Czy coś naprawdę „odchodzi” całkowicie? Niniejszy artykuł jest poszukiwaniem „rzeczywistego usunięcia” i metafizyki, która musi temu towarzyszyć. Dlaczego to jest ważne? W badaniach nad sztuczną inteligencją naukowcy zaproponowali ruchomy punkt docelowy do momentu osiągnięcia sztucznej inteligencji. Zaczęło się od testu Turinga i ewoluowało przez dużą ilość argumentacji (np. Dreyfussa, dotyczącą tego, czego nie potrafią komputery, przez „osobliwość” Kurzweila oraz wiele innych kryteriów i tysiące dyskusji na temat tego, czym jest inteligencja i co oznaczałoby ją symulować lub, jak wolę, naśladować). Cała (...) ta dyskusja wciąż polega na rozważaniu analogii do ludzkiej inteligencji, a nie na zbliżaniu się do samej rzeczy, ale dobre analogie muszą dotyczyć czegoś znacznie więcej niż funkcji analogicznej: muszą zbliżać się do prawdziwej nieodróżnialności. Moje wywody będą zatem dotyczyły głównie dziedziny metafizyki i ontologii, tak jak rozumiem słowo „rzeczywisty” w wyrażeniu „rzeczywiste usunięcie”. Nie sądzę, aby współcześni badacze właściwie rozumieli problem czasu i jego wpływ na kryterium lub. kryteria sztucznej inteligencji. Stąd j w tych ramach oferuję „rzeczywiste usunięcie” w znaczeniu, które należy właśnie opisać. Argument AI ma implikacje dla całej metafizyki, ponieważ odnosi się do fundamentalnego charakteru czasu. (shrink)

While today there is much discussion about the ethics of artificial intelligence, less work has been done on the philosophical nature of AI. Drawing on Bergson and Ricoeur, this paper proposes to use the concepts of time, process, and narrative to conceptualize AI and its normatively relevant impact on human lives and society. Distinguishing between a number of different ways in which AI and time are related, the paper explores what it means to understand AI as narrative, as (...) process, or as the emergent outcome of processes and narratives. It pays particular attention to what it calls the “narrator” and “time machine” roles of AI and the normative implications of these roles. It argues that AI processes and narratives shape our time and link past, present, and future in particular ways that are ethically and politically significant. (shrink)

For over a decade John Searle's ingenious argument against the possibility of artificial intelligence has held a prominent place in contemporary philosophy. This is not just because of its striking central example and the apparent simplicity of its argument. As its appearance in Scientific American testifies, it is also due to its importance to the wider scientific community. If Searle is right, artificial intelligence in the strict sense, the sense that would claim that mind can be instantiated through (...) a formal program of symbol manipulation, is basically wrong. No set of formal conditions can provide us with the characteristic feature of mind which is the intentionally of its mental contents. Formally regarded, such intentionally is an irreducible primitive. It cannot be analyzed into non-intentional (purely syntactic, symbolic) components. This paper will argue that this objection is based on a misunderstanding. Intentionality is not simply something given which is incapable of further analysis. It only appears so when we mistakenly abstract it from time. When we regard its temporal structure, it shows itself as a rule-governed, synthetic process, one capable of being instantiated both by machines and men. (shrink)

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first‐order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. (...) It may be possible to implement a temporal‐inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history. (shrink)

This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information and data-processing systems of all kinds, no matter whether human, (other) animal or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and phi losophical psychology through issues in cognitive psychology and socio biology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. While (...) primary emphasis will be placed upon theoretical, conceptual and epistemologi cal aspects of these problems and domains, empirical, experimental and methodological studies will also appear from time to time. Among the most challenging and difficult projects within the scope of artificial intelligence is the development and implementation of com puter programs suitable for processing natural language. Our purpose in compiling the present volume has been to contribute to the foundations of this enterprise by bringing together classic papers devoted to crucial problems involved in understanding natural language, which range from issues of formal syntax and logical form to those of possible-worlds and situation semantics. The book begins with a comprehensive introduc tion composed by Jack Kulas, the senior editor of this work, which pro vides a systematic orientation to this complex field, and ends with a selected bibliography intended to promote further research. If our efforts assist others in dealing with these problems, they will have been worthwhile. J. H. F. (shrink)

Economic value additions to knowledge and demand provide practical, embedded and extensible meaning to philosophizing cognitive systems. Evaluation of a cognitive system is an empirical matter. Thinking of science in terms of distributed cognition (interactionism) enlarges the domain of cognition. Anything that actually contributes to the specific quality of output of a cognitive system is part of the system in time and/or space. Cognitive science studies behaviour and knowledge structures of experts and categorized structures based on underlying structures. Knowledge representation (...) through understanding of ‘epistemic cultures’ is an evolutionary stage. But cognition goes beyond knowledge representation. Notwithstanding the importance of epistemology of phenomena, the practicability cum philosophical aspects of machine learning needs to be seen in dynamic behaviour in socio-economic-technical value additions if human machine interaction processes that are context specific are incorporated into strong artificial intelligent systems. Cognitive Science is also studied from both computational and biological angles. Evolution of interactive forms of reasoning through understanding of meta-language of computations or biological learning processes is possible. But the limitation of historical cultures predefines the role of interactive processes in user-networks beyond technology networks. Despite this limitation, inclusive development notions of a heterogeneous national society such as India or Europe can be tested and incorporated. (shrink)

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has possible states, and a traveling salesperson problem with as little as 82 cities already has more than different tours (similar to chess). Biological neurons (...) are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem—not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost–benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving. (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the (...) applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the (...) applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

