Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building (...) of simulative machine models before the advent of cybernetics are been investigated for the first time, on the basis of little known, and sometimes completely forgotten or unpublished, texts and figures. These pre-cybernetics projects can be considered as steps toward the “discovery” of a modelling methodology that has been fully developed by those more recent research programs, and that shares some of their central goals and key methodological proposals. -/- More info in Springer link: http://www.springer.com/new+%26+forthcoming+titles+%28default%29/book/978-1-4020-0606-7 -/- This book is the English translation of La scoperta dell'artificiale, Dunod/Masson, Milan, 1998. (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.

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)

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 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)

Μοναδική μελέτη και προσέγγιση της θεωρίας της γνώσης, για την παγκόσμια βιβλιογραφία, της διαλεκτικής πορείας της σκέψης από την λογική πλευρά της και της μελλοντικής μορφής που θα πάρουν οι διαλεκτικές δομές της, στην αδιαίρετη ενότητα γνωσιοθεωρίας, λογικής και διαλεκτικής, με την «μέθοδο του διαλεκτικού υλισμού». Έργο βαρύ με θέμα εξαιρετικά δύσκολο διακατέχεται από πρωτοτυπία και ζωντάνια που γοητεύει τον κάθε ανήσυχο στοχαστή από τις πρώτες γραμμές. Unique study and approach of the theory of knowledge, the world literature, the dialectic (...) course of thought from the logical side and the future form of its dialectical structures, the indivisible unity of knowledge, logic and dialectics, with the "method of dialectical materialism ". A heavy work on an extremely difficult subject is possessed by originality and liveliness that captivates every restless thinker from the first lines..."Εδώ ακριβώς, όπως έχουμε ξαναπεί, βρίσκεται το πρόβλημα. Η διαλεκτική λογική είναι ήδη το ξεπέρασμα της τυπικής λογικής σαν όργανο για τη σύλληψη της πραγματικότητας. Εμείς όμως θέλουμε να την κάνουμε να έχει «ακρίβεια» στα συμπεράσματά της. Και τότε αντιμετωπίζουμε το πρόβλημα που έχει επισημάνει ο Lenine, πως δηλαδή: όχι το αυτονόητο γεγονός της «κίνησης» του κόσμου, αλλά το «πως» να εκφράσουμε την κίνηση αυτή με τις έννοιες, με τις κρίσεις και τους συλλογισμούς και προχωρώντας πιο πέρα, με τη δυνατότητα που προσφέρεται, ύστερα από το Hegel, από τη φορμαλοποίηση κα αξιωματικοποίηση της τυπικής λογικής, να τις εκφράσουμε «συντακτικά» διατυπώνοντας την μεταβολή. Κι αυτός είναι ο «κόμπος» που πρέπει να λυθεί: όχι μονάχα πάνω στη σημαντική περιοχή, αλλά και στη συντακτική. Γιατί οι έννοιες, οι κρίσεις κι οι συλλογισμοί, αφού ήδη εκφράζονται από την τυπική λογική με σύμβολα ικανά να μας δώσουν, με το συνδυασμό τους μονάχα, καινούρια συμπεράσματα, αλλά ταυτόχρονα και τον «έλεγχο» (από την άποψη της τυπικής αλήθειας) των συλλογισμών μας, το κάνουν αυτό πάνω σε προτάσεις που εκφράζουν την ακινησία και τη σταθερότητα. Εδώ, το «μόνο» που χρειάζεται είναι να δώσουμε κίνηση και ζωή στο σύστημα της τυπικο-στατικής λογικής, έτσι που οι προτάσεις της με το συμβολισμό τους να αναφέρονται και στις διαδοχικά επόμενες μέσα στο χρόνο τιμές αλήθειας. Οι προτάσεις, με ενδιάμεσο τη σκέψη πάντοτε, αφού στέκονται όχι μονάχα απέναντι σε μια ακίνητη στιγμή της ροής του κόσμου, αλλά και ταυτόχρονα απέναντι σ' ένα αδιάκοπα μεταβαλλόμενο περιεχόμενο, απέναντι στις αντικειμενικές διακυμάνσεις της πραγματικότητας, θα πρέπει και οι προτάσεις αυτές να πιάνουν ταυτόχρονα: το στατικό και το ρέον. Το στατικό, σα στιγμή ταυτοποίησης και αναγνώρισης των διαδοχικά «μεταβαλλόμενων ακίνητων» στιγμών του περιεχόμενου και το ρέον, σα διαδοχική ολοκλήρωση των στάνταρ-στιγμών του περιεχόμενου των αντικειμενικών προτσές. Μήπως τα μαθηματικά μας βοηθάνε εδώ;....". (shrink)

This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of processing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it according to the needs emerging in different situational scenarios, is an important (...) aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer & Todd) due to their “bounded rationality” (Simon, 1957). In this perspective, one of the requirements that should be considered for the design, the realization and the evaluation of intelligent cognitively-inspired systems should be rep- resented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This requirement, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution. (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)

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

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)

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)

