From April 11 to April 13, 2024, the 9th annual International Scientific Conference “Transcendental turn in modern philosophy – 9: metaphysics, epistemology, theory of consciousness, cognitive science and artificial intelligence, theology” was held in Moscow. The focus of the conference remains the current and innovative ideas of transcendental cognitive science and artificial intelligence. The seminar also reflected metaphysical, epistemological and theological issues.

Nowadays, new directions for the development of artificial intelligence (AI) have emerged, the task has been set to develop artificial general intelligence (AGI), which is able to go beyond the narrow AI, gain a high degree of autonomy, independently solve problems in different environmental conditions and thus have the ability to perform the functions of natural intelligence. In this regard, important philosophical, theoretical, and methodological questions arise concerning the definition and evaluation of the social significance of new AI (...) achievements, especially regarding the correlation of their socio-humanitarian and technological aspects. It is necessary to overcome a purely technocratic approach, which usually ignores the negative consequences of digitalization, the possible risks and threats of AI development. These difficulties are conditioned by the paradigm gap between the system of concepts used to describe technologies and the systems of concepts specific to socio-humanitarian descriptions and explanations. The so-called ontological paradox arises during digital transformation and the introduction of AI into social systems. An ontological paradox is also an epistemological paradox, since any statement of an ontological type presupposes its epistemological justification. To overcome this gap, V.E. Lepskiy proposes the conception of self-developing polysubjective environments – cybernetics of the third order. This conception allows to create a conceptual “bridge” between two systems of definitions that do not have direct logical connections between them. A productive tool for this can be the information approach, which is widely used to solve such interdisciplinary problems. This makes possible to theoretically correctly connect technological and socio-humanitarian descriptions in a single conceptual structure and to analyze main socio-humanitarian criteria for evaluating digital innovations. The article discusses in detail the main provisions of the information approach and its application for the development of AGI systems, the socio-humanitarian assessment of AI autonomous development, and the resolution of security issues. (shrink)

Cognitive science is, of course, not really a new discipline, but a recognition of a fundamental set of common concerns shared by the disciplines of psychology, computer science, linguistics, economics, epistemology, and the social sciences generally. All of these disciplines are concerned with information processing systems, and all of them are concerned with systems that are adaptive—that are what they are from being ground between the nether millstone of their physiology or hardware, as the case may be, and the (...) upper millstone of a complex environment in which they exist. Systems that are adaptive may equally well be described as “artificial,” for as environments change, they can be expected to change too, as though they were deliberately designed to fit those environments (as indeed they sometimes are).The task of empirical science is to discover and verify invariants in the phenomena under study. The artificiality of information processing systems creates a subtle problem in defining empirical invariants in such systems. For observed regularities are very likely invariant only within a limited range of variation in their environments, and any accurate statement of the laws of such systems must contain reference to their relativity to environmental features. It is a common experience in experimental psychology, for example, to discover that we are studying sociology—the effects of the past histories of our subjects—when we think we are studying physiology—the effects of properties of the human nervous system. Similarly, business cycle economists are only now becoming aware of the extent to which the parameters of the system they are studying are dependent on the experiences of a population with economic events over the previous generation.In artificial sciences, the descriptive and the normative are never far apart. Thus, in economics, the “principle of rationality” is sometimes asserted as a descriptive invariant, sometimes as advice to decision makers. Similarly, in psychology, the processes of adaptation (learning) have always been a central topic, at one time a topic that dominated the whole field of research. Linguistics, too, has suffered its confusions between descriptive and normative attitudes towards its subject. But we must avoid the error, in studying information processing systems, of thinking that the adaptive processes themselves must be invariant; and we must be prepared to face the complexities of regression in the possibility that they themselves may be subject to improvement and adaptation.It might have been necessary a decade ago to argue for the commonality of the information processes that are employed by such disparate systems as computers and human nervous systems. The evidence for that commonality is now overwhelming, and the remaining questions about the boundaries of cognitive science have more to do with whether there also exist nontrivial commonalities with information processing in genetic systems than with whether men and machines both think. (shrink)

In this essay, I discuss two familiar objections to Hume's account of cognition, focusing on his ability to give a satisfactory account of the more normative dimensions of thought and language use. In doing so, I argue that Hume’s implicit account of these issues is far richer than is normally assumed. In particular, I show that Hume’s account of convention-driven artificial virtues like justice also applies to the proper use of conventional public languages. I then use this connection between (...) Hume’s conception of language and his moral theory to show how he can respond to a number of basic objections to his views. In doing so, I explore the sense in which human cognition is essentially linguistic, and so social, for Hume, as well as many other issues concerning the relationship between Hume’s philosophy of mind and language, his epistemology, and his ethics. (shrink)

The term ‘the artificial’ can only be given a precise meaning in the context of the evolution of computational technology and this in turn can only be fully understood within a cultural setting that includes an epistemological perspective. The argument is illustrated in two case studies from the history of computational machinery: the first calculating machines and the first programmable computers. In the early years of electronic computers, the dominant form of computing was data processing which was a reflection of (...) the dominant philosophy of logical positivism. By contrast, artificial intelligence (AI) adopted an anti-positivist position which left it marginalised until the 1980s when two camps emerged: technical AI which reverted to positivism, and strong AI which reified intelligence. Strong AI's commitment to the computer as a symbol processing machine and its use of models links it to late-modernism. The more directly experiential Virtual Reality (VR) more closely reflects the contemporary cultural climate of postmodernism. It is VR, rather than AI, that is more likely to form the basis of a culture of the artificial. (shrink)

Contrary to the assumptions of empiricist philosophies of science, the theory-laden character of data will not imply the inherent failure (subjectivity, circularity, or rationalization) of instruments to expose nature's secrets. The success of instruments is credited to scientists' capacity to create artificial technological analogs to familiar physical systems. The design of absorption spectrometers illustrates the point: Progress in designing many modern instruments is generated by analogically projecting theoretical insights from known physical systems to unknown terrain. An experimental realism is (...) defended. (shrink)

Those who claim, whether with fear or with hope, that algorithmic governance can control politics or the whole political process or that artificial intelligence is capable of taking charge of or wrecking democracy, recognize that this is not yet possible with our current technological capabilities but that it could come about in the future if we had better quality data or more powerful computational tools. Those who fear or desire this algorithmic suppression of democracy assume that something similar will (...) be possible someday and that it is only a question of technological progress. If that were the case, no limits would be insurmountable on principle. I want to challenge that conception with a limit that is less normative than epistemological; there are things that artificial intelligence cannot do, because it is unable to do them, not because it should not do them, and this is particularly apparent in politics, which is a peculiar decision-making realm. Machines and people take decisions in a very different fashion. Human beings are particularly gifted at one type of situation and very clumsy in others. The part of politics that is, strictly speaking, political is where this contrast and our greatest aptitude are most apparent. If that is the case, as I believe, then the possibility that democracy will one day be taken over by artificial intelligence is, as a fear or as a desire, manifestly exaggerated. The corresponding counterpart to this is: if the fear that democracy could disappear at the hands of artificial intelligence is not realistic, then we should not expect exorbitant benefits from it either. For epistemic reasons that I will explain, it does not seem likely that artificial intelligence is capable of taking over political logic. (shrink)

The results of Cubitt et al. on the spectral gap problem add a new chapter to the issue of undecidability in physics, as they show that it is impossible to decide whether the Hamiltonian of a quantum many-body system is gapped or gapless. This implies, amongst other things, that a reductionist viewpoint would be untenable. In this paper, we examine their proof and a few philosophical implications, in particular ones regarding models and limitative results. In more detail, we examine the (...) way these theorems model many-body quantum systems, and we question what, if anything, is the physical counterpart of the models used by Cubitt et al. We argue that these models are non-representational and that, even if they are so artificial that it is hard to imagine a physical system arising from them, they nonetheless offer an opportunity to learn about the world and the relation between mathematics and reality. On this basis, we draw the conclusion that their results do not undermine the reductionist viewpoint in a strong sense but leave the question open in a weak sense. (shrink)