The article describes the possibilities of using and modifying existing machine learning technologies in the field of natural language processing for the purpose of designing a system for automatically generating control and test tasks (CTT). The reason for such studies was the limitations in generating theminimumrequired amount ofCTtomaintain student engagement in game-based learning formats, such as quizzes, and others. These limitations are associated with the lack of time resources among training professionals for manual generation of tests. The article discusses the (...) applied research of the Large Language Model (LLM) and Generative pre-trained transformer (GPT) technologies for the development of a system for automatic generation of tests for the purpose of its implementation in the BoxBattle gamified learning platform. The result of such applied research can be a system for automatic generation of tests, which will reduce the time for developing tests. As a result, this will allow teachers to free up time to implement a personalized approach to teaching and develop students’soft skills. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Philosophic Foundations of Mimetic Theory and Cognitive Science(Including Artificial Intelligence)Jean-Pierre Dupuy (bio)In the mid 1970s I discovered at the same time cognitive science and mimetic theory. Being a philosopher with a scientific background, I immediately brought them together and tried to reconceptualize the latter in terms of the former. In a sense, I haven't stopped doing that in the last 45 years. That is why I feel (...) fully at home with the goals pursued by the organizers of this conference.I do not believe it is feasible or even desirable to operate the rapprochement between the two disciplines at a global level. The history of cognitive science is exceedingly involved, and the part played by artificial intelligence in it compared to the part played by the neurosciences has varied greatly over time.1 Only a piecemeal approach makes sense. I present here four short case studies that cover the whole period, from the beginnings in cybernetics to today's artificial intelligence (AI).In order to better situate these studies within a broader frame, I propose the diagram in Figure 1. To comment on it would require more time than available for my whole presentation. Let me just say the following. [End Page 1]It is essential to distinguish between two kinds of AI. The first to bear this name was launched by economist Herbert Simon and computer scientist Allan Newell at a Dartmouth conference in 1956. It was based on the premise that thinking is computing, with this computation bearing on sentences of a "language of thought" implementable in a digital computer. It turns out that this AI(1) was progressively substituted from the 1980s onward by an AI(2), which, to some extent, was a return to a fundamental result obtained by cybernetics in 1943. Neurophysiologist Warren McCulloch and mathematician Walter Pitts had constructed a mathematical model of a network of elementary calculators called "neurons" and explored its properties. The capacity to "think" was one of these. It was not situated at the elementary level, as was the case in AI(1), but it emerged at the collective level from the interactions between the neurons.This model was heavily criticized by the supporters of AI(1),2 and its first implementations, in the notorious "Perceptron" in particular (1957), were pitiful failures. However, the emergence of "second-order cybernetics" under the guidance of cybernetician Heinz von Foerster and the theory of complex, self-organizing systems that it helped develop, in cooperation and rivalry with similar efforts in the physical sciences, enabled the neural network model to come to a second life under different names: formal neurons, deep learning, big data, and the like. Together, they constitute AI(2).The following network of influences wouldn't have been possible without its stem, that is, the boxes containing the names of Kurt Gödel and Alan Turing. The former revolutionized logic by showing that reasoning about arithmetic and arithmetic are one and the same, insofar as it is possible to code in a 1-to-1 fashion propositions about integers (for instance, "7 is a prime number") by means of integers. The latter demonstrated that some mechanisms can self-transcend inasmuch as they generate an ensemble of outputs that cannot be defined mechanistically. Later, John von Neumann, who is credited along with Alan Turing with having invented the digital computer, would use this logical possibility to define complexity: A mechanism is complex if it is capable of generating one more complex than itself. The gate was open for the theories of complex, self-organizing systems to thrive and, by way of consequence, for AI(2) to emerge.fascination with modelsHerbert Simon (1916–2001), winner of the Nobel Prize in economics for his work on what he called "bounded rationality," is generally considered to be one [End Page 2] Click for larger view View full resolutionFigure 1.of the founders of artificial intelligence. Some cognitive scientists still rely on the computer program that he devised with Alan Newell to simulate the creative processes of human thought—a program aptly named "General Problem Solver." The title of one of his principal works, The Sciences of the... (shrink)

Time’s arrow is necessary for progress from a past that has already happened to a future that is only potential until creatively determined in the present. But time’s arrow is unnecessary in Einstein’s so-called block universe, so there is no creative unfolding in an actual present. How can there be an actual present when there is no universal moment of simultaneity? Events in various places will have different presents according to the position, velocity, and nature of the perceiver. Standing against (...) this view is traditional common sense since we normally experience time’s arrow as reality and the present as our place in the stream of consciousness, but we err to imagine we are living in the actual present. The present of our daily experience is actually a specious present, according to E. Robert Kelly (later popularized by William James), or duration, according to Henri Bergson, an habitus, as elucidated by Kerby (1991), or, simply, the psychological present (Adams, 2010) – all terms indicating that our experienced present so consists of the past overlapping into the future that any potential for acting from the creative moment is crowded out. Yet, for philosophers of process from Herakleitos onward, it is the philosophies of change or process that treat time’s arrow and the creative fire of the actual present as realities. In this essay, I examine the most well known but possibly least understood process cosmology of Alfred North Whitehead to seek out this elusive but actual present. In doing so, I will also ask if process philosophy is itself an example of the creative imagination and if this relates to doing science. I conclude Whitehead's process philosophy falls short of allowing for the actual creative spontaneity of a dynamic (eternal) present. (shrink)

By reviewing most of the neurobiology of consciousness, this article highlights some major reasons why a successful emulation of the dynamics of human consciousness by artificial intelligence is unlikely. The analysis provided leads to conclude that human consciousness is epigenetically determined and experience and context-dependent at the individual level. It is subject to changes in time that are essentially unpredictable. If cracking the code to human consciousness were possible, the result would most likely have to consist of a temporal (...) pattern code simulating long-distance signal reverberation and de-correlation of all spatial signal contents from temporal signals. In the light of the massive evidence for complex interactions between implicit (non-conscious) and explicit (conscious) contents of representation, the code would have to be capable of making implicit (non-conscious) processes explicit. It would have to be capable of a progressively less and less arbitrary selection of temporal activity patterns in a continuously developing neural network structure identical to that of the human brain, from the synaptic level to that of higher cognitive functions. The code’s activation thresholds would depend on specific temporal signal coincidence probabilities, vary considerably with time and across individual experience data, and would therefore require dynamically adaptive computations capable of emulating the properties of individual human experience. No known machine or neural network learning approach has such potential. (shrink)

This chapter discusses the problem of time in the thought of Bergson, showing how the evolution of the concept of duration is conducive to new developments in the philosophy of intuition. Duration, which in the Essay connotes the experience of a non-measurable lived experience, while in Matter and Memory it assumes rhythms of different intensities to justify the relationship between perception and memory, as well as in Creative Evolution is judged as the fabric of reality itself, in Duration and (...) Simultaneity it is posited not only as a criterion to discern what is real and what is artificial, but also to justify the measurement of reality, that is to say, to restore the point of contact between time as duration and space, that seemed previously compromised by the loss of ontological consistency of extension as the dimension of the body. In this paper, then, Bergson, also by virtue of a controversial, detailed comparison with the theory of relativity, finally arrives to support the hypothesis of a single temporal dimension, curved and dynamic, where space comes to be outlined as the abstract tangent of time. It is at this level, then, that it becomes possible to examine the emergence of the concepts within the meanders of intuition as concepts imbued with meaning: here is the primary source of that continuous “added” element of new nuclei of creativity that characterizes the very logic of Bergsonian living where what, as ideal relationship, is time, as a real relationship, becomes life, i.e., the continuous opening of a register in which time is inscribed, becoming incarnate thus in the very nature of every organism. (shrink)

