This paper presents the main topics, arguments, and positions in the philosophy of AI at present (excluding ethics). Apart from the basic concepts of intelligence and computation, the main topics of ar-tificial cognition are perception, action, meaning, rational choice, free will, consciousness, and normativity. Through a better understanding of these topics, the philosophy of AI contributes to our understand-ing of the nature, prospects, and value of AI. Furthermore, these topics can be understood more deeply through the discussion of AI; so (...) we suggest that “AI Philosophy” provides a new method for philosophy. (shrink)

The medium‐independence of computational descriptions has shaped common conceptions of computational explanation. So long as our goal is to explain how a system successfully carries out its computations, then we only need to describe the abstract series of operations that achieve the desired input–output mapping, however they may be implemented. It is argued that this abstract conception of computational explanation cannot be applied to so‐called real‐time computing systems, in which meeting temporal deadlines imposed by the systems with which a device (...) interfaces are constitutive of the computing tasks that a device performs. Instead, real‐time computing reveals the need for alternative conceptions of computational explanation, as well as computational implementation, that eschew medium‐independence. (shrink)

I aim to show that, contrary to standard deflationary or eliminativist theories of the self, we can argue from the phenomenology of pre-reflective self-awareness for the thesis that subjects of experience are substances. The phenomenological datum of subjectivity points to a specific metaphysical structure of our experience, that is, towards the substance view rather than the bundle or the minimal self view. Drawing on modern philosophical accounts of pre-reflective self-awareness, mineness and (self-) acquaintance, I will argue that a subject is (...) aware of being the one individual who has many experiences and that it is revealed to the subject that it is the bearer of experiences and their unifier. The subject is present in pre-reflective awareness and known as the subject of experiences, and even this minimal self-awareness gives us reason to favour the substance view. Thus, one can demonstrate how the debates on the phenomenology of pre-reflective self-awareness and the metaphysics of selfhood intersect. (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)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Nowadays, philosophers and scientists tend to agree that, even though human and artificial intelligence work quite differently, they can still illuminate aspects of each other, and knowledge in one domain can inspire progress in the other. For instance, the notion of “artificial” or “synthetic” phenomenology has been gaining some traction in recent AI research. In this paper, we ask the question: what (if anything) is the use of thinking about phenomenology in the context of AI, and in particular machine learning? (...) We will isolate one sense of “phenomenology”, namely the sense in which it is commonly understood within analytic philosophy of perception. Then, we will give examples of projects within sensory substitution and restoration science that rely heavily on machine learning and to which, according to us, phenomenology in the sense specified makes a relevant contribution. Finally, we will shed some light on what this contribution looks like and why it is important. (shrink)

The book’s core argument is that an artificial intelligence that could equal or exceed human intelligence—sometimes called artificial general intelligence (AGI)—is for mathematical reasons impossible. It offers two specific reasons for this claim: Human intelligence is a capability of a complex dynamic system—the human brain and central nervous system. Systems of this sort cannot be modelled mathematically in a way that allows them to operate inside a computer. In supporting their claim, the authors, Jobst Landgrebe and Barry Smith, marshal evidence (...) from mathematics, physics, computer science, philosophy, linguistics, and biology, setting up their book around three central questions: What are the essential marks of human intelligence? What is it that researchers try to do when they attempt to achieve "artificial intelligence" (AI)? And why, after more than 50 years, are our most common interactions with AI, for example with our bank’s computers, still so unsatisfactory? Landgrebe and Smith show how a widespread fear about AI’s potential to bring about radical changes in the nature of human beings and in the human social order is founded on an error. There is still, as they demonstrate in a final chapter, a great deal that AI can achieve which will benefit humanity. But these benefits will be achieved without the aid of systems that are more powerful than humans, which are as impossible as AI systems that are intrinsically "evil" or able to "will" a takeover of human society. (shrink)