I defend two theses here. First, I argue that at least many of the commitments that Wittgenstein identifies as “hinge commitments” are plausibly what cognitive psychology and artificial intelligence call “procedural knowledge.” Procedural knowledge can be implemented in cognitive systems in a variety of ways, and these modes of implementation, I argue, predict several properties of Wittgensteinian hinge commitments, including their functional profile, as well as other of their characteristic features. Second, I argue that thinking of hinge commitments as (...) a kind of procedural knowledge allows a unified virtue-theoretic treatment of the generation of knowledge, the transmission of knowledge, and Wittgensteinian “hinge knowledge.” This last thesis is noteworthy, in that Wittgenstein and his defenders have so far failed to offer any unified epistemology of hinge commitments and the knowledge that such commitments are supposed to make possible. (shrink)

Unlike natural agents, artificial agents are, to varying extent, designed according to sets of principles or assumptions. We argue that the designers philosophical position on truth, belief and knowledge has far reaching implications for the design and performance of the resulting agents. Of the many sources of design information and background we believe philosophical theories are under-rated as valuable influences on the design process. To explore this idea we have implemented some computer-based agents with their control algorithms inspired by (...) two strongly contrasting philosophical positions. A series of experiments on these agents shows that, despite having common tasks and goals, the behaviour of the agents is markedly different and this can be attributed to their individual approaches to belief and knowledge. We discuss these findings and their support for the view that epistemological theories have a particular relevance for artificial agent design. (shrink)

Reuben Hersh confided to us that, about forty years ago, the late Paul Cohen predicted to him that at some unspecified point in the future, mathematicians would be replaced by computers. Rather than focus on computers replacing mathematicians, however, our aim is to consider the (im)possibility of human mathematicians being joined by “artificial mathematicians” in the proving practice—not just as a method of inquiry but as a fellow inquirer.

Artificial intelligence has often been seen as an attempt to reduce the natural mind to informational processes and, consequently, to naturalize philosophy. The many criticisms that were addressed to the so-called “old-fashioned AI” do not concern this attempt itself, but the methods it used, especially the reduction of the mind to a symbolic level of abstraction, which has often appeared to be inadequate to capture the richness of our mental activity. As a consequence, there were many efforts to evacuate (...) the semantical models in favor of elementary physiological mechanisms simulated by information processes. However, these views, and the subsequent criticisms against artificial intelligence that they contain, miss the very nature of artificial intelligence, which is not reducible to a “science of the nature”, but which directly impacts our culture. More precisely, they lead to evacuate the role of the semantic information. In other words, they tend to throw the baby out with the bath-water. This paper tries to revisit the epistemology of artificial intelligence in the light of the opposition between the “sciences of nature” and the “sciences of culture”, which has been introduced by German neo-Kantian philosophers. It then shows how this epistemological view opens on the many contemporary applications of artificial intelligence that have already transformed—and will continue to transform—all our cultural activities and our world. Lastly, it places those perspectives in the context of the philosophy of information and more particularly it emphasizes the role played by the notions of context and level of abstraction in artificial intelligence. (shrink)

In place of the traditional epistemological view of knowledge as justified true belief we argue that artificial intelligence and law needs an evidence-based epistemology according to which scientific knowledge is based on critical analysis of evidence using argumentation. This new epistemology of scientific evidence (ESE) models scientific knowledge as achieved through a process of marshaling evidence in a scientific inquiry that results in a convergence of scientific theories and research results. We show how a dialogue interface of (...) argument from expert opinion, along with its set of critical questions, provides the argumentation component of the ESE. It enables internal scientific knowledge to be translated over into a wider arena in which individual nonexpert citizens and groups can make use of it. The external component shows how evidence is presented and used in a legal procedural setting that includes fact-finding, weighing the credibility of expert witnesses, and critical questioning of arguments. The paper critically reviews the standards of admissibility of scientific evidence using the ESE. (shrink)

The rapid advancement of artificial intelligence (AI) has led to renewed ambitions of developing artificial general intelligence. Alongside this has been a resurgence in the development of virtual and augmented reality (V/AR) technologies, which are viewed as “disruptive” technologies and the computing platforms of the future. V/AR effectively bring the digital world of machines, robots, and artificial agents to our senses while entailing the transposition of human activity and presence into the digital world of artificial agents (...) and machine forms of intelligence. The intersection of humans and machines in this shared space brings humans and machines into ontological continuity as informational entities in a totalizing informational environment, which subsumes both cyber and physical space in an artificially constructed virtual world. The reconstruction of mind (through AI) and world (through V/AR) thus has significant epistemological, ontological, and anthropological implications, which constitute the underlying features in the artificialization of mind and world. (shrink)

A situated agent is one which operates within an environment. In most cases, the environment in which the agent exists will be more complex than the agent itself. This means that an agent, human or artificial, which wishes to carry out non-trivial operations in its environment must use techniques which allow an unbounded world to be represented within a cognitively bounded agent. We present a brief description of some important theories within the fields of epistemology and metaphysics. We (...) then discuss ways in which philosophical problems of scepticism are related to the problems faced by knowledge representation. We suggest that some of the methods that philosophers have developed to address the problems of epistemology may be relevant to the problems of representing knowledge within artificial agents. (shrink)

Problem solving has recently become a central topic both in the philosophy of science and in cognitive science. This paper integrates approaches to problem solving from these two disciplines and discusses the epistemological consequences of such an integration. The paper first analyzes problem solving as getting a true answer to a question. It then explores some stages of cognitive activity relevant to question answering that have been delineated by historians and philosophers of science and by cognitive psychologists and artificial (...) intelligencers. The traditional opposition between discovery and justification is challenged. It is suggested that epistemology may be conceptualized, in part, as the critical assessment of problem-solving strategies. (shrink)

The epistemology of computer simulations has become a mainstream topic in the philosophy of technology. Within this large area, significant differences hold between the various types of models and simulation technologies. Agent-based and multi-agent systems simulations introduce a specific constraint on the types of agents and systems modelled. We argue that such difference is crucial and that simulation for the artificial sciences requires the formulation of its own specific epistemological principles. We present a minimally committed epistemology which (...) relies on the methodological principles of the Philosophy of Information and requires weak assumptions on the usability of the simulation and the controllability of the model. We use these principles to provide a new definition of simulation for the context of interest. (shrink)

By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body rupture (...) that has governed the production of scientific thought and the proliferation of approaches. In addition, by highlighting the sociohistorical conditions of AI, this paper investigates the historical characteristics of the shift of the crisis from psychology to AI. Additionally, we examine the epistemological and methodological roots of the main challenges encountered in AI research by noting that empiricism is the dominant tendency in the field. Empiricism gives rise to methodological and practical challenges, including challenges related to the emergence of meaning, abstraction, generalization, the emergence of symbols, concept formation, functional reflection of reality, and the emergence of higher psychological functions. Furthermore, through discussing attempts to formalize dialectical logic, the paper, based on contradiction formation, proposes a qualitative epistemological, methodological, and formal alternative by using a preliminary algorithmic model that grasps the formation of meaning as an essential ability for the qualitative reflection of reality and the emergence of other mental functions. (shrink)

What marks ours as the "post-epistemic era" is that it refuses to confer any special privilege on knowledge production as a social practice: whatever normative strictures apply to social practices in general, they apply specifically to epistemic practices as well. I trace how we have reached this state by distinguishing two conceptions of normativity in the history of epistemology: a top-down approach epitomized by Kant and Bentham, and a bottom-up approach associated with the Scottish Enlightenment. The advantage of the (...) latter is that it clearly distinguishes the emergence of norms from the conditions of their maintenance. I then show how more recent evolutionary epistemology has, in a pejorative way, "naturalized" socially constructed norms of cognitive competence, whereas the logical positivists — long the bane of "progressive" epistemologists — recognized the fully artificial character of epistemic norms and hence qualify as the first social epistemologists. (shrink)