Strategies for improving the explainability of artificial agents are a key approach to support the understandability of artificial agents’ decision-making processes and their trustworthiness. However, since explanations are not inclined to standardization, finding solutions that fit the algorithmic-based decision-making processes of artificial agents poses a compelling challenge. This paper addresses the concept of trust in relation to complementary aspects that play a role in interpersonal and human–agent relationships, such as users’ confidence and their perception of artificial (...) agents’ reliability. Particularly, this paper focuses on non-expert users’ perspectives, since users with little technical knowledge are likely to benefit the most from “post-hoc”, everyday explanations. Drawing upon the explainable AI and social sciences literature, this paper investigates how artificial agent’s explainability and trust are interrelated at different stages of an interaction. Specifically, the possibility of implementing explainability as a trust building, trust maintenance and restoration strategy is investigated. To this extent, the paper identifies and discusses the intrinsic limits and fundamental features of explanations, such as structural qualities and communication strategies. Accordingly, this paper contributes to the debate by providing recommendations on how to maximize the effectiveness of explanations for supporting non-expert users’ understanding and trust. (shrink)

Artificial intelligence systems have been widely applied to various contexts, including high-stake decision processes in healthcare, banking, and judicial systems. Some developed AI models fail to offer a fair output for specific minority groups, sparking comprehensive discussions about AI fairness. We argue that the development of AI systems is marked by a central paradox: the less participation one stakeholder has within the AI system’s life cycle, the more influence they have over the way the system will function. This means (...) that the impact on the fairness of the system is in the hands of those who are less impacted by it. However, most of the existing works ignore how different aspects of AI fairness are dynamically and adaptively affected by different stages of AI system development. To this end, we present a use case to discuss fairness in the development of corporate wellness programs using smart wearables and AI algorithms to analyze data. The four key stakeholders throughout this type of AI system development process are presented. These stakeholders are called service designer, algorithm designer, system deployer, and end-user. We identify three core aspects of AI fairness, namely, contextual fairness, model fairness, and device fairness. We propose a relative contribution of the four stakeholders to the three aspects of fairness. Furthermore, we propose the boundaries and interactions between the four roles, from which we make our conclusion about the possible unfairness in such an AI developing process. (shrink)

The movement of Artificial Intelligence (AI) research from universities to big corporations has had a significant impact on the development of the field. In the past, AI research was primarily conducted in academic institutions, which foster a culture of peer reviewing and collaboration to enhance quality improvements. The growing interest in AI among corporations, especially regarding Machine Learning (ML) technology, has shifted the focus of research from quality to quantity. Corporations have the resources to invest in large-scale ML projects (...) and they are often more interested in fast results than in ensuring that AI algorithms are reliable and safe. This paper proposes that the description of a Darwinian process made by the mathematician and physicist Freeman Dyson can be used to understand the implications of ML research scenario. The context of the Darwinian process—or lack thereof—can be used to draw a parallel between historical and current implications of technology expansion strongly driven by economic interests, making clear the consequences of shifting AI research from academia to industry. Finally, this paper indicates future directions for AI research to create a healthier environment for the evolution of ML technology. (shrink)

To assist in the evaluation process when determining architectures for new robots and intelligent systems equipped with artificial emotions, it is beneficial to understand the systems that have been built previously. Other surveys have classified these systems on the basis of their technological features. In this survey paper, we present a classification system based on a model similar to that used in psychology and philosophy for theories of emotion. This makes possible a connection to thousands of years of discourse (...) on the topic of emotion. Five theories of emotion are described based on an emotion theory model proposed by Power and Dalgleish. The paper provides classifications using a model of 10 new questions, for 14 major research projects that describe implementations or designs for systems that use artificial emotions for either robotics or general artificial intelligence. We also analyze the trends in the usage of various theories and complexity changes over time. (shrink)

In this contribution, I will argue for an ontological understanding of time as temporality. This, however, implies that in a certain sense being is temporality, by which I mean that on an ontological level temporality is nothing but the process of change, i.e. the dynamic aspect of being in its becoming, changing, and perishing, and that concrete beings are not merely in time, but they are temporal. This leads to the conclusion that actual time is the process of change that (...) becoming beings are, as well as the conclusion that reality is fundamentally temporal as argued by process metaphysicians like Alfred North Whitehead, Henri Bergson, Martin Heidegger, and Gilles Deleuze. The investigation begins with first establishing the methodological difficulties involved in thinking temporality as an ontological feature. In a second step, dynamic ontologies are introduced as the conceptual background best suited to think ontological temporality and their difference to event-ontologies is explained. Finally, the distinction between temporality and linear time is clarified. This introduction of temporality ends with some arguments for the existence of temporality that are inspired by Aristotle’s famous investigations into the nature of time. After having thus introduced temporality as an ontological feature and argued for the existence and relevance of it, its implications for our understanding of the dimensions of time and especially for anticipation are discussed. (shrink)

This article argues that existing approaches to programming ethical AI fail to resolve a serious moral-semantic trilemma, generating interpretations of ethical requirements that are either too semantically strict, too semantically flexible, or overly unpredictable. This paper then illustrates the trilemma utilizing a recently proposed ‘general ethical dilemma analyzer,’ GenEth. Finally, it uses empirical evidence to argue that human beings resolve the semantic trilemma using general cognitive and motivational processes involving ‘mental time-travel,’ whereby we simulate different possible pasts and futures. I (...) demonstrate how mental time-travel psychology leads us to resolve the semantic trilemma through a six-step process of interpersonal negotiation and renegotiation, and then conclude by showing how comparative advantages in processing power would plausibly cause AI to use similar processes to solve the semantic trilemma more reliably than we do, leading AI to make better moral-semantic choices than humans do by our very own lights. (shrink)

Interest in computerised central economic planning (CCEP) has seen a resurgence, as there is strong demand for an alternative vision to modern free (or not so free) market liberal capitalism. Given the close links of CCEP with what we would now broadly call artificial intelligence (AI)—e.g. optimisation, game theory, function approximation, machine learning, automated reasoning—it is reasonable to draw direct analogues and perform an analysis that would help identify what commodities and institutions we should see for a CCEP programme (...) to become successful. Following this analysis, we conclude that a CCEP economy would need to have a very different outlook from current market practices, with a focus on producing basic “interlinking” commodities (e.g. tools, processed materials, instruction videos) that consumers can use as a form of collective R &D. On an institutional level, CCEP should strive for the release of basic commodities that empower consumers by having as many alternative uses as possible, but also making sure that a baseline of basic necessities is widely available. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. (...) John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