“Rationality” in Simon's “bounded rationality” is the principle that humans make decisions on the basis of step-by-step reasoning using systematic rules of logic to maximize utility. “Bounded rationality” is the observation that the ability of a human brain to handle algorithmic complexity and large quantities of data is limited. Bounded rationality, in other words, treats a decision maker as a machine carrying out computations with limited resources. Under the principle of embodied cognition, a cognitive mind is an interactive machine. Turing-Church (...) computations are not interactive, and interactive machines can accomplish things that no Turing-Church computation can accomplish. Hence, if “rationality” is computation, and “bounded rationality” is computation with limited complexity, then “embodied bounded rationality” is both more limited than computation and more powerful. By embracing interaction, embodied bounded rationality can accomplish things that Turing-Church computation alone cannot. Deep neural networks, which have led to a revolution in artificial intelligence, are both interactive and not fundamentally algorithmic. Hence, their ability to mimic some cognitive capabilities far better than prior algorithmic techniques based on symbol manipulation provides empirical evidence for the principle of embodied bounded rationality. (shrink)

Starting in the 1950s, computer programs for simulating cognitive processes and intelligent behaviour were the hallmark of Good Old-Fashioned Artificial Intelligence and ‘cognitivist’ cognitive science. This article examines a somewhat neglected case of simulation pursued by one of the founding fathers of simulation methodology, Herbert A. Simon. In the 1970s and 1980s, Simon had repeated contacts with Marxist countries and scientists, in the context of which he advanced the idea that cognitivism could be used as a framework for simulating dialectical (...) materialism. Simon's idea was, in particular, to represent dialectical processes through a ‘symbolic’ version of dialectical logic. This article explores the context of Simon's interaction with Marxist countries—China and the USSR—and also assesses the outcome of the simulation. The difficulty with simulating distinctive features of dialectical materialism is read in light of the underlying assumptions of cognitivism and, ultimately, in light of the attempt to tame a rival world view. (shrink)

According to the semantic view of computation, computations cannot be individuated without invoking semantic properties. A traditional argument for the semantic view is what we shall refer to as the argument from the cognitive science practice. In its general form, this argument rests on the idea that, since cognitive scientists describe computations (in explanations and theories) in semantic terms, computations are individuated semantically. Although commonly invoked in the computational literature, the argument from the cognitive science practice has never been discussed (...) in detail. In this paper, we shall provide a critical reconstruction of this argument and an extensive analysis of its prospects, taking into account some ways of defending it that have never been explored so far. We shall argue that explanatory considerations support at best a weak version of the argument from the cognitive science practice, according to which semantic properties concur with formal syntactic properties in individuating computations in cognitive science, but not a strong version, according to which computation individuation in cognitive science is semantic as opposed to formal syntactic. (shrink)

Is it possible to build a conscious machine, an artifact that has qualitative experiences such as feeling pain, seeing the redness of a flower or enjoying the taste of coffee ? What makes such experiences conscious is their phenomenal character: it is like something to have such experiences. In contemporary philosophy of mind, the question of the qualitative aspect of conscious experiences is often addressed in terms of qualia. In a pre-theoretical and intuitive sense, qualia refer to the phenomenal character (...) or “what-it’s-like-ness” of a conscious experience: the painfulness of a pain experience or the redness of a visual experience of a red object. The problem of artificial qualia is the question of the theoretical possibility of the realization of qualia by artifacts. In this work, this problem will serve as a methodological tool to address what is arguably the most fundamental question of philosophy of mind: what exactly is consciousness? (shrink)

Throughout what is now the more than 50-year history of the computer many theories have been advanced regarding the contribution this machine would make to changes both in the structure of society and in ways of thinking. Like other theories regarding the future, these should also be taken with a pinch of salt. The history of the development of computer technology contains many predictions which have failed to come true and many applications that have not been foreseen. While we must (...) reserve judgment as to the question of the impact on the structure of society and human thought, there is no reason to wait for history when it comes to the question: what are the properties that could give the computer such far-reaching importance? The present book is intended as an answer to this question. The fact that this is a theoretical analysis is due to the nature of the subject. No other possibilities are available because such a description of the properties of the computer must be valid for any kind of application. An additional demand is that the description should be capable of providing an account of the properties which permit and limit these possible applications, just as it must make it possible to characterize a computer as distinct from a) other machines whether clocks, steam engines, thermostats, or mechanical and automatic calculating machines, b) other symbolic media whether printed, mechanical, or electronic and c) other symbolic languages whether ordinary languages, spoken or written or formal languages. This triple limitation, however, (with regard to other machines, symbolic media and symbolic languages) raises a theoretical question as it implies a meeting between concepts of mechanical-deterministic systems, which stem from mathematical physics, and concepts of symbolic systems which stem from the description of symbolic activities common to the humanities. The relationship between science and the humanities has traditionally been seen from a dualistic perspective, as a relationship between two clearly separate subject areas, each studied on its own set of premises and using its own methods. In the present case, however, this perspective cannot be maintained since there is both a common subject area and a new - and specific - kind of interaction between physical and symbolic processes. (shrink)