In this paper I critically examine the notion of explanation used in Artificial Intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. (...) At the end of the paper I outline the desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation. (shrink)

This essay argues that Huey Newton’s philosophical explanation of US empire fills an epistemological gap in our thinking that provides us with a basis for understanding the emergence and operational application of predictive policing, Big Data, cutting-edge surveillance programs, and semi-autonomous weapons by US military and policing apparati to maintain control over racialized populations historically and in the (still ongoing) Global War on Terror today – a phenomenon that Black Studies scholars and Black philosophers alike have yet to demonstrate the (...) conceptual acumen to explain. Newton’s ability to explain the conceptual foundation of US empire alongside his prediction of the emergence and application of AI and other Big Data technologies towards the repression of dissidents, criminals, terrorists, and other racialized groups abroad gives intercommunalism the explanatory power to lay the basis for a genealogical account of Black repression that also advances our understanding of how the US as a liberal democratic regime engages in enemy target acquisition (the construction of racialized groups as threats internally and externally) to rationalize the brutal repression of these populations by military-police agencies whose operational application of new AI and computing-surveillance technologies and semi-autonomous weapons are conditioned by the intensity of their racialized dehumanization of the enemy. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:Philosophy and Literature 25.2 (2001) 314-334 [Access article in PDF] The Epistemology of Cognitive Literary Studies F. Elizabeth Hart I Literary scholars have begun incorporating the insights of cognitive science into literary studies, bringing to bear on questions of literary experience the results of explorations within a wide range of fields that define today's cognitive science. The investigation of the human mind and its reasoning processes encompasses a (...) rich variety of empirical and speculative disciplines, including cognitive psychology, neuroscience, linguistics, evolutionary psychology, developmental psychology, computer science, philosophy, and anthropology. These disciplines are all related to one another by the questions they ask about the brain and mind and the ground they cover in staking out their research agendas; but each also contributes uniquely in terms of individual scope and focus. Literary critics, eavesdropping on this maturing coalition, are now producing critical works that apply cognitive research to a similarly impressive range of literary concerns, with results so far that seem to span the gap between traditional and more contemporary literary critical approaches.All such criticism is dependent on scientific studies of the brain and mind and so accepts axiomatically some degree of epistemological efficacy in scientific empiricism. But this acceptance has not been naive or uncritical and has, in fact, been at times carefully qualified by critics' sensitivity to debate--to the lack of consensus among cognitive scientists, and to the fact that cognitive science is itself in its infancy stage, with its own practitioners often openly tentative about their results and descriptive capabilities. As it happens, such debate, in recent years, has even proven beneficial for literary scholarship since an important [End Page 314] paradigm shift has taken place that now makes some aspects of cognitive science more compatible than ever with the interests of literary studies. This is because, whereas an earlier phase of cognitive science focused its energies on Artificial Intelligence and on theories of language and psychology that supported AI, grounding its theories implicitly in a metaphor of the mind as a computer, today's cognitive scientists tend to concentrate instead on the more organic metaphor of the "embodiment" of mind, that is, of the mind's substantive indebtedness to its bodily, social, and cultural contexts, and on the figurative phenomena of metaphor, metonymy, image schemata, "fields," "frames," other "integrative mental spaces" (as they are called), and the gradient structures and prototypical bases of semantic categories, all of which contribute to recasting human reason into a set of highly imaginative--not logical but figural--processes. 1 For literary theory and criticism, this shift in cognitive science toward "the body in the mind" 2 --tantamount to a wholesale rejection of philosophical rationalism--has given rise to a new kind of interdisciplinary practice, to the potential for a sincere engagement between diverse sciences and even between literature and science that stretches all the bounds of what literary scholars generally mean when they speak of being interdisciplinary (e.g., exchanges with history, art history, anthropology, film studies, sociology).In the discussion that follows, I would like to explore this new interdisciplinarity in literary studies by examining its epistemological implications. I begin with a description of the kinds of criticism now being produced under the rubric of cognitive literary criticism or--to reflect the field's scope more accurately--cognitive and cognitive-evolutionary criticism. This description is not intended as a comprehensive or evaluative survey, since in-depth review articles of extant works are available elsewhere. 3 Rather, I hope to familiarize my readers with the basic orientations of this theory and criticism so that they can grasp the epistemological relations that will become the essay's focus. To lay a further groundwork for this focus, I will review various uses of the term "cognitive" (or "cognitivism") in the scholarship and offer an analysis of its meanings, first in cognitive science and then in literary theory. Building, then, on what "cognitive" might signify for literary studies, I show how a literary approach based on today's cognitive science is uniquely situated among other approaches because of its underlying theory of human... (shrink)

The UK Government’s Code of Conduct for data-driven health and care technologies, specifically artificial intelligence -driven technologies, comprises 10 principles that outline a gold-standard of ethical conduct for AI developers and implementers within the National Health Service. Considering the importance of trust in medicine, in this essay I aim to evaluate the conceptualisation of trust within this piece of ethical governance. I examine the Code of Conduct, specifically Principle 7, and extract two positions: a principle of rationally justified trust (...) that posits trust should be made on sound epistemological bases and a principle of value-based trust that views trust in an all-things-considered manner. I argue rationally justified trust is largely infeasible in trusting AI due to AI’s complexity and inexplicability. Contrarily, I show how value-based trust is more feasible as it is intuitively used by individuals. Furthermore, it better complies with Principle 1. I therefore conclude this essay by suggesting the Code of Conduct to hold the principle of value-based trust more explicitly. (shrink)

The need for fair and just AI is often related to the possibility of understanding AI itself, in other words, of turning an opaque box into a glass box, as inspectable as possible. Transparency and explainability, however, pertain to the technical domain and to philosophy of science, thus leaving the ethics and epistemology of AI largely disconnected. To remedy this, we propose an integrated approach premised on the idea that a glass-box epistemology should explicitly consider how to incorporate (...) values and other normative considerations, such as intersectoral vulnerabilities, at critical stages of the whole process from design and implementation to use and assessment. To connect ethics and epistemology of AI, we perform a double shift of focus. First, we move from trusting the output of an AI system to trusting the process that leads to the outcome. Second, we move from expert assessment to more inclusive assessment strategies, aiming to facilitate expert and non-expert assessment. Together, these two moves yield a framework usable for experts and non-experts when they inquire into relevant epistemological and ethical aspects of AI systems. We dub our framework ‘epistemology-cum-ethics’ to signal the equal importance of both aspects. We develop it from the vantage point of the designers: how to create the conditions to internalize values into the whole process of design, implementation, use, and assessment of an AI system, in which values (epistemic and non-epistemic) are explicitly considered at each stage and inspectable by every salient actor involved at any moment. (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)

The high predictive accuracy of contemporary machine learning-based AI systems has led some scholars to argue that, in certain cases, we should grant them epistemic expertise and authority over humans. This approach suggests that humans would have the epistemic obligation of relying on the predictions of a highly accurate AI system. Contrary to this view, in this work we claim that it is not possible to endow AI systems with a genuine account of epistemic expertise. In fact, relying on accounts (...) of expertise and authority from virtue epistemology, we show that epistemic expertise requires a relation with understanding that AI systems do not satisfy and intellectual abilities that these systems do not manifest. Further, following the Distribution Cognition theory and adapting an account by Croce on the virtues of collective epistemic agents to the case of human-AI interactions we show that, if an AI system is successfully appropriated by a human agent, a _hybrid_ epistemic agent emerges, which can become both an epistemic expert and an authority. Consequently, we claim that the aforementioned hybrid agent is the appropriate object of a discourse around trust in AI and the epistemic obligations that stem from its epistemic superiority. (shrink)