It has been argued that our currently most satisfactory social epistemology of science can’t account for science that is based on artificial intelligence (AI) because this social epistemology requires trust between scientists that can take full responsibility for the research tools they use, and scientists can’t take full responsibility for the AI tools they use since these systems are epistemically opaque. I think this argument overlooks that much AI-based science can be done without opaque models, and that (...) agents can take full responsibility for the systems they use even if these systems are opaque. Requiring that an agent fully understand how a system works is an untenably strong condition for that agent to take full responsibility for the system and risks absolving AI developers from responsibility for their products. AI-based science need not create trust-related social epistemological problems if we keep in mind that what makes both individual scientists and their use of AI systems trustworthy isn’t full transparency of the internal processing but their adherence to social and institutional norms that ensure that scientific claims can be trusted. (shrink)

Modern life sciences research is increasingly relying on artificial intelligence (AI) approaches to model biological systems, primarily centered around the use of machine learning (ML) models. Although ML is undeniably useful for identifying patterns in large, complex data sets, its widespread application in biological sciences represents a significant deviation from traditional methods of scientific inquiry. As such, the interplay between these models and scientific understanding in biology is a topic with important implications for the future of scientific research, yet (...) it is a subject that has received little attention. Here, we draw from an epistemological toolkit to contextualize recent applications of ML in biological sciences under modern philosophical theories of understanding, identifying general principles that can guide the design and application of ML systems to model biological phenomena and advance scientific knowledge. We propose that conceptions of scientific understanding as information compression, qualitative intelligibility, and dependency relation modelling provide a useful framework for interpreting ML-mediated understanding of biological systems. Through a detailed analysis of two key application areas of ML in modern biological research – protein structure prediction and single cell RNA-sequencing – we explore how these features have thus far enabled ML systems to advance scientific understanding of their target phenomena, how they may guide the development of future ML models, and the key obstacles that remain in preventing ML from achieving its potential as a tool for biological discovery. Consideration of the epistemological features of ML applications in biology will improve the prospects of these methods to solve important problems and advance scientific understanding of living systems. (shrink)

This paper delves into the profound advancements of Large Language Models (LLMs), epitomized by GPT-3, in natural language processing and artificial intelligence. It explores the epistemological foundations of LLMs through the lenses of Aristotle and Kant, revealing apparent distinctions from human learning. Transitioning seamlessly, the paper then delves into the ethical landscape, extending beyond knowledge acquisition to scrutinize the implications of LLMs in decision-making and content creation. The ethical scrutiny, employing virtue ethics, deontological ethics, and teleological ethics, delves into (...) LLMs' behaviours and decisions, necessitating the exploration of novel ethical paradigms tailored to machine intelligence. The paper also addresses biases in data, privacy concerns, copyright implications, and intellectual property rights, emphasizing the need to adapt frameworks for both human and machine creators. The paper concludes by advocating for interdisciplinary dialogue between philosophers, cognitive scientists, and AI researchers to foster a balanced and ethically grounded integration of LLMs into the evolving landscape of artificial intelligence. Continuous reflection on the evolving relationship between human cognition and artificial intelligence emerges as a crucial aspect for shaping a harmonious and responsible future in this domain. (shrink)

Can artificial entities be worthy of moral considerations? Can they be artificial moral agents (AMAs), capable of telling the difference between good and evil? In this essay, I explore both questions—i.e., whether and to what extent artificial entities can have a moral status (“the machine question”) and moral agency (“the AMA question”)—in light of Kazuo Ishiguro’s 2021 novel Klara and the Sun. I do so by juxtaposing two prominent approaches to machine morality that are central to the (...) novel: the (1) view “from within,” including the standard (or “metaphysical”) perspective on moral agency, and the (2) view “from outside,” which includes behaviorism, functionalism and the social-relational perspective. Importantly, while the story illustrates both views, it exposes the epistemological vulnerability of the first in relation to the practical and social reality imposed by the second. That is, regardless of what metaphysical properties the Artificial Friend Klara can be said to have (from within), her moral status as well as agency ultimately depend on the views of others (from outside), including the others’ own epistemic beliefs about the nature of consciousness and personhood. (shrink)

The classical theory of computation does not represent an adequate model of reality for simulation in the social sciences. The aim of this paper is to construct a methodological perspective that is able to conciliate the formal and empirical logic of program verification in computer science, with the interpretative and multiparadigmatic logic of the social sciences. We attempt to evaluate whether social simulation implies an additional perspective about the way one can understand the concepts of program and computation. We demonstrate (...) that the logic of social simulation implies at least two distinct types of program verifications that reflect an epistemological distinction in the kind of knowledge one can have about programs. Computer programs seem to possess a causal capability (Fetzer, 1999) and an intentional capability that scientific theories seem not to possess. This distinction is associated with two types of program verification, which we call empirical and intentional verification. We demonstrate, by this means, that computational phenomena are also intentional phenomena, and that such is particularly manifest in agent-based social simulation. Ascertaining the credibility of results in social simulation requires a focus on the identification of a new category of knowledge we can have about computer programs. This knowledge should be considered an outcome of an experimental exercise, albeit not empirical, acquired within a context of limited consensus. The perspective of intentional computation seems to be the only one possible to reflect the multiparadigmatic character of social science in terms of agent-based computational social science. We contribute, additionally, to the clarification of several questions that are found in the methodological perspectives of the discipline, such as the computational nature, the logic of program scalability, and the multiparadigmatic character of agent-based simulation in the social sciences. (shrink)

We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.