One of the strands of the Transhumanist movement, Singulitarianism, studies the possibility that high-level artificial intelligence may be created in the future, debating ways to ensure that the interaction between human society and advanced artificial intelligence can occur safely and beneficially. But how can we guarantee this safe interaction? Are there any indications that a Singularity may be on the horizon? In trying to answer these questions, We'll make a small introduction to the area of security research in artificial intelligence. (...) We'll review some of the current paradigms in the development of autonomous intelligent systems and evidence that we can use to prospect the coming of a possible technological Singularity. Finally, we will present a reflection using the COVID-19 pandemic, something that showed that our biggest problem in managing existential risks is our lack of coordination skills as a global society. (shrink)

A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case was theoretically (...) based on the idiosyncratic notions of symbol systems and the representational abilities they give rise to, in particular with respect to knowledge. While focusing on the period 1956-1982, this review cites both earlier and later literature and it attempts to make visible their potential relevance to today's greatest unifying AI challenge, to wit, the design of wholly autonomous artificial agents (a.k.a. robots) that are not only rational and ethical, but also self-conscious. (shrink)

This thesis studies the role of conceptual content in inference and reasoning. The first two chapters offer a theoretical and historical overview of the relation between inference and meaning in philosophy and psychology. In particular, a critical analysis of the formality thesis, i.e., the idea that rational inference is a rule-based and topic-neutral mechanism, is advanced. The origins of this idea in logic and its influence in philosophy and cognitive psychology are discussed. Chapter 3 consists of an analysis of the (...) relationship between inference and representation. It is argued that inference has to be studied from a pluralistic per- spective due to its dependence on different formats of representing information. The following four chapters apply conceptual spaces, a formal theory of concepts within cognitive semantics, to three concept-based inference-types. First, an explication of Sellars notion of material inference is advanced. Later, the model is extended to account for nonmonotonic inference by studying the role expectations in reasoning. Finally, a conceptual space-model of category-based induction is presented. This model predicts most of the empirical properties of this psychological phenomenon and subsumes some of the previous theories in psychology. It is stated that the explanatory fruitfulness of this new approach is evidence for the failure of the formality thesis and calls for a unified model of rational inference that puts semantics at center stage. The last chapter of the thesis discusses how inference and concepts interact in scientific reasoning, which makes constant use of hybrid symbolic structures for representing conceptual information. Stephen Toulmin’s notions of method of representation and inferential technique are developed and applied in a case study about the emergence of the notion of instantaneous speed during the passage from geometrical physics to analytical mechanics. It is claimed that this analysis provides support to the pluralistic perspective for theorizing about reasoning. (shrink)

I develop an interdisciplinary framework for understanding the nature of agents and agency that is compatible with recent developments in the metaphysics of science and that also does justice to the mechanistic and normative characteristics of agents and agency as they are understood in moral philosophy, social psychology, neuroscience, robotics, and economics. The framework I develop is internal perspectivalist. That is to say, it counts agents as real in a perspective-dependent way, but not in a way that depends on an (...) external perspective. Whether or not something counts as an agent depends on whether it is able to have a certain kind of perspective. My approach differs from many others by treating possession of a perspective as more basic than the possession of agency, representational content/vehicles, cognition, intentions, goals, concepts, or mental or psychological states; these latter capabilities require the former, not the other way around. I explain what it means for a system to be able to have a perspective at all, beginning with simple cases in biology, and show how self-contained normative perspectives about proper function and control can emerge from mechanisms with relatively simple dynamics. I then describe how increasingly complex control architectures can become organized that allow for more complex perspectives that approach agency. Next, I provide my own account of the kind of perspective that is necessary for agency itself, the goal being to provide a reference against which other accounts can be compared. Finally, I introduce a crucial distinction that is necessary for understanding human agency: that between inclinational and committal agency, and venture a hypothesis about how the normative perspective underlying committal agency might be mechanistically realized. (shrink)