In this paper we want to analyze some philosophical and epistemological connections between a new kind of technology recently developed within robotics, and the previous mechanical approach. A new paradigm about machine-design in robotics, currently defined as ‘Embodied Intelligence’, has recently been developed. Here we consider the debate on the relationship between the hand and the intellect, from the perspective of the history of philosophy, aiming at providing a more suitable understanding of this paradigm. The new bottom-up approach to design (...) is deeply rooted in a new kind of empiricism, which tries to overcome issues connected with the previous approach strongly committed with the Artificial Intelligence (AI) debate and its origin. Since Turing’s time, the AI debate showed a rationalistic bias which remained undisputed until now. The paradigm shift we are witnessing nowadays is a reply to that bias in order to achieve not only a better way to design robots, but also to understand some underlying epistemological remarks. (shrink)

Describing resources and their relationships with organisms seems to be a useful approach to a ‘unified ecology’, contributing to fill the gap between natural and human oriented processes, and opening new perspectives in dealing with biological complexity. This Resource Criterion defines the main properties of resources, describes the mechanisms that link them to individual species, and gives a particular emphasis to the biosemiotic approach that allows resources to be identified inside a heterogeneous ecological medium adopting the eco-field model. In particular, (...) this Criterion allows to couple matter, structured energy and information composing the ecological systems to the biosemiotic and cognitive mechanisms adopted by individual species to track resources, transforming neutral surroundings into meaningful species-specific Umwelten. The expansion of the human semiotic niche that is a relevant evolutionary process of the present time, assigns the role of powerful and efficient agency to the Resources Criterion to evaluate the effect of human intrusion into the natural systems with habits of key stone species, under the challenge of a growing use of alloctonous, immaterial and symbolic resources of the actual globalized societal models. The Resource Criterion interprets the ecological dynamics contributing to complete the epistemology of the ecology, to open a bridge toward economy and other societal sciences, and to contribute to formulate a General Theory of Resources. (shrink)

This paper argues that AI follows classical versions of epistemology in assuming that the identity of the knowing subject is not important. In other words this serves to `delete the subject''. This disguises an implicit hierarchy of knowers involved in the representation of knowledge in AI which privileges the perspective of those who design and build the systems over alternative perspectives. The privileged position reflects Western, professional masculinity. Alternative perspectives, denied a voice, belong to less powerful groups including women. (...) Feminist epistemology can be used to approach this from new directions, in particular, to show how women''s knowledge may be left out of consideration by AI''s focus on masculine subjects. The paper uncovers the tacitly assumed Western professional male subjects in two flagship AI systems, Cyc and Soar. (shrink)

The article intends to show the significance of Ch. Peirce’s ideas for the development of contemporary philosophy, to find out the main directions of his criticism of the principles of Cartesian and more broadly modern philosophy (where it comes from Descartes) and to consider the positive program of his philosophy, which he offers as an alternative to Modern philosophy. Peirce starts from a pragmatic and semiotic approach to human nature, consciousness and cognition. Thanks to this approach, he managed to undermine (...) the key ideas of the modern understanding of cognition, human, language, namely: individualism, linguistic atomism, dogmatism, dualism, representationism, nominalism, foundationalism, etc. And, instead, to propose a new theory of knowledge, a new ontology and anthropology. Peirce asserts the indirectness of human cognition by signs (linguistic, cultural, sensory), the impossibility of introspection and intuition, advocates holism, fallibilism, pragmatism, realism, synechism, inferentialism and offers a new understanding of the personality — all this makes him a relevant and contemporary thinker. Peirce’s ideas offer a fresh perspective on modern scientific practice. Peirce’s rethinking of the foundations of knowledge puts him alongside many thinkers of the 20th and 21st centuries. The development of his ideas can be seen in modern anthropology, epistemology, research on artificial intelligence, semiotics, etc. The article is divided into four subsections, which are devoted to the main directions of Peirce’s criticism of Descartes’ philosophy: in the first — against Descartes’ skeptical methodology, Peirce puts the concept of fallibilism and the principle of belief, in the second — Peirce denies the possibility of intuition and proves the indirectness of our cognition by signs, in the third — it is about the advantages of the semiotic and communicative approach over individualism and linguistic atomism, in the fourth — Descartes’ nominalism and dualism, Peirce contrasts the realist approach and his theory of synechism, as well as semiotic anthropology. (shrink)

