I suggest that most discussions of intentional systems have overlooked an important aspect of living organisms: the intrinsic goal-directedness inherent in the behaviour of living eukaryotic cells. This goal directedness is nicely displayed by a normal cell’s ability to rearrange its own local material structure in response to damage, nutrient distribution or other aspects of its individual experience. While at a vastly simpler level than intentionality at the human cognitive level, I propose that this basic capacity of living things provides (...) a necessary building block for cognition and high-order intentionality, because the neurons that make up vertebrate brains, like most cells in our body, embody such capacities. I provisionally dub the capacities in question “nano-intentionality”: a microscopic form of “aboutness”. The form of intrinsic intentionality I propose is thoroughly materialistic, fully compatible with known biological facts, and derived non-mysteriously through evolution. Crucially, these capacities are not shared by any existing computers or computer components, and thus provide a clear, empirically-based distinction between brains and currently existing artificial information processing systems. I suggest that an appreciation of this aspect of living matter provides a potential route out of what may otherwise appear to be a hopeless philosophical quagmire confronting information-processing models of the mind. (shrink)

This paper aims to show that dominant conceptions of intelligence used in artificial intelligence (AI) are biased by normative assumptions that originate from the Global North, making it questionable if AI can be uncritically applied elsewhere without risking serious harm to vulnerable people. After the introduction in Sect. 1 we shortly present the history of IQ testing in Sect. 2, focusing on its multiple discriminatory biases. To determine how these biases came into existence, we define intelligence ontologically and underline its (...) constructed and culturally variable character. Turning to AI, specifically the Turing Test (TT), in Sect. 3, we critically examine its underlying intelligence conceptions. The test has been of central influence in AI research and remains an important point of orientation. We argue that both the test itself and how it is used in practice risk promoting a limited conception of intelligence which solely originated in the Global North. Hence, this conception should be critically assessed in relation to the different global contexts in which AI technologies are and will be used. In Sect. 4, we highlight how unequal power relations in AI research are a real threat, rather than just philosophical sophistry while considering the history of IQ testing and the TT’s practical biases. In the last section, we examine the limits of our account and identify fields for further investigation. Tracing colonial continuities in AI intelligence research, this paper points to a more diverse and historically aware approach to the design, development, and use of AI. (shrink)

The relationship between philosophy and research on artificial intelligence (AI) has been difficult since its beginning, with mutual misunderstanding and sometimes even hostility. By contrast, we show how an approach informed by both philosophy and AI can be productive. After reviewing some popular frameworks for computation and learning, we apply the AI methodology of “build it and see” to tackle the philosophical and psychological problem of characterizing perception as distinct from sensation. Our model comprises a network of very simple, but (...) interacting agents which have binary experiences of the “yes/no”-type and communicate their experiences with each other. When does such a network refer to a single agent instead of a distributed network of entities? We apply machine learning techniques to address the following related questions: i) how can the model explain stability of compound entities, and ii) how could the model implement a single task such as perceptual inference? We thereby find consistency with previous work on “interface” strategies from perception research. While this reflects some necessary conditions for the ascription of agency, we suggest that it is not sufficient. Here, AI research, if it is intended to contribute to conceptual understanding, would benefit from issues previously raised by philosophy. We thus conclude the article with a discussion of action-selection, the role of embodiment, and consciousness to make this more explicit. We conjecture that a combination of AI research and philosophy allows general principles of mind and being to emerge from a “quasi-empirical” investigation. (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.

In this article I defend two theses. The first is that the centrality of recording in the social world is manifested through the production of documents, a phenomenon which has been present since the earliest phases of society and which has undergone an exponential growth through the technological developments of the last decades. The second is that the centrality of documents leads to a view of normativity according to which human beings are primarily passive receptors of rules manifested through documents. (...) We are not intentional producers of values. The latter, as I shall suggest in my conclusion, should be viewed as being ‘socially dependent’ rather than ‘socially constructed’. (shrink)

Perhaps personality traits substantially influence one’s philosophically relevant intuitions. This suggestion is not only possible, it is consistent with a growing body of empirical research: Personality traits have been shown to be systematically related to diverse intuitions concerning some fundamental philosophical debates. We argue that this fact, in conjunction with the plausible principle that almost all adequate philosophical views should take into account all available and relevant evidence, calls into question some prominent approaches to traditional philosophical projects. To this end, (...) we present the Philosophical Personality Argument (PPA). We explain how it supports the growing body of evidence challenging some of the uses of intuitions in philosophy, and we defend it from some criticisms of empirically based worries about intuitions in philosophy. We conclude that the current evidence indicates that the PPA is sound, and thus many traditional philosophical projects that use intuitions must become substantially more empirically oriented. (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