Decision-making has traditionally been modelled as a serial process, consisting of a number of distinct stages. The traditional account assumes that an agent first acquires the necessary perceptual evidence, by constructing a detailed inner repre- sentation of the environment, in order to deliberate over a set of possible options. Next, the agent considers her goals and beliefs, and subsequently commits to the best possible course of action. This process then repeats once the agent has learned from the consequences of her (...) actions and subsequently updated her beliefs. Under this interpretation, the agent’s body is considered merely as a means to report the decision, or to acquire the relevant goods. However, embodied cognition argues that an agent’s body should be understood as a proper part of the decision-making pro- cess. Accepting this principle challenges a number of commonly held beliefs in the cognitive sciences, but may lead to a more unified account of decision-making. This thesis explores an embodied account of decision-making using a recent frame- work known as predictive processing. This framework has been proposed by some as a functional description of neural activity. However, if it is approached from an embodied perspective, it can also offer a novel account of decision-making that ex- tends the scope of our explanatory considerations out beyond the brain and the body. We explore work in the cognitive sciences that supports this view, and argue that decision theory can benefit from adopting an embodied and predictive perspective. (shrink)

1. Introduction; 2. Indexical self-awareness; 3. Detached self-awareness; 4. Social self-awareness; 5. Basic social self-awareness; 6. Developed social self-awareness; 7. Person-consciousness.

Of primary importance in formulating a response to the increasing prevalence and power of artificial intelligence (AI) applications in society are questions of ontology. Questions such as: What “are” these systems? How are they to be regarded? How does an algorithm come to be regarded as an agent? We discuss three factors which hinder discussion and obscure attempts to form a clear ontology of AI: (1) the various and evolving definitions of AI, (2) the tendency for pre-existing technologies to be (...) assimilated and regarded as “normal,” and (3) the tendency of human beings to anthropomorphize. This list is not intended as exhaustive, nor is it seen to preclude entirely a clear ontology, however, these challenges are a necessary set of topics for consideration. Each of these factors is seen to present a 'moving target' for discussion, which poses a challenge for both technical specialists and non-practitioners of AI systems development (e.g., philosophers and theologians) to speak meaningfully given that the corpus of AI structures and capabilities evolves at a rapid pace. Finally, we present avenues for moving forward, including opportunities for collaborative synthesis for scholars in philosophy and science. (shrink)