I will present two different applications - Dual PECCS and the TCL reasoning framework - addressing some crucial aspects of commonsense reasoning (namely: dealing with typicality effects and with the problem of commonsense compositionality) in a way that is integrated or compliant with different cognitive architectures. In doing so I will show how such aspects are better dealt with at different levels of representation and will discuss the adopted solution to integrate such representational layers.

Since Thomas Kuhn’s The Structure of Scientific Revolutions (1962), historians and philosophers of science have paid increasing attention to the implications of disciplinarity. In this chapter we consider restrictions posed to interdisciplinary exchange between ecology and economics that result from a particular kind of commitment to the ideal of disciplinary purity, that is, that each discipline is defined by an appropriate, unique set of objects, methods, theories, and aims. We argue that, when it comes to the objects of study in (...) ecology and economics, ideas of disciplinary purity have been underwritten by the artificial-natural distinction. We then problematize this distinction, and thus disciplinary purity, both conceptually and empirically. Conceptually, the distinction is no longer tenable. Empirically, recent interdisciplinary research has shown the epistemological and policy-oriented benefits of dealing with models which explicitly link anthropogenic (i.e., “artificial”) and non-anthropogenic factors (i.e., “natural”). We conclude that, in the current age of the Anthropocene, it is to be expected that without interdisciplinary exchange, ecology and economics may relinquish global relevance because the distinct and separate systems to which each “pure” science was originally made to apply will only diminish over time. (shrink)

Defining Artificial Intelligence and Artificial General Intelligence remain controversial and disputed. They stem from a longer-standing controversy of what is the definition of consciousness, which if solved could possibly offer a solution to defining AI and AGI. Central to these problems is the paradox that appraising AI and Consciousness requires epistemological objectivity of domains that are ontologically subjective. I propose that applying the philosophy of art, which also aims to define art through a lens of epistemological objectivity where (...) the domains are ontologically subjective, can further elucidate this unsolved question. In this sense, Art and AI and ultimately consciousness are multifaceted domains where conventional complexity theory and current philosophical approaches may be augmented by aesthetic principles ranging from classical Aristotelian essentialism to Wittgensteinian anti-essentialism. This approach of AI as art may offer novel solutions to characterising and elucidating the ciphers of consciousness and AI. (shrink)

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

A CONSTRUCTIVE CRITIQUE OF THE FOUNDATIONS OF PHILOSOPHY: Abstract of the Ph.D. Dissertation By Ramesh N. Patel June, 1970 -/- Philosophy pursues a rational explication of our understanding, experiences, and values in terms of objective truth and reality. Conspicuously, its view of rationality has been rigid and preconceived. Application of the preconceived reason in the explication of the essential features of our world fails and issues in a network of dialectical tangles. These artificially created tangles pose a unique intellectual challenge, (...) but the perennial failure to resolve them limits the intellectual response of philosophers to remaining caught in the tangles, or to taking intuitively favorable sides, or to simply denouncing the tangles as meaningless. Each is understandable. -/- Superficially, traditional philosophical reason looks like a correct tool for the explication of our world of common sense, but its applications to the world result in logical tangles showing its inadequacy. The philosopher’s faith in such reason ends up in one of two positions: abandoning our world in the interests of such reason, or embodying itself in the world through a special form of reason. Plato took the first and Aristotle took the second position. My analysis shows that Plato failed to explicate the relation between his world of Forms and our world of particulars, and, more importantly, between the Forms and the intuitive reason which grasps them. Aristotle, in my analysis, cannot conceal that the law of noncontradiction is a mere exhortation and has no descriptive necessity with respect to thought, things, or meaning. We also find such dialectical difficulties in Augustine, Hume and Strawson. -/- “Microphilosophical feasibilism” is our proposal for the best treatment, if not the definitive solution, of these problems. Instead of separating philosophical method into logic, epistemology, axiology, and metaphysics on the one side, and partitioning the projected results of philosophizing into validity, truth, valuation, and reality on the other, we need to start philosophizing with a general distinction between “method employed” and “intended result.” This distinction is not pre-colored by claims to ultimate verities. It is a direct aid to understanding. I consider the basic inevitable paradox that a philosopher should explain not only our understanding of experience but also his philosophizing itself. The circularity between “method employed” and “intended result” needs to be admitted as an initial fact about reason rather than circumvented by an implausibly claimed capacity of philosophical reason to formally transcend everything including this circularity. -/- Presuppositions are reduced to a minimum inevitable number. Being our primary microphilosophical criteria, they are reason, experience, and value in their minimal sense. They cannot be plausibly substituted or further reduced. They cannot be preferred one or more to others arbitrarily. We show how, in microphilosophy they coincide or synchronize. Jointly and basically, they form the minimum method employed. The initial, basic, intended result is called minimum feasibility. The fundamental circularity between method and result leaves no way out but to ground self-reference in such a manner that method and result “logico-genetically” coincide. Two extreme situations are avoided: “the logical zero-situation” where nothing is true or real, and “the credulous open situation” where everything is true or real. Finding a mean manifests a basic value. If the meaning of truth and reality is to be preserved, the basic value must have a clear impact and express descriptive rather than revisionary reason. -/- Several other stringent demands are raised as conditions which microphilosophy should fulfil to achieve feasibility. The final, joint, outcome of both “method” and “result,” in the thesis of microphilosophical feasibilism, is formulated thus: Self refers freely and symbolically to itself, own person, own body, material bodies, other persons’ bodies, other persons, and other selves. -/- Ramesh N. Patel Doctor of Philosophy in Philosophy conferred in June, 1970 University of New Mexico, Albuquerque, N.M. (shrink)

