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

Edmund Husserl’s eidetic phenomenology seeks a priori knowledge of essences and eidetic laws pertaining to conscious experience and its objects. Husserl believes that such eidetic knowledge has a higher epistemic status than the inherently fallible empirical knowledge, but a closer reading of his work shows that even eidetic claims are subject to error and open to modification. In this article, I develop a self-correcting account of Husserl’s method of eidetic variation, arguing that eidetic variation plays a critical role in both (...) challenging and improving upon the eidetic results in phenomenology. More specifically, I argue that the self-correcting account of eidetic variation 1) is consistent with Husserl’s own formulations of his eidetic methodology and epistemic principles; 2) captures the dual epistemic function of eidetic variation as means for both testing and intuitively validating eidetic claims; and 3) offers methodological support for contemporary attempts to integrate eidetic variation with non-eidetic methods and resources. To substantiate these claims, I first contrast the self-correcting account with the falsificationist interpretations of eidetic variation. Then, I turn to three applications of eidetic variation in order to examine how eidetic phenomenology could draw from real-life deviations, artificial variations, and critical–historical reflection. The goal is to lay the methodological groundwork for a self-correcting and integrative account of eidetic variation and illustrate its usefulness in research practice. (shrink)

AI-based technologies are increasingly pervasive in a number of contexts. Our affective and emotional life makes no exception. In this article, we analyze one way in which AI-based technologies can affect them. In particular, our investigation will focus on affective artificial agents, namely AI-powered software or robotic agents designed to interact with us in affectively salient ways. We build upon the existing literature on affective artifacts with the aim of providing an original analysis of affective artificial agents and their distinctive (...) features. We argue that, unlike comparatively low-tech affective artifacts, affective artificial agents display a specific form of agency, which prevents them from being perceived by their users as extensions of their selves. In addition to this, we claim that their functioning crucially depends on the simulation of human-like emotion-driven behavior and requires a distinctive form of transparency—we call it emotional transparency—that might give rise to ethical and normative tensions. (shrink)

Advances in artificial intelligence (AI) create an increasing similarity between the performance of AI systems or AI-based robots and human communication. They raise the questions: whether it is possible to communicate with, understand, and even empathically perceive artificial agents; whether we should ascribe actual subjectivity and thus quasi-personal status to them beyond a certain level of simulation; what will be the impact of an increasing dissolution of the distinction between simulated and real encounters. (1) To answer these questions, the paper (...) argues that the precondition for actually understanding others consists in the implicit assumption of the subjectivity of our counterpart, which makes shared feelings and a „we-intentionality” possible. This assumption is ultimately based on the presupposition of a shared form of life, conceived here as „conviviality.” (2) The possibility that future artificial agents could meet these preconditions is refuted on the basis of embodied and enactive cognition, which links subjectivity and consciousness to the aliveness of an organism. (3) Even if subjectivity is in principle impossible for artificial agents, the distinction between simulated and real subjectivity might nevertheless become increasingly blurred. Here, possible consequences are discussed, especially using the example of virtual psychotherapy. Finally, the paper makes case for a mindful appproach to the language we use to talk about artificial systems and pleads for preventing a systematic pretense of subjectivity. (shrink)

Deacon develops a minimal model of a nonparasitic virus to explore how nucleotide sequences came to be characterized by a code-like informational at the origin of life. The model serves to problematize the concept of biological normativity because it highlights two common yet typically implicit assumptions: that life could consist as an inert form, were it not for extrinsic sources of physical instability, and that life could have originated as a singular self-contained individual. I propose that the origin of life, (...) the genetic code, and biological normativity more generally, lead us to reject this passive individualism. (shrink)