ABSTRACT I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments. INTRODUCTION In contrast to the classic model of randomized-control trials, often with a large number of (...) subjects enrolled, precision medicine attempts to optimize therapeutic outcomes by focusing on the individual.[i] A recent publication highlights the strengths and weakness of both traditional evidence-based medicine and precision medicine.[ii] Plus, it outlines a tension in the shift from evidence-based medicine’s inductive reasoning style (the collection of data to postulate general theories) to precision medicine’s abductive reasoning style (the generation of an idea from the limited data available).[iii] The paper’s main example is the application of precision medicine for the treatment of cancer.[iv] I argue that this examination and appreciation for the shift to abductive reasoning should be extended to the intersection of neuroscience and novel brain-computer interfaces too. As the name suggests, brain-computer interfaces are a significant advancement in neurotechnology that directly connects someone’s brain to external or implanted devices.[v] Among the various kinds of brain-computer interfaces, adaptive deep brain stimulation devices require numerous personalized adjustments to their settings during the implantation and computation stages in order to provide adequate relief to patients with treatment-resistant disorders. What makes these devices unique is how adaptive deep brain stimulation integrates a sensory component to initiate the stimulation. While not commonly at the level of sophistication as self-supervising or generative large language models,[vi] they currently allow for a semi-autonomous form of neuromodulation. This paper highlights the implications of applying abductive reasoning to personalized implantable neurotechnologies. Then, it explores whether abductive reasoning is sufficient to justify insurance coverage for devices absent widespread clinical trials, which are better applied to one-size-fits-all treatments.[vii] ANALYSIS I. The State of Precision Medicine in Oncology and the Epistemological Shift While a thorough overview of precision medicine for the treatment of cancer is beyond the scope of this article, its practice can be roughly summarized as identifying clinically significant characteristics a patient possesses (e.g., genetic traits) to land on a specialized treatment option that, theoretically, should benefit the patient the most.[viii] However, in such a practice of stratification patients fall into smaller and smaller populations and the quality of evidence that can be applied to anyone outside these decreases in turn.[ix] As inductive logic helps to articulate, the greater the number of patients that respond to a particular therapy the higher the probability of its efficacy. By straying from this logical framework, precision medicine opens the treatment of cancer to more uncertainty about the validity of these approaches to the resulting disease subcategories.[x] Thus, while contemporary medical practices explicitly describe some treatments as “personalized”, they ought not be viewed as inherently better founded than other therapies.[xi] A relevant contemporary case of precision medicine out of Norway focuses on the care of a patient with cancer between the ventricles of the heart and esophagus, which had failed to respond to the standard regimen of therapies over four years.[xii] In a last-ditch effort, the patient elected to pay out-of-pocket for an experimental immunotherapy (nivolumab) at a private hospital. He experienced marked improvements and a reduction in the size of the tumor. Understandably, the patient tried to pursue further rounds of nivolumab at a public hospital. However, the hospital initially declined to pay for it given the “lack of evidence from randomised clinical trials for this drug relating to this [patient’s] condition.”[xiii] In rebuttal to this claim, the patient countered that he was actually similar to a subpopulation of patients who responded in “open‐label, single arm, phase 2 studies on another immune therapy drug” (pembrolizumab).[xiv] Given this interpretation of the prior studies and the patient’s response, further rounds of nivolumab were approved. Had the patient not had improvements in the tumor’s size following a round of nivolumab, then pembrolizumab’s prior empirical evidence in isolation would have been insufficient, inductively speaking, to justify his continued use of nivolumab.[xv] The case demonstrates a shift in reasoning from the traditional induction to abduction. The phenomenon of ‘cancer improvement’ is considered causally linked to nivolumab and its underlying physiological mechanisms.[xvi] However, “the weakness of abductions is that there may always be some other better, unknown explanation for an effect. The patient may for example belong to a special subgroup that spontaneously improves, or the change may be a placebo effect. This does not mean, however, that abductive inferences cannot be strong or reasonable, in the sense that they can make a conclusion probable.”[xvii] To demonstrate the limitations of relying on the abductive standard in isolation, commentators have pointed out that side effects in precision medicine are hard to rule out as being related to the initial intervention itself unless trends from a group of patients are taken into consideration.[xviii] As artificial intelligence (AI) assists the development of precision medicine for oncology, this uncertainty ought to be taken into consideration. The implementation of AI has been crucial to the development of precision medicine by providing a way to combine large patient datasets or a single patient with a large number of unique variables with machine learning to recommend matches based on statistics and probability of success upon which practitioners can base medical recommendations.[xix] The AI is usually not establishing a causal relationship[xx] – it is predicting. So, as AI bleeds into medical devices, like brain-computer interfaces, the same cautions about using abductive reasoning alone should be carried over. II. Responsive Neurostimulation, AI, and Personalized Medicine Like precision medicine in cancer treatment, computer-brain interface technology similarly focuses on the individual patient through personalized settings. In order to properly expose the intersection of AI, precision medicine, abductive reasoning, and implantable neurotechnologies, the descriptions of adaptive deep brain stimulation systems need to deepen.[xxi] As a broad summary of adaptive deep brain stimulation, to provide a patient with the therapeutic stimulation, a neural signal, typically referred to as a local field potential,[xxii] must first be detected and then interpreted by the device. The main adaptive deep brain stimulation device with premarket approval, the NeuroPace Responsive Neurostimulation system, is used to treat epilepsy by detecting and storing “programmer-defined phenomena.”[xxiii] Providers can optimize the detection settings of the device to align with the patient’s unique electrographic seizures as well as personalize the reacting stimulation’s parameters.[xxiv] The provider adjusts the technology based on trial and error. One day machine learning algorithms will be able to regularly aid this process in myriad ways, such as by identifying the specific stimulation settings a patient may respond to ahead of time based on their electrophysiological signatures.[xxv] Either way, with AI or programmers, adaptive neurostimulation technologies are individualized and therefore operate in line with precision medicine rather than standard treatments based on large clinical trials. Contemporary neurostimulation devices are not usually sophisticated enough to be prominent in AI discussions where the topics of neural networks, deep learning, generative models, and self-attention dominate the conversation. However, implantable high-density electrocorticography arrays (a much more sensitive version than adaptive deep brain stimulation systems use) have been used in combination with neural networks to help patients with neurologic deficits from a prior stroke “speak” through a virtual avatar.[xxvi] In some experimental situations, algorithms are optimizing stimulation parameters with increasing levels of independence.[xxvii] An example of neurostimulation that is analogous to the use of nivolumab in Norway surrounds a patient in the United States who was experiencing both treatment-resistant OCD and temporal lobe epilepsy.[xxviii]Given the refractory nature of her epilepsy, implantation of an adaptive deep brain stimulation system was indicated. As a form of experimental therapy, her treatment-resistant OCD was also indicated for the off-label use of an adaptive deep brain stimulation set-up. Another deep brain stimulation lead, other than the one implanted for epilepsy, was placed in the patient’s right nucleus accumbens and ventral pallidum region given the correlation these nuclei had with OCD symptoms in prior research. Following this, the patient underwent “1) ambulatory, patient-initiated magnet-swipe storage of data during moments of obsessive thoughts; (2) lab-based, naturalistic provocation of OCD-related distress (naturalistic provocation task); and (3) lab-based, VR [virtual reality] provocation of OCD-related distress (VR provocation task).”[xxix] Such signals were used to identify when to deliver the therapeutic stimulation in order to counter the OCD symptoms. Thankfully, following the procedure and calibration the patient exhibited marked improvements in their OCD symptoms and recently shared her results publicly.