In the Fall of 1983, I offered a junior/senior-level course in Philosophy of Artificial Intelligence, in the Department of Philosophy at SUNY Fredonia, after returning there from a year’s leave to study and do research in computer science and artificial intelligence (AI) at SUNY Buffalo. Of the 30 students enrolled, most were computerscience majors, about a third had no computer background, and only a handful had studied any philosophy. (I might note that enrollments have subsequently increased in the (...) Philosophy Department’s AI-related courses, such as logic, philosophy of mind, and epistemology, and that several computer science students have added philosophy as a second major.) This article describes that course, provides material for use in such a course, and offers a bibliography of relevant articles in the AI, cognitive science, and philosophical literature. (shrink)

In this article, I argue that the artificial components of hybrid bionic systems do not play a direct explanatory role, i.e., in simulative terms, in the overall context of the systems in which they are embedded in. More precisely, I claim that the internal procedures determining the output of such artificial devices, replacing biological tissues and connected to other biological tissues, cannot be used to directly explain the corresponding mechanisms of the biological component(s) they substitute (and therefore cannot (...) be used to explain the local mechanisms determining an overall biological or cognitive function replicated by such bionic models). I ground this analysis on the use of the Minimal Cognitive Grid (MCG), a novel framework proposed in Lieto (Cognitive design for artificial minds, 2021) to rank the epistemological and explanatory status of biologically and cognitively inspired artificial systems. Despite the lack of such a direct mechanistic explanation from the artificial component, however, I also argue that the hybrid bionic systems can have an indirect explanatory role similar to the one played by some AI systems built by using an overall structural design approach (but including the partial adoption of functional components). In particular, the artificial replacement of part(s) of a biological system can provide i) a local functional account of that part(s) in the context of the overall functioning of the hybrid biological–artificial system and ii) global insights about the structural mechanisms of the biological elements connected to such artificial devices. (shrink)

Following the publication of numerous ethical principles and guidelines, the concept of 'Trustworthy AI' has become widely used. However, several AI ethicists argue against using this concept, often backing their arguments with decades of conceptual analyses made by scholars who studied the concept of trust. In this paper, I describe the historical-philosophical roots of their objection and the premise that trust entails a human quality that technologies lack. Then, I review existing criticisms about 'Trustworthy AI' and the consequence of ignoring (...) these criticisms: if the concept of 'Trustworthy AI' is kept being used, we risk attributing responsibilities to agents who cannot be held responsible, and consequently, deteriorate social structures which regard accountability and liability. Nevertheless, despite suggestions to shift the paradigm from 'Trustworthy AI' to 'Reliable AI', I argue that, realistically, this concept will be kept being used. I end by arguing that, ultimately, AI ethics is also about power, social justice, and scholarly activism. Therefore, I propose that community-driven and social justice-oriented ethicists of AI and trust scholars further focus on (a) democratic aspects of trust formation; and (b) draw attention to critical social aspects highlighted by phenomena of distrust. This way, it will be possible to further reveal shifts in power relations, challenge unfair status quos, and suggest meaningful ways to keep the interests of citizens. (shrink)

Artificial intelligence (AI) and machine learning (ML) models have undoubtedly become useful tools in science. In general, scientists and ML developers are optimistic – perhaps rightfully so – about the potential that these models have in facilitating scientific progress. The philosophy of AI literature carries a different mood. The attention of philosophers remains on potential epistemological issues that stem from the so-called “black box” features of ML models. For instance, Eamon Duede (2023) argues that opacity in deep learning models (...) (DLMs) is epistemically problematic in the context of justification, though not in the context of discovery. -/- In this paper, I aim to show that a similar epistemological concern is echoed in the epistemology of imagination literature. It is traditionally held that, given its black box features, reliance on the imagination is epistemically problematic in the context of justification, though not in the context of discovery. The constraints-based approach to the imagination answers the epistemological concern by providing an account of how we can rely on the imagination in the context of justification by way of constraints. I argue by analogy that a similar approach can be applied to the opaque DLM case. Ultimately, my goal is to explore just how far this analogy extends, and whether a constraints-based approach to opaque DLMs can answer the epistemological concern surrounding their black box features in the context of justification. -/- (Note that this paper is IN PROGRESS and UNPUBLISHED). (shrink)

