la Eugen Cristescu, între inovație și decadență Secret service reforms from Mihail Moruzov to Eugen Cristescu Rodica LISEANU Take Ionescu – O biografie vectorială în istoria partidelor politice și în semantica diplomației Take Ionescu - A Vectorial Biography in the History of Political Parties and the Semantics of Diplomacy Remus MACOVEI Trădători și trădători Traitors and traitors -/- GEOPOLITICA / GEOPOLITICS -/- Antoaneta AVRAM Pandemia de COVID-19 și consecințele ei în peisajul anului 2022 The COVID-19 pandemic and its consequences in the landscape of 2022 Darius-Antoniu FERENȚ, Ioan MANCI Analiza conflictului ruso-cecen din perspectivă militară Analysis of the Russian-Chechen conflict from a military perspective Lisa-Maria ACHIMESCU Apusul universalismului european The decline of European universalism Emilian M. DOBRESCU Hegemonia chineză în producția de pământuri rare Chinese Hegemony in the Production of Rare Earths Ionuț-Daniel GORGOVAN ”Descurajarea integrată”. Analiză asupra strategiei SUA în Pacific "Integrated deterrence". Analysis of US Pacific Strategy Tiberiu TĂNASE, Violeta Ioana NAGÂȚ Conflictul arabo-israelian. Percepții privind cauzele și natura conflictului. Repere istorice și actualitate The Arab-Israeli conflict. Perceptions of the causes and nature of conflict. Historical landmarks and current affairs -/- SECURITATE / SECURITY -/- Tănase TIBERIU Minighid de pregătire și protecție contrainformativă Counterintelligence preparation and protection mini-guide Darius-Antoniu FERENȚ ISIS - Jocul morții - Martiri, asasinate și fascinație The Killing Game - Martyrdom, Murder and the Lure of ISIS Traian OSTAHIE Abordări generale ale conceptului de cultură de securitate For a 21st century realism Dalia Elena SILAGHI Terorismul cibernetic: o formă contemporană de terorism Cyber Terrorism: A Contemporary Form of Terrorism Paul Alexandru HELER Conflictul asimetric și consecințele sale în securitatea Asiei Asymmetric Conflict and Its Consequences in Asian Security Daria-Maria TÂRNĂVEAN Confruntarea informațională și repercusiunile sale asupra mediului actual de securitate The Information Confrontation and its Repercussions on the Current Security Environment Darius-Antoniu FERENȚ Analiza atacurilor cibernetice, precum APT (Advanced Persistent Threat) și ransomware Analysis of cyber attacks such as APT (Advanced Persistent Threat) and ransomware Daniela Georgiana GOLEA, Oana Ștefania COȘA Nevoia de adaptare managerială a noilor probleme de securitate modernă The need for managerial adaptation to new modern security issues Darius-Antoniu FERENȚ, Corneliu PREJA Implicarea NATO în domeniul apărării cibernetice NATO's involvement in cyber defence Alexandru CRISTIAN Paradigme în mediul internațional de securitate Paradigms in the international security environment Alexandru CRISTIAN Schimbarea paradigmelor în mediul internațional de securitate Changing paradigms in the international security environment Dan D. FARCAȘ Adevăr și dezinformare în fenomenul OZN Truth and disinformation in the UFO phenomenon Alexandru CRISTIAN Un nou mediu de securitate A new security environment -/- ȘTIINȚA INFORMAȚIEI / INFORMATION SCIENCE -/- Oana Ștefania COȘA Rolul structurilor de intelligence medical în atingerea obiectivul de consolidare a sistemului de sănătate The role of medical intelligence structures in achieving the objective of strengthening the health system Nicolae SFETCU Preocupări legislative în mineritul datelor Legislative concerns in data mining Nicolae SFETCU Utilizarea analiticii rețelelor sociale în intelligence Using social network analytics in intelligence Nicolae SFETCU Ingineria cunoașterii în colectarea informațiilor Knowledge Engineering in Intelligence Gathering -/- ISSN 2971 – 9089 ISSN-L 2821 – 8159, DOI: 10.58679/II26972. (shrink)