There is an overlooked similarity between three classic accounts of the conditions of object experience from three distinct disciplines. Sociology: the “inversion” that accompanies discovery in the natural sciences, as local causes of effects are reattributed to an observed object. Psychology: the “externalization” that accompanies mastery of a visual–tactile sensory substitution interface, as tactile sensations of the proximal interface are transformed into vision-like experience of a distal object. Biology: the “projection” that brings forth an animal’s Umwelt, as impressions on its (...) body’s sensory surfaces are reconfigured into perception of an external object. This similarity between the effects of scientific practice and interface-use on the one hand, and of sensorimotor interaction on the other, becomes intelligible once we accept that skillful engagement with instruments and interfaces constitutes a socio-material augmentation of our basic perceptual capacity. This enactive interpretation stands in contrast to anti-realism about science associated with constructivist interpretations of these three phenomena, which are motivated by viewing them as the internal mental construction of the experienced object. Instead, it favors a participatory realism: the sensorimotor basis of perceptual experience loops not only through our body, but also through the external world. This allows us to conceive of object experience in relational terms, i.e., as one or more subjects directly engaging with the world. Consequently, we can appreciate scientific observation in its full complexity: it is a socio-materially augmented process of becoming acquainted with the observed object that—like tool-use and perceiving more generally—is irreducibly self, other-, and world-involving. (shrink)

Meaning has traditionally been regarded as a problem for philosophers and psychologists. Advances in cognitive science since the early 1960s, however, broadened discussions of meaning, or more technically, the semantics of perceptions, representations, and/or actions, into biology and computer science. Here, we review the notion of “meaning” as it applies to living systems, and argue that the question of how living systems create meaning unifies the biological and cognitive sciences across both organizational and temporal scales.

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)

This draft preprint presents a nine step argument for “Connectionist Structuralism” (CS), an account of the ontology of abstract objects that is neither purely nominalist nor purely platonist. CS is a common, often implicit assumption in parts of the artificial intelligence literature, but such discussions have not presented formal accounts of the position or engaged with metaphysical issues that potentially undermine it. By making the position legible and presenting an initial case for it, we hope to support a constructive dialogue (...) between AI researchers and philosophers of metaphysics that helps both sides to refine the position. -/- CS proposes that each abstract object we can draw on in human analysis corresponds to a particular subset of an individual person’s brain structure whose functionality is isomorphic to a subset of the nodes and connections in a suitable connectionist network. In other words, abstract objects are physically realised, but in individual brains, rather than only in the referent objects (pure nominalism) or in metaphysical universals (pure platonism). -/- This paper’s minimum claim is that CS can account for all abstract object predicables regarding sensible properties, such as “is red” or “is a square”. Using evidence from cognitive neuroscience, machine learning, and evolutionary biology, as well as a fully traceable toy example, we describe how CS can support our core cognitive uses of such sensible properties and can account for our core phenomenal experiences of them. In the former, CS provides sufficient albeit imperfect inferential safety, whose limitations are argued to strengthen rather than weaken the case for CS as describing human behaviour. In the latter, four target phenomenal features are accounted for – abstract objects as feeling intangible, non-located, transparent, and unchanging - along with accounts for further phenomena such as semantic refinement, the Stroop effect, synaesthesia, semantic clarity, sensory overload, and satiation. -/- Our minimum claim concerns the day-to-day usage and felt experience of abstract objects, but we suggest also an extended claim in which CS can form the basis of a pragmatic sufficient logic. As such, the initial outline of a response is provided for five common objections to positions that seek to ground abstract objects without reference to metaphysically stand-alone universals: referential opacity; identity of indiscernibles; infinite regression; non-physical concepts; and necessary truths. These outlines lay the foundation for (but do not seek to formally demonstrate) an extended CS account that addresses other abstract objects, including issues relating to their use in mathematics and formal logic. -/- The CS account leads to a four-layer hierarchy of similarity for whether your “red” is the same as mine. Considering both semantic and phenomenal similarity, we conclude that our “reds” are likely non-identical but can be made close enough for practical purposes. Finally, we describe how future work could elaborate CS as a metaphysical project and how confidence in it could be tested through empirical research. (shrink)

Lack of coherence in concepts of intelligence has implications for artificial intelligence. ‘Intelligence’ is an abstraction grounded in human experience while supposedly freed from the embodiment that is the basis of that experience. In addition to physical instantiation, embodiment is a condition of dependency, of an autopoietic system upon an environment, which thus matters to the system itself. The autonomy and general capability sought in artificial general intelligence implies artificially re-creating the organism’s natural condition of embodiment. That may not be (...) feasible; and, if feasible, it may not be controllable or advantageous. (shrink)