his article will focus on the mechanistic origins of the computer metaphor, which forms the conceptual framework for the methodology of the cognitive sciences, some areas of artificial intelligence and the philosophy of mind. The connection between the history of computing technology, epistemology and the philosophy of mind is expressed through the metaphorical dictionaries of the philosophical discourse of a particular era. The conceptual clarification of this connection and the substantiation of the mechanistic components of the computer metaphor (...) is the main goal of this article. The statement is substantiated that the invention of mechanical computing devices, having a long history in the European engineering tradition, formed the prerequisites for the emergence of machine functionalism in the modern philosophy of mind. The idea of multiple implementation stems from the principle that a formal symbol system prescribes rules for the use of rational abstractions through the physical architecture of a computational engine. The article considers the reasons for the conceptual shift and reveals the semantic foundations for the metaphorical transfer of the properties of abstract objects from the theory of automata to the field of modern philosophy of mind. The criticism and ways of protecting the philosophical program of machine functionalism are analyzed by changing the content of the metaphor “Mind as machine”. The reasons for the stability of the information-computer approach in cognitive sciences are also disclosed and explained. (shrink)

Prominent philosophical accounts of artistic forgery have neglected a central aspect of the aesthetic harm it perpetrates. To be properly understood, forgery must be seen in the context of our ongoing attempts to augment our aesthetic understanding in conditions of uncertainty. The bootstrapping necessary under these conditions requires a highly refined comprehension of historical context. By creating artificial associations among aesthetically relevant qualities and misrepresenting historical relationships, undetected forgeries stunt or distort aesthetic understanding. The effect of this may be (...) quite pervasive, and removing known forgeries from museum walls will be insufficient to eradicate it. Continued attention to forgeries, once exposed, can in fact serve us by increasing our understanding of how aesthetic understanding is formed and by helping us to repair the damage they have inflicted. (shrink)

Diversity is often announced as a solution to ethical problems in artificial intelligence (AI), but what exactly is meant by diversity and how it can solve those problems is seldom spelled out. This lack of clarity is one hurdle to motivating diversity in AI. Another hurdle is that while the most common perceptions about what diversity is are too weak to do the work set out for them, stronger notions of diversity are often defended on normative grounds that fail (...) to connect to the values that are important to decision-makers in AI. However, there is a long history of research in feminist philosophy of science and a recent body of work in social epistemology that taken together provide the foundation for a notion of diversity that is both strong enough to do the work demanded of it, and can be defended on epistemic grounds that connect with the values that are important to decision-makers in AI. We clarify and defend that notion here by introducing emergent expertise as a network phenomenon wherein groups of workers with expertise of different types can gain knowledge not available to any individual alone, as long as they have ways of communicating across types of expertise. We illustrate the connected epistemic and ethical benefits of designing technology with diverse groups of workers using the examples of an infamous racist soap dispenser, and the millimeter wave scanners used in US airport security. (shrink)

Thanks to mounting discussion about projected technologies’ possibly altering the species mentally and physically, philosophical investigation of what human beings are proceeds robustly. Many thinkers contend that whatever we are has little to do with how we should behave. Yet, tampering with what the human being is may tread upon human rights to be whatever one is. Rights given in widely recognized documents such as the U.N. Declaration of the Rights of Indigenous Peoples assume what humans are and need depends (...) upon ontological assumptions about human existence. Godman turns to human ontology—just what kind of beings humans are (from an angle distinct from the other two books) and how these play into rights. She is concerned with how well the social sciences accord with what we are. Gunkel homes in on a more specific moral topic than Godman's general concern with identity’s role in rights: automata moral status and rights. He enjoins us to inquire into what human rights are before we take on the new technologies’ challenge to it. Schneider’s Artificial You, while not explicitly about rights, poses a rights quandary. Certain individuals with reputable resources and practical backing explicitly seek to evolve the species as they see fit. The urgency among these works concerns how we need a sharper idea of what we are, how we should act and be treated, and how our rights fare among potential human improvements. (shrink)

In recent years, a number of philosophers have turned their attention to developments in Artificial Intelligence, and in particular to deepfakes. A deepfake is a portmanteau of ‘deep learning' and ‘fake', and for the most part they are videos which depict people doing and saying things they never did. As a result, much of the emerging literature on deepfakes has turned on questions of trust, harms, and information-sharing. In this paper, I add to the emerging concerns around deepfakes by (...) drawing on resources from vice epistemology. As deepfakes become more sophisticated, I claim, they will develop to be a source of online epistemic corruption. More specifically, they will encourage consumers of digital online media to cultivate and manifest various epistemic vices. My immediate focus in this paper is on their propensity to encourage the development of what I call ‘intellectual cynicism'. After sketching a rough account of this epistemic vice, I go on to suggest that we can partially offset such cynicism – and fears around deceptive online media more generally – by encouraging the development what I term a trained ‘digital sensibility'. This, I contend, involves a calibrated sensitivity to the epistemic merits of online content. (shrink)