With the rapid development of big data, artificial intelligence teaching systems have gradually been developed extensively. The powerful artificial intelligence teaching systems have become a tool for teachers and students to learn independently in various universities. The characteristic of artificial intelligence teaching system is to get rid of the constraints of traditional teaching time and space and build a brand-new learning environment, which is the mainstream trend of future learning. As the carrier of students’ autonomous learning, the (...) artificial intelligence teaching system provides a wealth of learning resources and learning tools on the one hand, and on the other hand, it gradually accumulates more and more learning behaviors, learning status, and other large amounts of data, which is an in-depth study of online learning and provides valuable and generative dynamic resources. Based on relevant researches on domestic and foreign related learning analysis and common big data analysis methods, combined with actual learning evaluation goals, this paper proposes an artificial intelligence teaching system using big data analysis methods and a modeling process framework for online learning evaluation and uses student data to carry out predictive evaluation modeling to evaluate student learning outcomes. The evaluation results can enable teachers to predict whether students can successfully complete the course of learning after a period of teaching. Through the final evaluation, students’ learning problems can be discovered in time based on the evaluation results, and targeted interventions can be made for students who are at risk. The scientific and objective learning evaluation obtained in this study through data analysis can not only provide teachers with relevant information and provide personalized guidance to students, but also improve the adaptive and personalized service functions of the learning platform of the artificial intelligence teaching system, greatly reducing teachers teaching burden. Artificial intelligence teaching evaluation can help educators understand the problems in teaching, adjust teaching strategies in time, and improve teaching results. (shrink)

The topic of temporal meaning in texts has received considerable attention in recent years from scholars in linguistics, logical semantics, cognitive science, and artificial intelligence. Representing Time in Natural Language offers a systematic and detailed account of how we use temporal information contained in a text or in discourse to reason about the flow of time, inferring the order in which events happened when this is not explicitly stated. A new representational system is designed to formalize an appropriately context-dependent (...) notion of situated inference. The Dynamic Aspect Tree, representing temporal dependencies, constitutes a novel and important dynamic temporal logic, one that makes it easy to see "what follows when" from the information given in an ordinary English text. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:The Eleatic BergsonDonna Jones (bio)Suzanne Guerlac. THINKING IN TIME: AN INTRODUCTION TO HENRI BERGSON. Ithaca: Cornell UP 2006. [TT]In her Thinking in Time: An Introduction to Henri Bergson Suzanne Guerlac reminds her readers that the metaphysician has indeed been the subject of many hatreds, as the Bergsonist Gilles Deleuze once noted. But from this taut philosophical study one cannot easily make out any possible grounds for enmity; (...) nor were any expressed in the special Bergson issue of Modern Literary Notes edited by Guerlac. In this short review essay I shall voice some of that historic criticism, especially in regard to the social and political implications of his thought. Guerlac’s study may also win its own opponents for its sharp criticisms of Deleuze’s interpretations of Bergson and (in her reading) comically machinic view of life [TT 176–87]. This alone makes the book worth reading; it is also exhaustively researched, and I cannot remember learning as much from the footnotes of any other book.Bergson is presented here as a great metaphysician on the nature of time and space, the philosophical father of nonlinear science, and a theorist of concrete and affective individuality. Rejecting implicitly Leszek Kolakowski’s reading of two Bergsons, Guerlac presents him as neither a cosmologist who opposes life and matter nor an existentialist who retrieves consciousness by opposing time to space. Nor does he emerge as a systematic thinker in this study. In the last chapter Guerlac offers a dizzying defense of Bergson for providing the philosophical basis for cultural studies of the qualitative irreducibility of affectual states, the metaphysics of chaos theory, the critique of artificial intelligence, and the rudiments of an aesthetic theory of digital art and virtual reality.All of this follows from the epochal significance of Bergson’s metaphysical distinction between time and space as carefully grounded in the most advanced physics of his time. Guerlac does not discuss in any detail Bergson’s vitalist critiques of Darwinian biology in Creative Evolution, the work through which the metaphysical arsonist fired the modernist imagination in diverse, contradictory, and at times violent ways.1 Nor does she explore the occult and mystical themes that run throughout Bergson’s work and informed his personal and professional associations.In fact, Guerlac writes: “An explicit appeal to the social values of mystical experience in this study [The Two Sources of Morality and Religion] appeared to vindicate those who had criticized Bergson all along for being simply a mystic. And yet the title of the work, and the basis for the notion of closed and open societies, derive from scientific, not mystical discourse. They refer us to an opposition between closed and open systems in Sadi Carnot’s theories of thermodynamics” [TT 9]. Yet Bergson bases the opposition [End Page 21] between closed and open societies not in thermodynamics but in sociobiology. Having sociobiological reasons to believe in the power of the myths of closed morality on the human mind—groups that had instilled loyalty through myth and ritual had been more successful in the course of evolutionary history; that is, humans susceptible to myth have been successful in a positivist Darwinian sense—he called for the cooptation of such susceptibility by those myths and rituals that instilled loyalty to humanity as such. Humanity could dynamically extend the scope of sociability rather than follow the roles and customs that maintained a closed society in static equilibrium. Though Bergson would later courageously risk pneumonia to stand as an old man in the cold rain to register as a Jew in Vichy France, he claimed in this last book from 1932 that the only complete inspiration for universal openness could be found in Christian mysticism and mythology [Two Sources 227]. He explicitly criticized the insularity of the Judaic religion (and caricatured Eastern forms of mysticism). In other words, Bergson put his faith in Christian mystics alone and thus invited the charge of having laid the foundation for mystical and intolerant leadership.Bergson’s universal humanism also crashed on the limits of colonial racism. In considering closed primitive societies, Bergson called attention to missionary stories full of detailed accounts of childish and monstrous deeds. He implored his... (shrink)

Artificial Intelligence and Philosophy. Artificial Intelligence can be considered as the so far last attempt to decode the anthropological comparison between human beings and machines. Thereby it also represents in a prominent way what can be called "systemic thought". Searle's conclusive argument against strong AI (that is the idea of computers having intention in a literal way) refers to his precise distinction between syntax and semantics. This difference obviously opposing some of Searle's other essential ideas will only convince (...) if it also explains the genetic-pragmatic aspect. A theory explaining the "life of mind" and the possibility of understanding needs to combine representation and intention with the subjective causation of signs. At the same time they have to be contextualized within a model of teleologically interpreted life recognized with the help of self-experience and self-reflection. This suggests that AI is a simulation which wrongly believes to be a real duplication. Actually it is a semiotic reduction (syntax and semantic surface of signs only) and a psychological compensation (Turing test) connected with a genetic or abductive fallacy. The biological decontextualization, in fact the elimination of teleology and intention, the deconstruction of subjectivity, the loss of the genetic-pragmatic dimension and the abductive fallacy induce the strong AI to confuse its surface-illusion of simulated understanding with the real process itself. (shrink)