SUMMARY Major theories of philosophical psychology and philosophy of mind are examined on the basis of the fundamental questions of ontology, metaphysics, epistemology, semantics and logic. The result is the choice between language of eliminative reductionism and dualism, neither of which answers properly the relation between mind and body. In the search for a non–dualistic and non–reductive language, Wittgenstein’s notion of language–games as the representative links between language and the world is considered together with Peirce’s semeiosis of cognition. The result (...) is a redefined notion of cognition as the goal–oriented ability to instantiate strategies from the rules of the language–games. Cognition is considered within the four-dimensional languagegames: rules/strategies, primitive/complete language-games, family-resemblances/forms of life, and non-temporal continuum as the language-games’ modality. Cognition is seen as a continuum process of non–individualized elements that unites perception, volition, emotion, will and thought against the commonly accepted understandings of cognition in exclusive terms of perception and thinking. The alternative to the mind/body problem is the continuity of cognition where rules and strategies, syntax and semantics, are considered inseparable within language–games. This continuity of cognition is explained in terms of virtual (univocal) identity, replacing the old philosophical paradigm of the mind/body problem. -/- SOMMARIO Le teorie maggiori della psicologia filosofica e filosofia della mente sono esaminate a partire dalle questioni fondamentali di ontologia, metafisica, epistemologia, semantica e logica. Il risultato è la scelta tra un linguaggio riduzionista eliminativo e un dualismo che però non confronta in modo adeguato la relazione tra mente e corpo. Nella ricerca per un linguaggio non-dualistico e non-riduttivo, viene considerata la nozione del gioco linguistico di Wittgenstein come un legame rappresentativo tra il linguaggio e il mondo insieme con la semiosi cognitiva di Peirce. Il risultato è la nozione ridefinita della cognizione come abilità goal-orientata di creare le strategie dalle regole dei giochi linguistici. La cognizione è considerata entro le quattro dimensioni dei giochi linguistici: regole/strategie, giochi linguistici primitivi/completi, somiglianze di famiglia/forme di vita, e la continuità atemporale come la modalità dei giochi linguistici. La cognizione diventa un processo continuo degli elementi non-individualizzati che tende ad unificare la percezione, volizione, emozione, la volontà e il pensiero contro un concetto generalmente accettato di vedere la cognizione nei termini esclusivamente di percezione e di pensiero. L’alternativa al problema della mente/corpo è la continuità della cognizione, dove le regole e le strategie, la sintassi e la semantica, sono considerate inseparabili nei giochi linguistici. Questa continuità della cognizione è spiegata nei termini dell’identità virtuale (univoca) sostituendo un paradigma filosofico previo del problema della mente/corpo. (shrink)

A response to a recent critique by Cem Bozşahin of the theory of syntactic semantics as it applies to Helen Keller, and some applications of the theory to the philosophy of computer science.

The mainstream view in cognitive science is that computation lies at the basis of and explains cognition. Our analysis reveals that there is no compelling evidence or argument for thinking that brains compute. It makes the case for inverting the explanatory order proposed by the computational basis of cognition thesis. We give reasons to reverse the polarity of standard thinking on this topic, and ask how it is possible that computation, natural and artificial, might be based on cognition and not (...) the other way around. (shrink)

Cultural psychologists often describe the relationship between mind and culture as ‘dynamic.’ In light of this, we provide two desiderata that a theory about encultured minds ought to meet: the theory ought to reflect how cultural psychologists describe their own findings and it ought to be thoroughly naturalistic. We show that a realist theory of causal powers — which holds that powers are causally-efficacious and empirically-discoverable — fits the bill. After an introduction to the major concepts in cultural psychology and (...) describing causal power realism, we use a case study — the effects of pathogen prevalence on culture and cognition — to show the explanatory capacities of the powers framework. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

A large body of compelling evidence has been accumulated demonstrating that embodiment – the agent’s physical setup, including its shape, materials, sensors and actuators – is constitutive for any form of cognition and as a consequence, models of cognition need to be embodied. In contrast to methods from empirical sciences to study cognition, robots can be freely manipulated and virtually all key variables of their embodiment and control programs can be systematically varied. As such, they provide an extremely powerful tool (...) of investigation. We present a robotic bottom-up or developmental approach, focusing on three stages: (a) low-level behaviors like walking and reflexes, (b) learning regularities in sensorimotor spaces, and (c) human-like cognition. We also show that robotic based research is not only a productive path to deepening our understanding of cognition, but that robots can strongly benefit from human-like cognition in order to become more autonomous, robust, resilient, and safe. (shrink)

An action-oriented perspective changes the role of an individual from a passive observer to an actively engaged agent interacting in a closed loop with the world as well as with others. Cognition exists to serve action within a landscape that contains both. This chapter surveys this landscape and addresses the status of the pragmatic turn. Its potential influence on science and the study of cognition are considered (including perception, social cognition, social interaction, sensorimotor entrainment, and language acquisition) and its impact (...) on how neuroscience is studied is also investigated (with the notion that brains do not passively build models, but instead support the guidance of action). A review of its implications in robotics and engineering includes a discussion of the application of enactive control principles to couple action and perception in robotics as well as the conceptualization of system design in a more holistic, less modular manner. Practical applications that can impact the human condition are reviewed (e.g., educational applications, treatment possibilities for developmental and psychopathological disorders, the development of neural prostheses). All of this foreshadows the potential societal implications of the pragmatic turn. The chapter concludes that an action-oriented approach emphasizes a continuum of interaction between technical aspects of cognitive systems and robotics, biology, psychology, the social sciences, and the humanities, where the individual is part of a grounded cultural system. (shrink)