This paper discusses the nature of consciousness’ intrinsic intentionality from a transcendental-phenomenological viewpoint. In recent philosophy of mind the essentially intentional character of consciousness has become obscured because the latter is predominantly understood in terms of “qualia” or the “what-it-is-like-ness” of mental states and it is hard to see why such subjective “feels”, of all things, could bestow states with objective reference. As the paper attempts to demonstrate, this is an inadequate understanding of consciousness, which should instead be defined in (...) terms of presence. Consciousness essentially takes place as presence-of, i.e., consists in something coming to appearance. This presence-of is not only a fundamental, irreducible phenomenon, but also in a radical sense un-naturalisable. Naturalism only knows “nature”, as the world of objects, and the question of intentionality then seems to be how certain inner-worldly objects can be “representations” of other inner-worldly objects. In fact, no object is ever intrinsically “about” anything. This is exclusively the nature of subjectivity qua consciousness, which is not an object alongside other objects but rather exists as the manifestation of objects. (shrink)

With recent advancements in technology and computer science, we have reached a point where we can clearly state that thinking is no longer the exclusive privilege of living minds. Artificial intelligence can gather and process information in a manner fairly similar or even superior to our thinking process. AI can use this processed information in a reasoning process to make decisions and execute them. However, what makes our mind distinct from AI is the addition of “I,” that is, an entity (...) composed of two mental processes of awareness-based choice selection and discretionary selection of information for attention that are the fundamental core function of decision making and awareness processes, respectively. ABCS allows the coexistence of free will alongside—but unrestrained by—the chain of causality in the decision-making process with the help of awareness. “I” also allows for DSIA, which is a key step in the awareness process. This intertwined process is crucial for our consciousness. Similar to AI, the mind is devoid of any form of self-recognition without awareness. Self-awareness is a collaborative function of “I” and the mind. “I” is instrumental in the sense of self-awareness, but on its own, it is selfless. Additionally, “I” is the heart of the decision-making process. Therefore, AI is missing “I,” a selfless master of the mind. (shrink)

Integrated Information Theory posits a new kind of information, which, given certain constraints, constitutes consciousness. Searle objects to IIT because its appeal to information relies on observer-relative features. This misses the point that IIT’s notion of integrated information is intrinsic, the opposite of observer-relative. Moreover, Searle overlooks the possibility that IIT could be embraced as an extension of his theory. While he insists that causal powers of the brain account for consciousness, he maintains that these causal powers aren’t tied to (...) protoplasmic material. Whatever these causal powers are, they don’t consist in mere information-processing or computation. IIT agrees, and also positively characterizes the relevant causal powers as those involved in generating integrated information. Examining the further commitments of each theory reveals that IIT renews a fundamentally ontological challenge to information-processing and computational theories of mind. (shrink)

Integrated Information Theory posits a new kind of information, which, given certain constraints, constitutes consciousness. Searle objects to IIT because its appeal to information relies on observer-relative features. This misses the point that IIT’s notion of integrated information is intrinsic, the opposite of observer-relative. Moreover, Searle overlooks the possibility that IIT could be embraced as an extension of his theory. While he insists that causal powers of the brain account for consciousness, he maintains that these causal powers aren’t tied to (...) protoplasmic material. Whatever these causal powers are, they don’t consist in mere information-processing or computation. IIT agrees, and also positively characterizes the relevant causal powers as those involved in generating integrated information. Examining the further commitments of each theory reveals that IIT renews a fundamentally ontological challenge to information-processing and computational theories of mind. (shrink)

The principle of indifference has fallen from grace in contemporary philosophy, yet some papers have recently sought to vindicate its plausibility. This paper follows suit. In it, I articulate a version of the principle and provide what appears to be a novel argument in favour of it. The argument relies on a thought experiment where, intuitively, an agent’s confidence in any particular outcome being true should decrease with the addition of outcomes to the relevant space of possible outcomes. Put simply: (...) the greater the number of outcomes, the weaker your confidence should be in any one of those outcomes. The argument holds that this intuition favours the principle of indifference. I point out that, in contrast, the intuition is also incompatible with a major alternative to the principle which prescribes imprecise credences: the so-called wide interval view. Consequently, the argument may also be seen as an argument against the wide interval view. (shrink)

I critically examine some provocative arguments that John Searle presents in his book The Rediscovery of Mind to support the claim that the syntactic states of a classical computational system are "observer relative" or "mind dependent" or otherwise less than fully and objectively real. I begin by explaining how this claim differs from Searle's earlier and more well-known claim that the physical states of a machine, including the syntactic states, are insufficient to determine its semantics. In contrast, his more recent (...) claim concerns the syntax, in particular, whether a machine actually has symbols to underlie its semantics. I then present and respond to a number of arguments that Searle offers to support this claim, including whether machine symbols are observer relative because the assignment of syntax is arbitrary, or linked to universal realizability, or linked to the sub-personal interpretive acts of a homunculus, or linked to a person's consciousness. I conclude that a realist about the computational model need not be troubled by such arguments. Their key premises need further support. (shrink)