[xxx] In both cases, there is a similar level of abductive justification for the efficacy of the delivered therapy. In the case study in which the patient was treated with adaptive deep brain stimulation, they at least had their neural activity tested in various settings to determine the optimum parameters for treatment to avoid them being based on guesswork. Additionally, the adaptive deep brain stimulation lead was already placed before the calibration trials were conducted, meaning that the patient had already taken on the bulk of the procedural risk before the efficacy could be determined. Such an efficacy test could have been replicated in the first patient’s cancer treatment, had it been biopsied and tested against the remaining immunotherapies in vitro. Yet, in the case of cancer with few options, one previous dose of a drug that appeared to work on the patient may justify further doses. However, as the Norwegian case presents, corroboration with known responses to a similar drug (from a clinical trial) could be helpful to validate the treatment strategy. (It should be noted that both patients were resigned to these last resort options regardless of the efficacy of treatment.) There are some elements of inductive logic seen with adaptive deep brain stimulation research in general. For example, abductively the focus could be that patient X’s stimulation parameters are different from patient Y’s and patient Z’s. In contrast, when grouped as subjects who obtained personalized stimulation, patients X, Y, and Z demonstrate an inductive aspect to this approach’s safety and/or efficacy. The OCD case holds plenty of abductive characteristics in line with precision medicine’s approach to treating cancer and as more individuals try the method, there will be additional data. With the gradual integration of AI into brain-computer interfaces in the name of efficacy, this reliance on abduction will continue, if not grow, over time. Moving forward, if a responsive deep brain stimulation treatment is novel and individualized (like the dose of nivolumab) and there is some other suggestion of efficacy (like clinical similarities to other patients in the literature), then it may justify insurance coverage for the investigative intervention, absent other unrelated reasons to deny it. III. Ethical Implications and Next Steps While AI’s use in oncology and neurology is not yet as prominent as its use in other fields (e.g., radiology), it appears to be on the horizon for both.[xxxi] AI can be found in both the functioning of the neurotechnologies as well as the implementation of precision medicine. The increasing use of AI may serve to further individualize both oncologic and neurological therapies. Given these implications and the handful of publications cited in this article, it is important to have a nuanced evaluation of how these treatments, which heavily rely on abductive justification, ought to be managed. The just use an abductive approach may be difficult as AI infused precision medicine is further pursued. At baseline, such technology relies on a level of advanced technology literacy among the general public and could exclude populations who lack access to basic technological infrastructure or know-how from participation.[xxxii] Even among nations with adequate infrastructure, as more patients seek out implantable neurotechnologies, which require robust healthcare resources, the market will favor patient populations that can afford this complex care.[xxxiii] If patients already have the means to pay for an initial dose/use of a precision medicine product out of pocket, should insurance providers be required to cover subsequent treatments?[xxxiv] That is, if a first dose of a cancer drug or a deep brain stimulator over its initial battery life is successful, patients may feel justified in having the costs of further treatments covered. The Norwegian patient’s experience implies there is a precedent for the idea that some public insurance companies ought to cover successful cancer therapies, however, insurance companies may not all see themselves as obligated to cover neurotechnologies that rely on personalized settings or that are based on precision/abductive research more than on clinical trials. CONCLUSION The fact that the cases outlined above rely on abductive style of reasoning implies that there may not be as strong a justification for coverage by insurance, as they are both experimental and individualized, when compared to the more traditional large clinical trials in which groups have the same or a standardized protocol (settings/doses). If a study is examining the efficacy of a treatment with a large cohort of patients or with different experimental groups/phases, insurance companies may conclude that the resulting symptom improvements are more likely to be coming from the devices themselves. A preference for inductive justification may take priority when ruling in favor of funding someone’s continued use of an implantable neurostimulator. There are further nuances to this discussion surrounding the classifications of these interventions as research versus clinical care that warrant future exploration, since such a distinction is more of a scale[xxxv] than binary and could have significant impacts on the “right-to-try” approach to experimental therapies in the United States.[xxxvi] Namely, given the inherent limitations of conducting large cohort trials for deep brain stimulation interventions on patients with neuropsychiatric disorders, surgically innovative frameworks that blend abductive and inductive methodologies, like with sham stimulation phases, have traditionally been used.[xxxvii] Similarly, for adaptive brain-computer interface systems, if there are no large clinical trials and instead only publications that demonstrate that something similar worked for someone else, then, in addition to the evidence that the first treatment/dose worked for the patient in question, the balance of reasoning would be valid and arguably justify insurance coverage. As precision approaches to neurotechnology become more common, frameworks for evaluating efficacy will be crucial both for insurance coverage and for clinical decision making. ACKNOWLEDGEMENT This article was originally written as an assignment for Dr. Francis Shen’s “Bioethics & AI” course at Harvard’s Center for Bioethics. I would like to thank Dr. Shen for his comments as well as my colleagues in the Lázaro-Muñoz Lab for their feedback. - [i] Jonathan Kimmelman and Ian Tannock, “The Paradox of Precision Medicine,” Nature Reviews. Clinical Oncology 15, no. 6 (June 2018): 341–42, https://doi.org/10.1038/s41571-018-0016-0. [ii] Henrik Vogt and Bjørn Hofmann, “How Precision Medicine Changes Medical Epistemology: A Formative Case from Norway,” Journal of Evaluation in Clinical Practice 28, no. 6 (December 2022): 1205–12, https://doi.org/10.1111/jep.13649. [iii] David Barrett and Ahtisham Younas, “Induction, Deduction and Abduction,” Evidence-Based Nursing 27, no. 1 (January 1, 2024): 6–7, https://doi.org/10.1136/ebnurs-2023-103873. [iv] Vogt and Hofmann, “How Precision Medicine Changes Medical Epistemology,” 1208. [v] Wireko Andrew Awuah et al., “Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications,” World Neurosurgery, May 22, 2024, S1878-8750(24)00867-2, https://doi.org/10.1016/j.wneu.2024.05.104. [vi] Mark Riedl, “A Very Gentle Introduction to Large Language Models without the Hype,” Medium (blog), May 25, 2023, https://mark-riedl.medium.com/a-very-gentle-introduction-to-large-language-models-without-the-hype-5 f67941fa59e. [vii] David E. Burdette and Barbara E. Swartz, “Chapter 4 - Responsive Neurostimulation,” in Neurostimulation for Epilepsy, ed. Vikram R. Rao (Academic Press, 2023), 97–132, https://doi.org/10.1016/B978-0-323-91702-5.00002-5. [viii] Kimmelman and Tannock, 2018. [ix] Kimmelman and Tannock, 2018. [x] Simon Lohse, “Mapping Uncertainty in Precision Medicine: A Systematic Scoping Review,” Journal of Evaluation in Clinical Practice 29, no. 3 (April 2023): 554–64, https://doi.org/10.1111/jep.13789. [xi] Kimmelman and Tannock, “The Paradox of Precision Medicine.” [xii] Vogt and Hofmann, 1206. [xiii] Vogt and Hofmann, 1206. [xiv] Vogt and Hofmann, 1206. [xv] Vogt and Hofmann, 1207. [xvi] Vogt and Hofmann, 1207. [xvii] Vogt and Hofmann, 1207. [xviii] Vogt and Hofmann, 1210. [xix] Mehar Sahu et al., “Chapter Three - Artificial Intelligence and Machine Learning in Precision Medicine: A Paradigm Shift in Big Data Analysis,” in Progress in Molecular Biology and Translational Science, ed. David B. Teplow, vol. 190, 1 vols., Precision Medicine (Academic Press, 2022), 57–100, https://doi.org/10.1016/bs.pmbts.2022.03.002. [xx] Stefan Feuerriegel et al., “Causal Machine Learning for Predicting Treatment Outcomes,” Nature Medicine 30, no. 4 (April 2024): 958–68, https://doi.org/10.1038/s41591-024-02902-1. [xxi] Sunderland Baker et al., “Ethical Considerations in Closed Loop Deep Brain Stimulation,” Deep Brain Stimulation 3 (October 1, 2023): 8–15, https://doi.org/10.1016/j.jdbs.2023.11.001. [xxii] David Haslacher et al., “AI for Brain-Computer Interfaces,” 2024, 7, https://doi.org/10.1016/bs.dnb.2024.02.003. [xxiii] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 103–4; “Premarket Approval (PMA),” https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpma/pma.cfm?id=P100026. [xxiv] Burdette and Swartz, “Chapter 4 - Responsive Neurostimulation,” 104. [xxv] Burdette and Swartz, 126. [xxvi] Sean L. Metzger et al., “A High-Performance Neuroprosthesis for Speech Decoding and Avatar Control,” Nature 620, no. 7976 (August 2023): 1037–46, https://doi.org/10.1038/s41586-023-06443-4. [xxvii] Hao Fang and Yuxiao Yang, “Predictive Neuromodulation of Cingulo-Frontal Neural Dynamics in Major Depressive Disorder Using a Brain-Computer Interface System: A Simulation Study,” Frontiers in Computational Neuroscience 17 (March 6, 2023), https://doi.org/10.3389/fncom.2023.1119685; Mahsa Malekmohammadi et al., “Kinematic Adaptive Deep Brain Stimulation for Resting Tremor in Parkinson’s Disease,” Movement Disorders 31, no. 3 (2016): 426–28, https://doi.org/10.1002/mds.26482. [xxviii] Young-Hoon Nho et al., “Responsive Deep Brain Stimulation Guided by Ventral Striatal Electrophysiology of Obsession Durably Ameliorates Compulsion,” Neuron 0, no. 0 (October 20, 2023), https://doi.org/10.1016/j.neuron.2023.09.034. [xxix] Nho et al. [xxx] Nho et al.; Erik Robinson, “Brain Implant at OHSU Successfully Controls Both Seizures and OCD,” OHSU News, accessed March 3, 2024, https://news.ohsu.edu/2023/10/25/brain-implant-at-ohsu-successfully-controls-both-seizures-and-ocd. [xxxi] Awuah et al., “Bridging Minds and Machines”; Haslacher et al., “AI for Brain-Computer Interfaces.” [xxxii] Awuah et al., “Bridging Minds and Machines.” [xxxiii] Sara Green, Barbara Prainsack, and Maya Sabatello, “The Roots of (in)Equity in Precision Medicine: Gaps in the Discourse,” Personalized Medicine 21, no. 1 (January 2024): 5–9, https://doi.org/10.2217/pme-2023-0097. [xxxiv] Green, Prainsack, and Sabatello, 7. [xxxv] Robyn Bluhm and Kirstin Borgerson, “An Epistemic Argument for Research-Practice Integration in Medicine,” The Journal of Medicine and Philosophy: A Forum for Bioethics and Philosophy of Medicine 43, no. 4 (July 9, 2018): 469–84, https://doi.org/10.1093/jmp/jhy009. [xxxvi] Vijay Mahant, “‘Right-to-Try’ Experimental Drugs: An Overview,” Journal of Translational Medicine 18 (June 23, 2020): 253, https://doi.org/10.1186/s12967-020-02427-4. [xxxvii] Michael S. Okun et al., “Deep Brain Stimulation in the Internal Capsule and Nucleus Accumbens Region: Responses Observed during Active and Sham Programming,” Journal of Neurology, Neurosurgery & Psychiatry 78, no. 3 (March 1, 2007): 310–14, https://doi.org/10.1136/jnnp.2006.095315. (shrink)