The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, (...) what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature. (shrink)

This research was conducted on the basis of research references from Microsoft Indonesia regarding the adoption of artificial intelligence in Indonesia which obtained research results that there were 14% of employees and leaders of technology-based companies in Indonesia who were still skeptical of the adoption of artificial intelligence. This study aims to provide a theoretical overview from the point of view of the philosophy of communication science in responding to considerations about the good and bad 'doubt' or skepticism (...) of 14% of employees and company leaders towards the adoption of the Artificial Intelligence platform by Microsoft companies in Indonesia. This study uses a qualitative method using a literature study approach. The reference books in the form of books that are the primary sources in this research are books entitled Aristotle Physic Book VIII and the book Philosophy of Communication Studies: Introduction to Ontology, Epistemology, Axiology. The results show that from the point of view of the philosophy of science, the essence of AI is a tool or means as well as a machine that is created with components that resemble and play a role as if the mind is capable of creating or creating like humans. AI is made human as a co-creator which in principle remains limited and will never be perfect 'in se' so it is appropriate that a tone of skepticism accompanies it. (shrink)

What role does the wild duck play in Ibsen's famous drama? I argue that, besides mirroring the fate of the human cast members, the duck is acting as animal subject in a quasi-experiment, conducted in a private setting. Analysed from this perspective, the play allows us to discern the epistemological and ethical dimensions of the new scientific animal practice (systematic observation of animal behaviour under artificial conditions) emerging precesely at that time. Ibsen's play stages the clash between a scientific (...) and a romantic understanding of animals that still constitutes the backdrop of most contemporary debates over animals in research. Whereas the scientific understanding reduces the animal's behaviour, as well as its environment, to discrete and modifiable elements, the romantic view regards animals as being at one with (or violently disconnected from) their natural surroundings. (shrink)

While feminist critiques of AI are increasingly common in the scholarly literature, they are by no means new. Alison Adam’s Artificial Knowing (1998) brought a feminist social and epistemological stance to the analysis of AI, critiquing the symbolic AI systems of her day and proposing constructive alternatives. In this paper, we seek to revisit and renew Adam’s arguments and methodology, exploring their resonances with current feminist concerns and their relevance to contemporary machine learning. Like Adam, we ask how new (...) AI methods could be adapted for feminist purposes and what role new technologies might play in addressing concerns raised by feminist epistemologists and theorists about algorithmic systems. In particular, we highlight distributed and federated learning as providing partial solutions to the power-oriented concerns that have stymied efforts to make machine learning systems more representative and pluralist. (shrink)

Generative artificial intelligence has given us synthetic media that are increasingly easy to create and increasingly hard to distinguish from photographs and videos. Whereas an existing literature has been concerned with how these new media might make a difference for would-be knowers—the viewers of photographs and videos—I advance a thesis about how they will make a difference for would-be communicators—those who embed photos and videos in their speech acts. I claim that the presence of these media in our information (...) environment reduces our ability to show one another things, even as it may increase our resources for telling. And I argue that this has consequences beyond the disruption of knowledge acquisition; showing is a way that we preserve relational equality through superficial asymmetries in political communication, and thereby express respect for our audiences. If synthetic media reduce our options for showing, they then interfere in the way that we manage our relationships in the context of collective political action. (shrink)

Summary. The prerequisites of this study have three interwoven sources, the natural sciences and philosophical and socio-political ones. They are trends in the way of being of a modern, technogenic civilization. The COVID-19 pandemic caused significant damage to the image of the omnipotent techno-science that has developed in the mentality of this sociocultural type.Our goal was to study the co-evolutionary nature of this phenomenon as a natural consequence of the nature of the evolutionary strategy of our biological species. Technological civilization (...) as a cultural-civilizational type is one of the evolutionary options of Homo sapiens, the ontological basis of which is a stable evolutionary strategy (SESH). The latter is capable to spontaneous development and is consisting from biological, sociocultural, and techno-rationalistic modules. Evolutionary risks inextricably linked with it. At present, humankind has entered an era when its existence should be ensured by the meta-system engineering of its own cultural and ecological niche. By such artificially created elements, it implies the design and implementation of ecological systems of various levels of complexity, from the design of elements of such systems (which are biological individuals, populations and species) to the design of a global ecological system (biosphere). At the same time, there is a sharp jump in the value of anthropogenic and technological risk due to a sharp increase in the instability of the structure of ecological systems. We are witnessing a third wave of breaking and reconstruction of the existing relations between the elements of such systems after the Neolithic revolution and great geographical discoveries. Technological innovations led to sharp violations of normal social ecodynamics, leading to outbreaks of new infections, in particular. Theuncontested development of all sectors of controlled evolution technology is the way out of the last such crisis and the prevention of similar ones in the future. There are global nature of the organization of modern technological civilization and the transformation of the biosphere not so much into the noosphere (according to Vernadsky) but into the technosphere that determines the effect of a cascade reaction: any local co-adaptive conflict between elements of a cultural, socio-ecological niche tends to turnglobal and systemic problems. Humanity is constantly forced to make a choice between the stability and adaptability of the technosphere. (shrink)