In this paper I assess the explanatory role of internal representations in connectionist models of cognition. Focusing on both the internal‘hidden’units and the connection weights between units, I argue that the standard reasons for viewing these components as representations are inadequate to bestow an explanatorily useful notion of representation. Hence, nothing would be lost from connectionist accounts of cognitive processes if we were to stop viewing the weights and hidden units as internal representations.

In a recent article, Fridland characterises a central capacity of skill users, an aspect she calls ‘control’. Control, according to Fridland, is evidenced in the way in which skill users are able to marshal a variety of mental and bodily resources in order to keep skill deployment operating fluidly and appropriately. According to Fridland, two prevalent contemporary accounts of skill—Stanley & Krakauer’s and Hubert Dreyfus’s —fail to account for the features of control, and do so necessarily. While I agree with (...) Fridland that features of control represent desiderata for a satisfactory characterisation of the capacity of skills to respond to perturbations, I argue that her account is limited in two ways; first it is applicable only to a particular class of skills I call motor skills, leaving other classes of skills unaccounted for; second, she employs a problematic distinction that rules out the automatic and pre-reflective use of discursive, propositional cues in skill deployment. I put forward a critical elaboration of Fridland’s account based on two more general characteristic features of skills I call opportunistic robustness and normative sensitivity. I suggest that these features avoid the difficulties isolated, while preserving the substance of Fridland’s account of control. (shrink)

The traditional view of symbol grounding seeks to connect an a priori internal representation or ‘form’ to its external referent. But such a ‘form’ is usually itself systematically composed out of more primitive parts, so this view ignores its grounding in the physics of the world. Some previous work simulating multiple talking/listening agents has effectively taken this stance, and shown how a shared discrete speech code can emerge. Taking the earlier work of Oudeyer, we have extended his model to include (...) a dispersive force intended to account broadly for a speaker’s motivation to increase auditory distinctiveness. New simulations show that vowel systems result that are more representative of the range seen in human languages. These simulations make many profound abstractions and assumptions. Relaxing these by including more physically and physiologically realistic mechanisms for talking and listening is seen as the key to replicating more complex and dynamic aspects of speech, such as consonant-vowel patterning. (shrink)

The paper proposes that gender can be used to explore alternative epistemologies represented within AI systems. Current research on feminist epistemology is reviewed then criticisms of the main philosophical position dominant in AI are outlined. These criticisms say little about epistemology and nothing about gender. It is suggested that the way forward might be found within the sociology of scientific knowledge as its approach is in accord with the postmodernist view of feminist epistemology in seeing knowledge as a cultural product. (...) However, the sociology of knowledge must brush up its feminist credentials and feminist epistemology must supply more concrete examples with which to test out our theories in AI. (shrink)

The current understanding of the important role of information or knowledge retrieval to artificial intelligence is not adequate. In this article, three study areas related to retrieval are summarized and commented. The need for a more systematic and unified way of studying retrieval and intelligence is argued. A brief outline for future study is suggested, some philosophical and social implications of this study are also discussed.

Artificial Intelligence, and the cognitivist view of mind on which it is based, represent the last stage of the rationalist tradition in philosophy. This tradition begins when Socrates assumes that intelligence is based on principles and when Plato adds the requirement that these principles must be strict rules, not based on taken-for-granted background understanding. This philosophical position, refined by Hobbes, Descartes and Leibniz, is finally converted into a research program by Herbert Simon and Allen Newell. That research program is now (...) in trouble, so we must return to its source and question Socrates' assumption that intelligence consists in solving problems by following rules, and that one acquires the necessary rules by abstracting them from specific cases. A phenomenological description of skill acquisition suggests that the acquisition of expertise moves in just the opposite direction: from abstract rules to particular cases. This description of expertise accounts for the difficulties that have confronted AI for the last decade. (shrink)