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

Recent work in artificial intelligence has increasingly turned to argumentation as a rich, interdisciplinary area of research that can provide new methods related to evidence and reasoning in the area of law. Douglas Walton provides an introduction to basic concepts, tools and methods in argumentation theory and artificial intelligence as applied to the analysis and evaluation of witness testimony. He shows how witness testimony is by its nature inherently fallible and sometimes subject to disastrous failures. At the same (...) time such testimony can provide evidence that is not only necessary but inherently reasonable for logically guiding legal experts to accept or reject a claim. Walton shows how to overcome the traditional disdain for witness testimony as a type of evidence shown by logical positivists, and the views of trial sceptics who doubt that trial rules deal with witness testimony in a way that yields a rational decision-making process. (shrink)

It is expedient, timely and even urgent to question again Whitehead's approach to education and learning. It is expedient, because education remains (one of) the most important cultural factors. It is timely, because of the need to investigate the efficacy of the process philosophical approach in the context of the growing influence of artificial intelligence. It is urgent, because artificial intelligence gains traction in the context of a global systemic crisis."--Page 4 of cover.

The aim of this essay is to elaborate two distinctive but interconnected modes of temporalization through common points in the work of Jaques Derrida and Jaques Lacan, namely the Subalternated and the Dislocated time. The explication of the different form of time structure reveals time as relational phenomena, which is constituted by the non-relation between the subject and the Other. The process of subjectivization as the formalization of the Real, or as the work of mourning, excludes traumatic present and opens (...) the space for free flow of the Subalternated time, which is marked by linearity, identitarian thought, subordination, causality ect. The present is, therefore, marked by ontologization of the posthumous remains, canonized, excluded and deprived of any subversive potential. But despite this eternal requiem, something remains between the future and the past, non-present in its spectral subsistence which disrupts smooth stream of the Subalternated narrativity, announcing arrival, return and temporarily inexhaustible artificiality of novelty in the form of the Dislocated time. (shrink)

Accountability is a cornerstone of the governance of artificial intelligence (AI). However, it is often defined too imprecisely because its multifaceted nature and the sociotechnical structure of AI systems imply a variety of values, practices, and measures to which accountability in AI can refer. We address this lack of clarity by defining accountability in terms of answerability, identifying three conditions of possibility (authority recognition, interrogation, and limitation of power), and an architecture of seven features (context, range, agent, forum, standards, (...) process, and implications). We analyse this architecture through four accountability goals (compliance, report, oversight, and enforcement). We argue that these goals are often complementary and that policy-makers emphasise or prioritise some over others depending on the proactive or reactive use of accountability and the missions of AI governance. (shrink)

Artificial intelligence-based (AI) technologies such as machine learning (ML) systems are playing an increasingly relevant role in medicine and healthcare, bringing about novel ethical and epistemological issues that need to be timely addressed. Even though ethical questions connected to epistemic concerns have been at the center of the debate, it is going unnoticed how epistemic forms of injustice can be ML-induced, specifically in healthcare. I analyze the shortcomings of an ML system currently deployed in the USA to predict patients’ (...) likelihood of opioid addiction and misuse (PDMP algorithmic platforms). Drawing on this analysis, I aim to show that the wrong inflicted on epistemic agents involved in and affected by these systems’ decision-making processes can be captured through the lenses of Miranda Fricker’s account of hermeneutical injustice. I further argue that ML-induced hermeneutical injustice is particularly harmful due to what I define as an automated hermeneutical appropriation from the side of the ML system. The latter occurs if the ML system establishes meanings and shared hermeneutical resources without allowing for human oversight, impairing understanding and communication practices among stakeholders involved in medical decision-making. Furthermore and very much crucially, an automated hermeneutical appropriation can be recognized if physicians are strongly limited in their possibilities to safeguard patients from ML-induced hermeneutical injustice. Overall, my paper should expand the analysis of ethical issues raised by ML systems that are to be considered epistemic in nature, thus contributing to bridging the gap between these two dimensions in the ongoing debate. (shrink)