The objective of this work is to explore the notion of “action” and “agency” in artificial intelligence (AI). It employs a metaphysical notion of action and agency as an epistemological tool in the critique of the notion of “action” and “agency” in artificial intelligence. Hence, both a metaphysical and cognitive analysis is employed in the investigation of the quiddity and nature of action and agency per se, and how they are, by extension employed in the language and science (...) of artificial intelligence. The advent of the science of artificial intelligence and cognitive science, and the technological applications of artificial intelligence in the production of agents such as driverless cars and expert systems, have raised the question of moral, ethical and/or legal responsibility in AI agents. This has re-emphasized the importance of the philosophical discourse on the notions of action and agency, which in contemporary intellectual discourse are now perceived to be phenomena within the epistemic competence of the natural sciences. This paper argues that AI systems do not and cannot possess free agency and autonomy, thus cannot be morally and ethically responsible, hence, it recommends a socio-political response to the question of responsibility in AI. It is then the duty of individual nations, or the global community to define and enact policies on who shoulders the responsibility of actions executed by AIs. (shrink)

In a recent paper in this journal, Carter and Peterson raise two distinctly epistemological puzzles that arise for anyone aspiring to defend the precautionary principle. The first puzzle trades on an application of epistemic contextualism to the precautionary principle; the second puzzle concerns the compatibility of the precautionary principle with the de minimis rule. In this note, I argue that neither puzzle should worry defenders of the precautionary principle. The first puzzle can be shown to be an instance of the (...) familiar but conceptually harmless challenge of adjudicating between relevant interests to reach assessments of threats when applying the precautionary principle. The second puzzle can be shown to rely on a subtle but crucial misrepresentation of the relevant probabilities at play when applying the precautionary principle. (shrink)

There is an existing debate regarding the view that technological instruments, devices, or machines can assert ‎or testify. A standard view in epistemology is that only humans can testify. However, the notion of quasi-‎testimony acknowledges that technological devices can assert or testify under some conditions, without ‎denying that humans and machines are not the same. Indeed, there are four relevant differences between ‎humans and instruments. First, unlike humans, machine assertion is not imaginative or playful. Second, ‎machine assertion is prescripted (...) and context restricted. As such, computers currently cannot easily switch ‎contexts or make meaningful relevant assertions in contexts for which they were not programmed. Third, ‎while both humans and computers make errors, they do so in different ways. Computers are very sensitive to ‎small errors in input, which may cause them to make big errors in output. Moreover, automatic error control ‎is based on finding irregularities in data without trying to establish whether they make sense. Fourth, ‎testimony is produced by a human with moral worth, while quasi-testimony is not. Ultimately, the notion of ‎quasi-testimony can serve as a bridge between different philosophical fields that deal with instruments and ‎testimony as sources of knowledge, allowing them to converse and agree on a shared description of reality, ‎while maintaining their distinct conceptions and ontological commitments about knowledge, humans, and ‎nonhumans.‎. (shrink)

The near-miss has been considered an important factor of reinforcement in gambling behavior, and previous research has focused more on its industry-related causes and effects and less on the gaming phenomenon itself. The near-miss has usually been associated with the games of slots and scratch cards, due to the special characteristics of these games, which include the possibility of pre-manipulation of award symbols in order to increase the frequency of these “engineered” near-misses. In this paper, we argue that starting from (...) an elementary mathematical description of the classical (by pure chance) near-miss, generalizable to any game, and focusing equally on the epistemology of its constitutive concepts and their mathematical description, we can identify more precisely the fallacious elements of the near-miss cognitive effects and the inadequate perception and representation of the observational-intentional “I was that close.” This approach further suggests a strategy of using non-standard mathematical knowledge of an epistemological type in problem-gambling prevention and cognitive therapies. (shrink)

In a recent paper in this journal, we proposed two novel puzzles associated with the precautionary principle. Both are puzzles that materialise, we argue, once we investigate the principle through an epistemological lens, and each constitutes a philosophical hurdle for any proponent of a plausible version of the precautionary principle. Steglich-Petersen claims, also in this journal, that he has resolved our puzzles. In this short note, we explain why we remain skeptical.