Distributed cognition (d-cog) claims that many cognitive processes are distributed across groups and the surrounding material and cultural environment. Recently, Nancy Nersessian, Ronald Giere, and others have suggested that a d-cog approach might allow us to bring together cognitive and social theories of science. I explore this idea by focusing on the specific interpretation of d-cog found in Edwin Hutchins' canonical text Cognition in the wild. First, I examine the scope of a d-cog approach to science, showing that there are (...) important disputes between cognitive and social theorists on which d-cog remains silent. Second, I suggest that, where social explanations can be recast in d-cog terms, this reformulation will not be acceptable to all social theorists. Finally, I ask how we should make sense of the claim that, on a d-cog analysis, social factors are cognitive factors. (shrink)

What is nontrivial digital computation? It is the processing of discrete data through discrete state transitions in accordance with finite instructional information. The motivation for our account is that many previous attempts to answer this question are inadequate, and also that this account accords with the common intuition that digital computation is a type of information processing. We use the notion of reachability in a graph to defend this characterization in memory-based systems and underscore the importance of instructional information for (...) digital computation. We argue that our account evaluates positively against adequacy criteria for accounts of computation. (shrink)

The frame problem was originally a problem for Artificial Intelligence, but philosophers have interpreted it as an epistemological problem for human cognition. As a result of this reinterpretation, however, specifying the frame problem has become a difficult task. To get a better idea of what the frame problem is, how it gives rise to more general problems of relevance, and how deep these problems run, I expound six guises of the frame problem. I then assess some proposed heuristic solutions to (...) the frame problem; I show that these proposals misunderstand, and fail to address, an important aspect of the frame problem. Finally, I argue that though human cognition does not solve the frame problem in its epistemological guise, human cognition avoids some of the epistemological worries. (shrink)

The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...) none of these kinds of evidence can be relevant to a choice among competing computational models unless there are in fact type-type correlations between the states postulated by computational models and brain states. Thus, I conclude, research into the computational procedures employed in human cognition must be conducted hand-in-hand with research into the brain processes which realize those procedures. (shrink)

Deliberate contextual vocabulary acquisition (CVA) is a reader’s ability to figure out a (not the) meaning for an unknown word from its “context”, without external sources of help such as dictionaries or people. The appropriate context for such CVA is the “belief-revised integration” of the reader’s prior knowledge with the reader’s “internalization” of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new (...) classroom curriculum designed to help students use CVA to improve their reading comprehension. (shrink)

The extended mind hypothesis (EMH) is the claim that the mind can and does extend beyond the human body. Adams and Aizawa (A&A) contend that arguments for EMH commit a ‘coupling constitution fallacy’. We deny that the master argument for EMH commits such a fallacy. But we think that there is an important question lurking behind A&A's allegation: under what conditions is cognition spread across a tightly coupled system? Building on some suggestions from Haugeland, we contend that the system must (...) exhibit a distinctive sort of semantic activity, semantic activity that the system as a whole takes responsibility for. (shrink)

We can readily identify goal-directed systems and distinguish them from non-goal-directed systems. A woodpecker hunting for grubs is the first, a pendulum returning to rest is the second. But what is it to be a goal-directed system? Perhaps the dominant answer to this question, inspired by systems theories such as cybernetics, is that goal-directed systems are distinguished by their tendency to seek, aim at, or maintain some more-or-less easily identifiable goal. Cybernetics and the like would hold that physical systems subject (...) only to physical laws can exhibit such behavior. If sound, this systems approach to teleology would unify a diverse range of goal-directed phenomena and neatly side-step many traditional bogey-men of teleology, such as anthropomorphism and future causation. Goal-directed phenomena would be a normal feature of the natural causal world that could be described in purely descriptive and quantitative terms, and receive ordinary causal explanations. Thus, the systems approach promises to provide a naturalistic-cum-descriptive account of teleology suitable for use in naturalistic accounts of other phenomena, including the intentionality of mental states and even self-consciousness. (shrink)