Among the reasons that Whitehead is such an interesting philosopher is that his work resonates across philosophical traditions. This collection develops connections between Whiteheadian concepts and recent European thinkers. The purpose is not simply to compare, however, but, as editor Jeremy Fackenthal suggests, to develop a Whiteheadian thinking “in tandem” with European philosophers in order to create disruptions or “dislocations” in thought that can engender creative approaches to contemporary problems.One general feature of the book deserves mention at the outset, though (...) I will return to it later in the review: although phenomenology and hermeneutics are each represented in the volume, roughly half of the essays focus on the work of Gilles Deleuze (if we group these together with essays focused on “new materialism,” which is connected with Deleuze, the proportion is more like two-thirds). This will be no surprise, since Deleuze is the European philosopher who was most appreciative of Whitehead. And yet, the result is that the conversation with “continental philosophy” is highly selective.The book is organized into three parts, each comprised of three essays. In part 1, “Technological and Systematic Dislocations,” William S. Hamrick brings Whitehead into conversation with Maurice Merleau-Ponty's concept of “the flesh” to suggest an ecological ontology capable of overcoming the instrumentalization of nature. Hamrick's strategy is to read Whitehead and Merleau-Ponty as contributing to an “ontological foundation” for a sustainable civilization (18). Bo Eberle writes about the “quantified self” suggested in new, wearable self-monitoring technologies. Drawing on Whitehead's notion of causal efficacy, Eberle argues that devices such as Fitbit give us access to bodily states that bypasses the standard ways we are aware of ourselves (i.e., through consciousness). He goes further to argue that, like all ways of knowing, wearable technologies are selective, and that the selection reflects the interests of the manufacturers and promotes a certain kind of subjectivity (in the case of Fitbit, a subjectivity that is oriented toward “getting your steps in”). Whitehead's epistemology thus enables the critic of wearable technologies to understand how contemporary experience and self-understanding is shaped not simply by recent technological mediations but by the material interests embedded in them. J.R. Hustwit and Carl Dyke take on what they call “activist” uses of Whitehead that naively (in their judgment) suggest that a non-relational substance metaphysics is to blame for most of our social ills. They employ the theory of complex adaptive systems in conversation with Whitehead and Antonio Gramsci to show that, in fact, achieving significant social change is far more difficult and even unpredictable than mere insertion of better ideas into public discourse. Dominant hegemonies have ways of adapting and absorbing attempts to undermine them. Here Hustwit and Dyke draw on the philosophy of Paul Ricoeur to suggest that resistance to the dominant hegemony is nevertheless always possible because of the vagueness of discourse, allowing a plurality of competing interpretations and thus fractures in the dominant hegemony. However, even this kind of resistance is impossible to control and its effects incapable of being entirely predicted. To the essay's titular question, “Can Whitehead save the world?” the authors answer, “No.”In part 2, “Animal/Human Dislocations,” Jeremy Fackenthal draws on the new materialism of Rosi Braidotti (and Deleuze) along with Whitehead to argue for a “process-relational” nomadism that culminates in a “becoming-imperceptible.” His reading together of Whitehead and Braidotti creates the opportunity to recast the former's concept of objective immortality in such a way that it does not refer to death so much as to being diffused into a relational network. The satisfaction of an actual occasion may be taken as its translation into its effects among its relations. Fackenthal argues that this becoming-imperceptible makes possible a sustainable presence within a fragile network of life. Deena M. Lin uses Whitehead, Braidotti, and Judith Butler to lay out a vision of human life as precarious and deserving attention, care, and solidarity. She presses this vision as a direct moral assault on US (Trump's) policy toward Syrian refugees. Going further, she draws on Braidotti's posthuman account of zoe to argue for universal connection and solidarity with all life that should foster, as the title of her essay puts it, “Welcoming of Syrian Life.” Tano Posteraro offers an intriguing essay drawing on Jakob von Uexküll's account of animal experience. Posteraro argues that Uexküll's notion of an animal's Umwelt, shaped by its distinctive perceptual apparatus, needs to be supplemented by Whitehead's notion of prehension to account for the active dimension of animal perception. Rather than a passive registering of animal environments, Posteraro argues that the latter are constructed, and that concepts (or conceptual prehensions) should be understood as tools with which such environments are constructed.In part 3, “Time, the World, and Abstraction,” three essays probe ways that Whiteheadian and Deleuzian themes may resonate critically and constructively. Kris Klotz brings the thought of Whitehead and Deleuze together to inquire about how alternatives to present social forms are envisioned. He argues that, for Whitehead, every actual occasion involves a selection of some features of the past, and, further, that every selection is also an abstraction from the concrete welter of influences impinging on it. For Whitehead, then, to envision alternatives involves a critique of abstractions, a reinstatement of the concrete plurality of possibilities. This, for Whitehead, is what rationality does: it works at restoring the concrete, and, with it, a series of alternatives that have been eclipsed. To this Whiteheadian analysis, Klotz adds Deleuze and Guattari's critique of “opinion.” For Deleuze and Guattari, opinion is not an idiosyncrasy but a consensus to which differences of experience are subjected. Philosophy, for Deleuze and Guattari, must always struggle against opinion in order to bring alternatives to light. These conceptions of reason/philosophy, Klotz argues, represent useful ways to critique inequalities and other social wrongs that are embedded in the social forms that are routinely taken for granted. Elijah Prewitt-Davis uses a scene from Buffy the Vampire Slayer, along with philosophical innovations of Foucault, Deleuze, and Whitehead, to offer a distinctive account of power. Prewitt-Davis calls attention to Deleuze's important book on Leibniz, The Fold, and to how Whitehead's concept of prehension lies at its center. The upshot is that power is not reduced to dominance but, instead, is at work wherever any occasion of experience persists amid the onslaught of the influence of other occasions. Power, as Deleuze wrote, is the capacity to be affected—and to decide how to constitute oneself anew. Keith Robinson offers a detailed account of Whitehead's epistemology along with a reading of Deleuze's analysis of temporality to suggest that there is a common trajectory that seeks to undermine what the title of the essay describes as the “Bifurcation of Nature” (i.e., the separation of natural phenomena and human experience) within the dominant Western philosophical tradition.There is much of value in the volume as a whole. Aside from staging what Spinoza might have called good encounters between Whitehead and a number of continental philosophers, enhancing the conceptual power of each, the contributors are able to make a case that these philosophical trajectories, when read together, have much to contribute to our understanding of and response to the pressing issues of the day. In addition, readers who are interested in the relationship between Whitehead and the new materialism will find this volume to be a helpful resource, stocked with fine examples of how to sharpen new materialist thinking with Whitehead and how to read contemporary contexts through these lenses. On the other hand, this book is not a handbook nor a comprehensive reference. The organizational logic behind its division into sections is murky and the amount of attention given to particular continental philosophers as opposed to others appears somewhat arbitrary: there are recent continental philosophers who get little to no attention at (Derrida, Malabou, Žižek, Agamben, Latour, Nancy, for example), several of whom might prove to be quite interesting to read “in tandem” with Whitehead. Perhaps that is just to say that there are many more essays to be written. (shrink)

The tendency to idealise artificial intelligence as independent from human manipulators, combined with the growing ontological entanglement of humans and digital machines, has created an “anthrobotic” horizon, in which data analytics, statistics and probabilities throw our agential power into question. How can we avoid the consequences of a reified definition of intelligence as universal operation becoming imposed upon our destinies? It is here argued that the fantasised autonomy of automated intelligence presents a contradistinctive opportunity for philosophical consciousness to understand (...) itself anew as holistic and co-creative, beyond the recent “analytic” moment of the history of philosophy. Here we introduce the concept of “crealectic intelligence”, a meta-analytic and meta-dialectic aspect of consciousness. Intelligent behaviour may consist in distinguishing discrete familiar parts or reproducible functions in the midst of noise via an analytic process of segmentation; intelligence may also manifest itself in the constitution of larger wholes and dynamic unities through a dialectic process of association or assemblage. But, by contrast, crealectic intelligence co-creates realities in the image of an ideal or truth, taking into account the desiring agent imbued with a sense of possibility, in a relationship not only with the Real but also with the creative sublime or “Creal”. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence and neuroscience. John (...) Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

This paper argues that to negate the ontological difference between the natural and the artificial, is not plausible; nor is the reduction of the natural to the artificial or vice versa possible. Except if one intends to empty the semantic content of the terms and notions: “natural” and “artificial.” Most philosophical discussions on Artificial Intelligence (AI) have always been in relation to the human person, especially as it relates to human intelligence, consciousness and/or mind in general. (...) This paper, intends to broaden the conversation, by discussing AI in relation to the notions of “nature” and the “artificial.” This intention is to more critically understand the artificiality in and of artificial intelligence. To achieve this, the notion of “nature” in Aristotle’s Philosophy of Nature, has been employed as an epistemological tool in interrogating the notion of the artificial and the objectives of the science and technology of Artificial Intelligence. (shrink)