The expression, ‘the culture of the artificial’ results from the confusion between nature and culture, when nature mingles with culture to produce the ‘artificial’ and science becomes ‘the science of the artificial’. Artificial intelligence can thus be defined as the ultimate expression of the crisis affecting the very foundation of the system of legitimacy in Western society, i.e. Reason, and more precisely, Scientific Reason. The discussion focuses on the emergence of the culture of the artificial (...) and the radical forms of pragmatism, sophism and marketing from a French philosophical perspective. The paper suggests that in the postmodern age of the ‘the crisis of the systems of legitimacy’, the question of social acceptability of any action, especially actions arising out of the application of AI, cannot be avoided. (shrink)

This contribution is an essay of formal philosophy—and more specifically of formal ontology and formal epistemology—applied, respectively, to the philosophy of nature and to the philosophy of sciences, interpreted the former as the ontology and the latter as the epistemology of the modern mathematical, natural, and artificial sciences, the theoretical computer science included. I present the formal philosophy in the framework of the category theory (CT) as an axiomatic metalanguage—in many senses “wider” than set theory (ST)—of mathematics (...) and logic, both of the “extensional” logics of the pure and applied mathematical sciences (=mathematical logic), and the “intensional” modal logics of the philosophical disciplines (=philosophical logic). It is particularly significant in this categorical framework the possibility of extending the operator algebra formalism from (quantum and classical) physics to logic, via the so-called “Boolean algebras with operators” (BAOs), with this extension being the core of our formal ontology. In this context, I discuss the relevance of the algebraic Hopf coproduct and colimit operations, and then of the category of coalgebras in the computations over lattices of quantum numbers in the quantum field theory (QFT), interpreted as the fundamental physics. This coalgebraic formalism is particularly relevant for modeling the notion of the “quantum vacuum foliation” in QFT of dissipative systems, as a foundation of the notion of “complexity” in physics, and “memory” in biological and neural systems, using the powerful “colimit” operators. Finally, I suggest that in the CT logic, the relational semantics of BAOs, applied to the modal coalgebraic relational logic of the “possible worlds” in Kripke’s model theory, is the proper logic of the formal ontology and epistemology of the natural realism, as a formalized philosophy of nature and sciences. (shrink)

In exact epistemology Immanuel Kant’s statement on the priority of application of cognitive faculties is detailed as an intellectual process. Empirical regularities of the JSM–method of automated support for research are synthetic a posteriori judgements. Theoretical intelligence is primary, and its aspects are understanding (Verstand) and mind (Gemüt). The conditions of possible experience in Kant’s sense are implemented in the JSM–method of ASR in intelligent systems. And the JSM–method itself is the transcendental logic of artificial intelligence using two (...) theories of truth, the coherence theory and the correspondence theory. Exact epistemology, which is exact neo-Kantianism, can be naturally considered as a reflection of information society culture on its civilizational (technological) aspects. (shrink)

The demarcation of pseudoscience has been one of the most important philosophical tasks since the 1960s. During the 1980s, an atmosphere of defeatism started to spread among philosophers of science, some of them claimed the failure of the demarcation project. I defend that the more auspicious approach to the problem might be through the intellectual character of epistemic agents, i.e., from the point of view of vice epistemology. Unfortunately, common lists of undesirable character features are usually based on a (...) priori reasoning, and therefore might be considered artificial or too vague. When we base our position on contemporary behavioural sciences, we can see that the epistemic character of believers in pseudoscience is for the most part determined by two related factors. Firstly, these epistemic agents show a higher level of cognitive laziness. By this I mean an inability or unwillingness to engage in reflective thinking and a reluctance to account for counterevidence. Secondly, they yield more easily to metacognitive overconfidence. This can be broadly understood as so-called “knowledge illusion”, the inability to recognize one’s own intellectual limits. The deficiency usually stems from a misunderstanding of the division of cognitive labour and of the agent’s role in epistemic society. I find the proposed epistemological approach to pseudoscience crucial. Only if we understand the descriptive aspects of the problem, can we think of normative solutions to it. (shrink)

Artificial intelligence plays an important role and has been used by several countries as a health strategy in an attempt to understand, control and find a cure for the disease caused by Coronavirus. These intelligent systems can assist in accelerating the process of developing antivirals for Coronavirus and in predicting new variants of this virus. For this reason, much research on COVID-19 has been developed with the aim of contributing to new discoveries about the Coronavirus. However, there are some (...) epistemological aspects about the use of AI in this pandemic period of Covid-19 that deserve to be discussed and need reflections. In this scenario, this article presents a reflection on the two epistemological aspects faced by the COVID-19 pandemic: The epistemological aspect resulting from the use of patient data to fill the knowledge base of intelligent systems; the epistemological problem arising from the dependence of health professionals on the results/diagnoses issued by intelligent systems. In addition, we present some epistemological challenges to be implemented in a pandemic period. (shrink)

Artificial intelligence (AI) systems are quickly gaining ground in healthcare and clinical decision-making. However, it is still unclear in what way AI can or should support decision-making that is based on incapacitated patients’ values and goals of care, which often requires input from clinicians and loved ones. Although the use of algorithms to predict patients’ most likely preferred treatment has been discussed in the medical ethics literature, no example has been realised in clinical practice. This is due, arguably, to (...) the lack of a structured approach to the epistemological, ethical and pragmatic challenges arising from the design and use of such algorithms. The present paper offers a new perspective on the problem by suggesting that preference predicting AIs be viewed as sociotechnical systems with distinctive life-cycles. We explore how both known and novel challenges map onto the different stages of development, highlighting interdisciplinary strategies for their resolution. (shrink)

A situated agent is one which operates within an environment. In most cases, the environment in which the agent exists will be more complex than the agent itself. This means that an agent, human or artificial, which wishes to carry out non-trivial operations in its environment must use techniques which allow an unbounded world to be represented within a cognitively bounded agent. We present a brief description of some important theories within the fields of epistemology and metaphysics. We (...) then discuss ways in which philosophical problems of scepticism are related to the problems faced by knowledge representation. We suggest that some of the methods that philosophers have developed to address the problems of epistemology may be relevant to the problems of representing knowledge within artificial agents. (shrink)

Through the epistemic potential of speculative science fiction, this article offers a reflection on the impacts of technologies on the human body. More precisely, this article illuminates how literary texts anticipate the challenges which will arise in relation with the body of the future from our most awaited and fantasized technologies. Olga Ravn’s novel, The employees (2018), will be the basis of this article’s inquiry into how the augmented body, extraterrestrial life and artificial intelligence impact the body and, by (...) the same token, upset our own understanding of ourselves and the world which surrounds us. As a whole, this article illustrates that a future where technical advancement takes precedence on social progress can lead not only to a disembodied reality, but more importantly to an alienated humanity. (shrink)

This paper analyses the phenomenology and epistemology of chatbots such as ChatGPT and Bard. The computational architecture underpinning these chatbots are large language models (LLMs), which are generative artificial intelligence (AI) systems trained on a massive dataset of text extracted from the Web. We conceptualise these LLMs as multifunctional computational cognitive artifacts, used for various cognitive tasks such as translating, summarizing, answering questions, information-seeking, and much more. Phenomenologically, LLMs can be experienced as a “quasi-other”; when that happens, users (...) anthropomorphise them. For most users, current LLMs are black boxes, i.e., for the most part, they lack data transparency and algorithmic transparency. They can, however, be phenomenologically and informationally transparent, in which case there is an interactional flow. Anthropomorphising and interactional flow can, in some users, create an attitude of (unwarranted) trust towards the output LLMs generate. We conclude this paper by drawing on the epistemology of trust and testimony to examine the epistemic implications of these dimensions. Whilst LLMs generally generate accurate responses, we observe two epistemic pitfalls. Ideally, users should be able to match the level of trust that they place in LLMs to the degree that LLMs are trustworthy. However, both their data and algorithmic opacity and their phenomenological and informational transparency can make it difficult for users to calibrate their trust correctly. The effects of these limitations are twofold: users may adopt unwarranted attitudes of trust towards the outputs of LLMs (which is particularly problematic when LLMs hallucinate), and the trustworthiness of LLMs may be undermined. (shrink)