The debate around the notions of a priori knowledge and a posteriori knowledge has proven crucial for the development of many fields in philosophy, such as metaphysics, epistemology, metametaphysics etc. We advocate that the recent debate on the two notions is also fruitful for man-made distributed computing systems and for the epistemic analysis thereof. Following a recently proposed modal and fallibilistic account of a priori knowledge, we elaborate the corresponding concept of a priori belief: We propose a rich taxonomy (...) of types of a priori beliefs and their role for the different agents that participate in the system engineering process, which match the existing view exceedingly well and are particularly promising for explaining and dealing with unexpected behaviors in fault-tolerant distributed systems. Developing such a philosophical foundation will provide a sound basis for eventually implementing our ideas in a suitable epistemic reasoning and analysis framework and, hence, constitutes a mandatory first step for developing methods and tools to cope with the various challenges that emerge in such systems. (shrink)

This paper applies a virtue epistemology approach to using the Internet, as to improve our information-seeking behaviours. Virtue epistemology focusses on the cognitive character of agents and is less concerned with the nature of truth and epistemic justification as compared to traditional analytic epistemology. Due to this focus on cognitive character and agency, it is a fruitful but underexplored approach to using the Internet in an epistemically desirable way. Thus, the central question in this paper is: How (...) to use the Internet in an epistemically virtuous way? Using the work of Jason Baehr, it starts by outlining nine intellectual or epistemic virtues: curiosity, intellectual autonomy, intellectual humility, attentiveness, intellectual carefulness, intellectual thoroughness, open-mindedness, intellectual courage and intellectual tenacity. It then explores how we should deploy these virtues and avoid the corresponding vices when interacting with the Internet, particularly search engines. Whilst an epistemically virtuous use of the Internet will not guarantee that one will acquire true beliefs, understanding or even knowledge, it will strongly improve one’s information-seeking behaviours. The paper ends with arguing that teaching and assessing online intellectual virtues should be part of school and university curricula, perhaps embedded in critical thinking courses, or even better, as individual units. (shrink)

The question of authenticity centers in the lives of women of color to invite and restrict their representative roles. For this reason, Gayatri Chakravorty Spivak and Uma Narayan advocate responding with strategic essentialism. This paper argues against such a strategy and proposes an epistemic understanding of the question of authentic- ity. The question stems from a kernel of truth—the connection between experience and knowledge. But a coherence theory of knowledge better captures the sociality and the holism of experience and knowledge.

AI chatbots are disseminating more and more of the Internet’s search engine activity, transforming the face of education, serving as personalized AIs in intellectual and emotional relationships with humans, becoming “digital workers” that may outmode us at work, and more. Indeed, the larger category of generative AI may be one of the most transformative technologies of this decade, or even this century. Given this, it is imperative that we understand the epistemological challenges that arise with the everyday use of LLM (...) chatbots. How will AI chatbots impact the way we come to know the world, and indeed, how will their use impact our very lives? In this piece, I articulate a major challenge that arises from their growing use, a problem which I call the “boiling frog problem.” According to the metaphor, if you boil a frog by putting it in scalding hot water, it will try to save itself. If you put the frog in a pot of tepid water, it will not notice it is boiling so it will not try to save itself. In both cases, the outcome is the same—the frog dies. In a similar fashion, the combination of factors I identify herein, over time, gives rise to unhealthy engagement with chatbots and ultimately, to diminished human agency. (shrink)

The rapid development of artificial womb technologies means that we must consider if and when it is permissible to kill the human subject of ectogestation—recently termed a ‘gestateling’ by Elizabeth Chloe Romanis—prior to ‘birth’. We describe the act of deliberately killing the gestateling as gestaticide, and argue that there are good reasons to maintain that gestaticide is morally equivalent to infanticide, which we consider to be morally impermissible. First, we argue that gestaticide is harder to justify than abortion, primarily (...) because the gestateling is completely independent of its biological parents. Second, we argue that gestaticide is morally equivalent to infanticide. To demonstrate this, we explain that gestatelings are born in a straightforward sense, which entails that killing them is infanticide. However, to strengthen our overall claim, we also show that if gestatelings are not considered to have been born, killing them is still equivalent to killing neonates with congenital anomalies and disabilities, which again is infanticide. We conclude by considering how our discussion of gestaticide has implications for the permissibility of withdrawing life-sustaining treatment from gestatelings. (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)

This paper discusses the epistemology of the Internet of Things [IoT] by focusing on the topic of trust. It presents various frameworks of trust, and argues that the ethical framework of trust is what constitutes our responsibility to reveal desired norms and standards and embed them in other frameworks of trust. The first section briefly presents the IoT and scrutinizes the scarce philosophical work that has been done on this subject so far. The second section suggests that the field (...) of epistemology is not sufficiently capable of dealing with technologies, and presents a possible solution to this problem. It is argued that knowledge is not only social phenomena, but also a technological one, and that in order to address epistemological issues in technology, we need to carefully depart from traditional epistemic analysis and form a new approach that is technological (termed here Techno-Epistemology). The third and fourth sections engage in an epistemic analysis of trust by dividing it in to various frameworks. The last section argues that these various frameworks of trust can be understood to form a trustworthy large-scale socio-technological system, emphasizing the place of ethical trust as constituting our commitment to give proper accounts for all of the other frameworks. (shrink)

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...) comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the fi nal part of the paper further directions of research will be explored, trying to address current limitations and future challenges. (shrink)