“Creating the future” is a notion introduced by Alfred North Whitehead to define the task of universities and the function of philosophy. Implicitly, it is a rejection of the idea that the future is already determined, and in some sense, already exists, with the appearance of temporal becoming an illusion. “Creation” originally meant “the action of causing to exist”, or “a coming into being”. The “future” is not normally considered to be what can be created. Originally, it meant “yet to (...) be”. It now tends to be defined in relation to time, as “the time to come”, where “time” is assumed to be an independent existent along with space as the “containers” of material beings, with the future in some sense pre-existing its becoming present. The quantity of “matter” or “mass-energy” is assumed to be constant and to change its position and configurations in predictable ways over time. To refer to the future as being created is to reject this view of the universe and the basic concepts that define it, replacing these with concepts that can make intelligible the freedom of and creativity of people, the future as in process of being created, and humans as partially responsible for what future is created. It is to recognize that there are real possibilities that can be envisioned, understood, chosen, and brought into existence, with some process philosophers claiming that new possibilities can also be created. (shrink)

The urban environment is increasingly engaging with artificial intelligence, a focus on the automation of urban processes, whether it be singular artefacts or city-wide systems. The impact of such technological innovation on the social dynamics of the urban environment is an ever changing and multi-faceted field of research. In this paper, the space and time defined by the autonomous vehicle is used as a window to view the way in which a shift in urban transport dynamics can impact the (...) temporal experience of an individual. Using the finite window of time created by an autonomous vehicle, a theoretical framework is put forward that seeks to show how contrasting narratives exist regarding the experience of the window of time within the autonomous vehicle. By taking a theoretical approach informed by social theory, the dissolution of barriers between separate spheres of life is explored to highlight the increased commodification of time. In focusing on both the space and time created by the autonomous vehicle this approach seeks to highlight how artificial intelligence can provide a contemporary space in the urban environment while also opening a new window of time. The cognitive dissonance observed when comparing the narratives of autonomous vehicle stakeholders and the historical shift in time use leads to a belief that technology makes the user more free in terms of time. With this paper the autonomous vehicle is shown to be an ideal space and time to view the way in which the use of such technology does not increase free-time, but further dissolves the boundaries between what is and what is not work-time. (shrink)

The development of the systems of artificial intelligence (AI) and digital transformation in general lead to the formation of multitude of autonomous agents of artificial and mixed genealogy, as well as to complex structures in the information and regulatory environment with many opportunities and pathologies and a growing level of uncertainty in making managerial decisions. The situation is complicated by the continuing plurality of understanding of the essence of AI systems. The modern expanded understanding of AI goes back (...) to ideas formulated more than 100 years ago. In official national policy documents on the development of AI, working definitions of AI are preferred. The current stage of AI systems life cycle can be assessed as the completion of the initial period in the development of systems associated with weak AI. The ability of artificial systems to realize themselves as a separate person becomes one of the serious scientific and practical challenges. Attention to the issues of the ethics of AIS indicates the expansion of the diversity of its forms and the beginning of the work in the field of goal-setting. New moral and ethical problems also arise in connection with the possibility of the creation of genuine conscious subjects in the foreseeable future. There is an increasing phenomenon of degradation of natural intelligence. It is required to take into account the issue of the heterogeneity of data generated by humans, electronic sensors and network devices in the dynamic complex environments of the digital economy, the issue of the complexity of the process of co-evolution of AI systems, collective and individual natural consciousness. A special area of opportunities and risks is the development of neurotechnologies. The object of control is digital twins, through which there can be manipulation of real attitudes, preferences, and behavior of individuals. As a result, there are the development of technological capabilities that provoke destructive phenomena as well as the formation of a new class of mass addictions. (shrink)

Trade in high-value-added toxins for therapeutic and biological use is expanding. These toxins are generally derived from microalgae belonging to the dinoflagellate family. Due to the difficulties to grow these sensitive planktonic species and to the complexity of methods used to synthesize these molecules, which are generally complex chemical structures, biotoxin manufacturers called on artificial intelligence technologies. Manufacturing processes have been greatly improved through the development of specific learning neural networks, applied to each phases of biotoxin production: photo-bioreactors operating (...) at optimal yied; new chemical synthesis research processes; toxin biosynthetic research pathways offering short-cut possibilities. (shrink)

With the emergence of big data, cloud computing, and other technologies, artificial intelligence technology has set off a new wave in the field of education. The application of AI technology to deep learning in university teachers' teaching and students' learning processes is an innovative way to promote the quality of teaching and learning. This study proposed the deep learning-based assessment to measure whether students experienced an improvement in terms of their mastery of knowledge, development of abilities, and emotional experiences. (...) It also used comparative analysis of pre-tests and post-tests through online questionnaires to test the results. The impact of technology on teachers' teaching and students' learning processes, identified the problems in the teaching and learning processes in the context of the application of AI technology, and proposed strategies for reforming and optimizing teaching and learning. It recommends the application of software and platforms, such as Waston and Knewton, under the orientation of AI technology to improve efficiency in teaching and learning, optimize course design, and engage students in deep learning. The contribution of this research is that the teaching and learning processes will be enhanced by the use of intelligent and efficient teaching models on the teachers' side and personalized and in-depth learning on the students' side. On the one hand, the findings are helpful for teachers to better grasp the actual conditions of in-class teaching in real time, carry out intelligent lesson preparations, enrich teaching methods, improve teaching efficiency, and achieve personalized and precision teaching. On the other hand, it also provides a space of intelligent support for students with different traits in terms of learning and effectively improves students' innovation ability, ultimately achieving the purpose of “artificial intelligence + education.”. (shrink)

This paper examines the intersecting of the themes of temporality and truth in Deleuze's philosophy. For the ancients, truth was something eternal: what was true was true in all times and in all places. Temporality (coming to be and passing away) was the realm of the mutable, not the eternal. In the seventeenth century, change began to be seen in a positive light (progress, evolution, and so on), but this change was seen to be possible only because of the immutable (...) laws of nature that govern change. It was not until philosophers such as Bergson, James, Whitehead – and then Deleuze – that time began to be taken seriously on its own account. On the one hand, in Deleuze, time, freed from its subordination to movement, now becomes autonomous: it is the pure form of change (continuous variation) that lies at the basis of Deleuze's metaphysics in Difference and Repetition (and is explored more thematically in The Time-Image). As a result, on the other hand, the false, freed from its subordination to the form of the true, assumes a power of its own (the power of the false), which in turn implies a new ‘analytic of the concept’ that Deleuze develops in What Is Philosophy? (shrink)