I use Carl Gillett’s much heralded dimensioned theory of realization as a platform to develop a plausible part–whole theory. I begin with some basic desiderata for a theory of realization that its key terms should be defined and that it should be explanatory. I then argue that Gillett’s original theory violates these conditions because its explanatory force rests upon an unspecified “in virtue of” relation. I then examine Gillett’s later version that appeals instead to theoretical terms tied to “mechanisms.” Yet (...) I argue that it too violates the desiderata, since it defines realization for mechanisms in terms of two undefined ideas whose explanatory credentials have not been established—“implementation” and “grounds.” Thus I drop those ideas in favor of an explicit constraint that the parts and properties provide a mechanistic explanation. I also distinguish a special mechanistic theory from a preferred general theory that incorporates other kinds of part–whole explanations that target causal powers or capacities. The result is a theory that has the explanatory virtues of mechanistic theories as well as a broader scope desired by Gillett. I also compare the result to a similar idea from Robert Cummins that has been neglected in recent discussions of realization, namely, his general property analysis rather than his functional analysis. Finally, I defend the preferred general theory against possible objections that attempt to show a conflict between metaphysical demands on a theory of realization versus facts about good scientific explanation. (shrink)

It has recently become quite fashionable for one to defend a functionalist theory of mental states. However, as with most trends, functionalism has come under fire in certain philosophical quarters. In this paper, I shall not take up the question of whether any version of functionalism is true. I shall instead discuss a particular objection to a specific brand of functionalism.

It has recently become quite fashionable for one to defend a functionalist theory of mental states. However, as with most trends, functionalism has come under fire in certain philosophical quarters. In this paper, I shall not take up the question of whether any version of functionalism is true. I shall instead discuss a particular objection to a specific brand of functionalism.

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)

The commentary argues that we cannot be sure that human consciousness has survival value and that in order to understand the origins and, perhaps, the function of consciousness, we should examine the behavioural and neural precursors to consciousness in nonhumans. An example is given of research on the role of context in decisions regarding fleeing from probable predators in the Mongolian gerbil.

Gray's account is remarkable in its depth and scope but too little attention is paid to poor correspondences with the literature on hippocampal/subicular damage, the theta rhythm, and novelty detection. An alternative account, focusing on hippocampal involvement in organizing memories in a way that makes them accessible to conscious recollection but not in access to consciousness per se, avoids each of these limitations.

Introduction: representationalismMost theorists of cognition endorse some version of representationalism, which I will understand as the view that the human mind is an information-using system, and that human cognitive capacities are representational capacities. Of course, notions such as ‘representation’ and ‘information-using’ are terms of art that require explication. As a first pass, representations are “mediating states of an intelligent system that carry information” (Markman and Dietrich 2001, p. 471). They have two important features: (1) they are physically realized, and so (...) have causal powers; (2) they are intentional, in other words, they have meaning or representational content. This presumes a distinction between a representational vehicle—a physical state or structure that has causal powers and is responsible for producing behavior—and its content. Consider the following characterization of a device that computes the addition functionReaders will recognize the similarity t. (shrink)

This paper argues that many so-called digital technologies can be construed as notational technologies, explored through the example of Monegraph, an art and digital asset management platform built on top of the blockchain system originally developed for the cryptocurrency bitcoin. As the paper characterizes it, a notational technology is the performance of syntactic notation within a field of reference, a technologized version of what Nelson Goodman called a “notational system.” Notational technologies produce abstracted entities through positive and reliable, or constitutive, (...) tests of socially acceptable meaning. Accordingly, this account deviates from typical narratives of blockchains, instead demonstrating that blockchain technologies are effective at managing digital assets because they produce abstracted identities through the performance of notation. Since notational technologies rely on configurations of socially acceptable meaning, this paper also provides a philosophical account of how blockchain technologies are socially embedded. (shrink)

In his book The rediscovery of the mind John Searle claims that unconscious mental states (1) have first-person "aspectual shape", but (2) that their ontology is purely third-person. He attempts to eliminate the obvious inconsistency by arguing that the aspectual shape of unconscious mental states consists in their ability to cause conscious first-person states. However, I show that this attempted solution fails insofar as it covertly acknowledges that unconscious states lack the aspectual shape required for them to play a role (...) in psychological explanation. (shrink)

Roger Schank's research in AI takes seriously the ideas that understanding natural language involves mapping its expressions into an internal representation scheme and that these internal representations have a syntax appropriate for computational operations. It therefore falls within the computational approach to the study of mind. This paper discusses certain aspects of Schank's approach in order to assess its potential adequacy as a (partial) model of cognition. This version of the Language of Thought hypothesis encounters some of the same difficulties (...) that arise for Fodor's account. (shrink)