The book by Max Tegmark draws attention to the dangers and benefits that await humanity as a result of the Artificial Intelligence (AI) technologies development. Cosmologist and astrophysicist Tegmark, realizing the impossibility to predict the AI development, offers exciting scenarios of civilization development options for tens, thousands, millions and billions of years. The analysis of the opposite scenarios is aimed at the idea formation that the consequences of creating a general AI, superior to the human level, will be more (...) significant than from all other achievements of civilization. Tegmark is one of the founders and leaders of the “Beneficial AGI” movement, he presents the results of the discussion of the issues he raises with leading experts in the field of AI. Tegmark concludes his book with a call to optimism: “My book urge you to think about what future you would like, and not what future scares you, this way we can find goals for which it’s worth working”. (shrink)

The relevance of this study's topic is supported by the argument that a philosophical understanding of the fundamental concepts of epistemology as they pertain to the educational process is crucial as the educational setting becomes increasingly digitalised. This paper aims to explore the epistemological component of the philosophy of learning in light of the educational process digitalisation. The research comprised a sample of 462 university students from Kazakhstan, with 227 participants assigned to the experimental and 235 to the control (...) groups. The research employed a scientific methodology that encompassed various scientific methods, including sociological, socio-philosophical, and culturological analysis. In addition, philosophical methods such as system analysis, structural and functional analysis, and comparative and hermeneutical approaches were utilised for the philosophical and epistemological interpretation of the cognitive activity mechanism of students and its dynamics in light of digitalising education. Furthermore, psychological diagnostic methods such as SAMOAL personality self-actualisation diagnostics, personality volitional potential diagnostics, and psychodiagnostic methods were employed. This study offers a theoretical rationale for the integration of digital technology in the realm of education, grounded in philosophical and epistemological principles. In addition, it challenges the null hypothesis that the philosophical and epistemological framework of the educational process does not influence its cognitive and personal aspects. Digital learning has been found to improve both cognitive and personal aspects of the learning process. All indicators of self-realization increased statistically significantly (p 0.05). The positive impact of digital technologies on increasing the communicative competence of students can be considered as a factor in mitigating epistemic risks in the learning process.The findings can be used to avoid epistemological risks and improve learning effectiveness in a digital learning environment. There are plans to investigate the gender dimension of digital learning epistemology in the future. (shrink)

Aims To examine the understanding of the ethical dilemmas associated with Big Data and artificial intelligence (AI) among Jordanian medical students, physicians in training, and senior practitioners. Methods We implemented a literature-validated questionnaire to examine the knowledge, attitudes, and practices of the target population during the period between April and August 2023. Themes of ethical debate included privacy breaches, consent, ownership, augmented biases, epistemology, and accountability. Participants’ responses were showcased using descriptive statistics and compared between groups using t-test (...) or ANOVA. Results We included 466 participants. The greater majority of respondents were interns and residents (50.2%), followed by medical students (38.0%). Most participants were affiliated with university institutions (62.4%). In terms of privacy, participants acknowledged that Big Data and AI were susceptible to privacy breaches (39.3%); however, 59.0% found such breaches justifiable under certain conditions. For ethical debacles involving informed consent, 41.6% and 44.6% were aware that obtaining informed consent posed an ethical limitation in Big Data and AI applications and denounced the concept of “broad consent”, respectively. In terms of ownership, 49.6% acknowledged that data cannot be owned yet accepted that institutions could hold a quasi-control of such data (59.0%). Less than 50% of participants were aware of Big Data and AI’s abilities to augment or create new biases in healthcare. Furthermore, participants agreed that researchers, institutions, and legislative bodies were responsible for ensuring the ethical implementation of Big Data and AI. Finally, while demonstrating limited experience with using such technology, participants generally had positive views of the role of Big Data and AI in complementing healthcare. Conclusion Jordanian medical students, physicians in training and senior practitioners have limited awareness of the ethical risks associated with Big Data and AI. Institutions are responsible for raising awareness, especially with the upsurge of such technology. (shrink)

The article addresses the problem of identifying methods to develop the ability of artificial intelligence (AI) systems to provide explanations for their findings. This issue is not new, but, nowadays, the increasing complexity of AI systems is forcing scientists to intensify research in this direction. Modern neural networks contain hundreds of layers of neurons. The number of parameters of these networks reaches trillions, genetic algorithms generate thousands of generations of solutions, and the semantics of AI models become more complicated, (...) going to the quantum and non-local levels. The world’s leading companies are investing heavily in creating explainable AI (XAI). However, the result is still unsatisfactory: a person often cannot understand the “explanations” of AI because the latter makes decisions differently than a person, and perhaps because a good explanation is impossible within the framework of the classical AI paradigm. AI faced a similar problem 40 years ago when expert systems contained only a few hundred logical production rules. The problem was then solved by complicating the logic and building added knowledge bases to explain the conclusions given by AI. At present, other approaches are needed, primarily those that consider the external environment and the subjectivity of AI systems. This work focuses on solving this problem by immersing AI models in the social and economic environment, building ontologies of this environment, taking into account a user profile and creating conditions for purposeful convergence of AI solutions and conclusions to user-friendly goals. (shrink)

From April 21 to April 23, 2022, the 7th annual International Scientific Conference “The Transcendental Turn in Contemporary Philosophy: Epistemology, Cognitive Science and Artificial Intelligence” was held in Moscow. Traditionally, transcendental workshops present three main lines of transcendentalism, which go back to I. Kant's own transcendental idealism, neo-Kantianism, and phenomenology. In 2022, the Kantian and neo-Kantian days of the seminar were associated with the development of the ideas of cognitive science, transcendental epistemology, artificial intelligence and transcendental (...) philosophy of science, and as part of the phenomenological day, the issue of the role of reflection in the structure of cognitive activity was discussed in detail. (shrink)

Jacques Lafitte occupies an odd place in the philosophy of technology. He was a French engineer who made a significant and conceptually innovative contribution to this field, yet his influence has been elusive and largely ignored until relatively recently. Many of Lafitte’s ideas find echoes in the work of later philosophers, yet, notably in the case of Simondon, apparently without any direct line of influence. Lafitte placed the machine at the centre of his thinking about technology and articulated various layers (...) of analysis around it; for example, he considered machines in the broader context of an artificial world or “mechanosphere”, which encompassed certain aspects of philosophical anthropology. In this work we seek to reconstruct Lafitte’s ideas and briefly trace some of their later impact. We identify three dimensions in Lafitte’s analysis: epistemological, ontological and anthropological. We argue that the most remarkable fact about Lafitte’s thought is the way it inaugurates, and anticipates, the approach of later currents, not just in the “French tradition”, who also made an effort to integrate machine theory into broader philosophical, anthropological and political aspects, in terms that echo Lafitte’s. In particular, we will focus on Gilbert Simondon and cybernetics. (shrink)

This paper argues that whereas philosophical discussions of first-person methods often turn on the veridicality of first-person reports, more attention should be paid to the experimental circumstances under which the reports are generated, and to the purposes of designing such experiments. After pointing to the ‘constructedness’ of first-person reports in the science of perception, I raise questions about the criteria by which to judge whether the reports illuminate something about the nature of perception. I illustrate this point with a historical (...) debate between Gestalt psychologist and atomists, both of whom used first-person methods to investigate perception. (shrink)