The problem of commensurability/incommensurability of different cultural codes is a key problem of modern civilizational development. This is the problem of the search for communicative unity in the world of cultural and biological diversity, which has to be protected, and the search for the cohesion of different Umwelten, of semiotically-defined artificial and natural environments, of ecological and cognitive niches, taking into account that each of them has their own identity and uniqueness. The purpose of the article is to draw (...) attention to the fact that the question of the so-called incommensurability of different conceptual schemes, paradigms, language consciousnesses is widely discussed not only in cross-cultural studies and philosophical problems of translation but also in connection with the problems of incommensurability between the language of classical physics and the language of relativistic quantum physics. Attention is drawn to the problem of the incommensurability and correlation of different languages that are used in debates about the foundations of quantum mechanics, its interpretation, comprehension and ontology. Two approaches stand out in this debate. The first approach is based on the language of the formed being, on the language of things localized in time and on the logic of Aristotle. The second approach is based on the language of the becoming, process and nonlocality, on the search for various processual-oriented temporal logics. In this regard, we discuss the processual approach to understanding quantum mechanics, proposed in the philosophical and physical works of D. Bohm. The authors argue that the experience of constructive understanding of the metaproblems of the interpretation of quantum mechanics, the critical reception of the legacy of such philosophers of the process as Peirce, Bergson and Whitehead, the deep reflection on the problems of commensurability/ incommensurability of linguistic consciousnesses of different cultures – will eventually create a common synergetic-interdisciplinary space of cooperation for the solutions of the above-mentioned issues. (shrink)

The possibility of AI consciousness depends much on the correct answer to the mind–body problem: how our materialistic brain generates subjective consciousness? If a materialistic answer is valid, machine consciousness must be possible, at least in principle, though the actual instantiation of consciousness may still take a very long time. If a non-materialistic one (either mentalist or dualist) is valid, machine consciousness is much less likely, perhaps impossible, as some mental element may also be required. Some recent advances in neurology (...) (despite the separation of the two hemispheres, our brain as a whole is still able to produce only one conscious agent; the negation of the absence of a free will, previously thought to be established by the Libet experiments) and many results of parapsychology (on medium communications, memories of past lives, near-death experiences) suggestive of survival after our biological death, strongly support the non-materialistic position and hence the much lower likelihood of AI consciousness. Instead of being concern about AI turning conscious and machine ethics, and trying to instantiate AI consciousness soon, we should perhaps focus more on making AI less costly and more useful to society. (shrink)

During 2018, as part of a research project funded by the Deviant Practice Grant, artist Bruno Moreschi and digital media researcher Gabriel Pereira worked with the Van Abbemuseum collection (Eindhoven, NL), reading their artworks through commercial image-recognition (computer vision) artificial intelligences from leading tech companies. The main takeaways were: somewhat as expected, AI is constructed through a capitalist and product-focused reading of the world (values that are embedded in this sociotechnical system); and that this process of using AI is (...) an innovative way for doing institutional critique, as AI offers an untrained eye that reveals the inner workings of the art system through its glitches. This paper aims to regard these glitches as potentially revealing of the art system, and even poetic at times. We also look at them as a way of revealing the inherent fallibility of the commercial use of AI and machine learning to catalogue the world: it cannot comprehend other ways of knowing about the world, outside the logic of the algorithm. But, at the same time, due to their “glitchy” capacity to level and reimagine, these faulty readings can also serve as a new way of reading art; a new way for thinking critically about the art image in a moment when visual culture has changed form to hybrids of human–machine cognition and “machine-to-machine seeing”. (shrink)

The article examines the challenges involved in the process of developing artificial ethical agents. The process involves the creators or designing professionals, the procedures to develop an ethical agent and the artificial systems. There are two possibilities available to create artificial ethical agents: (a) programming ethical guidance in the artificial Intelligence (AI)-equipped machines and/or (b) allowing AI-equipped machines to learn ethical decision-making by observing humans. However, it is difficult to fulfil these possibilities due to the subjective (...) nature of ethical decision-making. The challenge related to the developers is that they themselves lack training in ethical skills. The creators who develop an artificial ethical agent should be able to foresee the ethical issues and have knowledge about ethical decision-making to improve the ethical use of AI-equipped machines. The suggestion is that the focus should be on training professionals involved in the process of developing these artificial systems in ethics rather than developing artificial ethical agents and thereby attributing ethical agency to it. (shrink)

In this article, the potential for Artificial Intelligence (AI) to be appraised as an object of beauty is critically examined through the lens of philosophical thought. Tracing beauty’s evolution from Platonic ideals to contemporary interpretations, the analysis contends that AI’s emergence offers a unique illustration of beauty in the modern age. Confronting the challenge of assigning beauty to entities devoid of consciousness or emotional depth, the argument unfolds to suggest that the intricate design of AI’s algorithms and its technological (...) advancements constitute an emergent form of beauty, one inherently tied to human intellectual endeavor. The article proposes that with time, as with historical art forms, the aesthetic qualities of AI may gain broader acknowledgment, implying that technological advancements necessitate an ongoing revision of established beauty norms. The historical evolution of beauty has been traced in details, and an extended literature review has been provided, serving as the foundation for a comprehensive exploration into the potential for AI to be recognized as an object of beauty. The harmony, elegance, and problem-solving capabilities of AI algorithms have been meticulously evaluated, with a comparison made to classical elements of beauty in an effort to contextually situate AI within longstanding aesthetic traditions. Furthermore, the analysis has been extended to consider the emergence of artificial and digital beauty, alongside an investigation into the parallels drawn between divine and artificial intelligence. As part of a deeper inquiry, the possibility of human emotions, such as awe and love, being authentically directed towards AI has been scrutinized. In conclusion, a reflection has been presented on the transformative role of AI in reimagining the conceptual landscape of beauty, prompting readers to thoughtfully engage with an extended review and the philosophical and cultural implications emerging from AI’s evolving influence on the field of aesthetics. (shrink)

To shorten the travel time and improve comfort, the automatic train driving system is considered to replace manual driving. In this article, an automatic control method of computer application data-processing system based on artificial intelligence is proposed. An automatic train operation (ATO) introduced the structure and function of an autopilot system (train), optimized the train running on the target curve, introduced the basic principle of fuzzy generalized predictive control (PC) algorithm, and combined with the characteristics of ATO system design (...) the speed controller based on optimization algorithm, the target curve to make use of the designed controller to track and simulation validation. The experimental outcomes demonstrate that when the train runs to 90 s, the displacement difference reaches about 40 m, which proves that the fuzzy PC has better displacement tracking, punctual arrival, and higher stopping accuracy. (shrink)