Ioannis G. Ntagkas

This book provides a framework for thinking about foundational philosophical questions surrounding machine learning as an approach to artificial intelligence. Specifically, it links recent breakthroughs in deep learning to classical empiricist philosophy of mind. In recent assessments of deep learning's current capabilities and future potential, prominent scientists have cited historical figures from the perennial philosophical debate between nativism and empiricism, which primarily concerns the origins of abstract knowledge. These empiricists were generally faculty psychologists; that is, they argued that the active engagement of general psychological faculties-such as perception, memory, imagination, attention, and empathy-enables rational agents to extract abstract knowledge from sensory experience. This book explains a number of recent attempts to model roles attributed to these faculties in deep neural network based artificial agents by appeal to the faculty psychology of philosophers such as Aristotle, Ibn Sina (Avicenna), John Locke David Hume, William James, and Sophie de Grouchy. It illustrates the utility of this interdisciplinary connection by showing how it can provide benefits to both philosophy and computer science: computer scientists can continue to mine the history of philosophy for ideas and aspirational targets to hit on the way to more robustly rational artificial agents, and philosophers can see how some of the historical empiricists' most ambitious speculations can be realized in specific computational systems.

Libertarian views of freedom claim that, although determinism would rule out our freedom, we are nevertheless free on some occasions. An odd implication of such views (to put it mildly) seems to be that indeterminism somehow enhances or contributes to our agency. But how could that be? What does indeterminism have to offer agency? This paper develops a novel answer, one that is centred around the notion of explanation. In short, it is argued that, if indeterminism holds in the right places, then the best explanation of the history of the world necessarily cites facts about our agency. Along the way, alternative proposals regarding the significance of indeterminism are considered and, ultimately, rejected in favour of the one developed in this paper.

In “Study of Concepts”, Peacocke puts forward an argument for non-conceptualism derived from the possession conditions of observational concepts. In this paper, I raise two objections to this argument. First, I argue that if non-conceptual perceptual contents are scenario contents, then perceptual experiences cannot present perceivers with the circumstances specified by the application conditions of observational concepts and, therefore, they cannot play the semantic and epistemic roles Peacocke wants them to play in the possession conditions of these concepts. Second, I argue that if non-conceptual perceptual contents are protopropositions, then Peacocke’s account of the possession conditions of observational concepts falls into the Myth of the Given.

To make the case for non-conceptualism, Heck draws on an apparent dichotomy between linguistic and iconic representations. According to Heck, whereas linguistic representations have conceptual content, the content of iconic representations is non-conceptual. Based on the case of cartographic systems, the authors criticize Heck’s dichotomous distinction. They argue that maps are composed of semantically arbitrary elements that play different syntactic roles. Based on this, they claim that maps have a predicative structure and convey conceptual content. Finally, the authors argue that, despite their differences, maps and sentences can logically interact with each other through heterogeneous inferences. These considerations not only challenge the view that conceptual content and inferential processes necessarily involve linguistic representations; furthermore, they provide a new perspective for thinking about maps, their semantics and syntax, and their interaction with linguistic systems.

Philosophers have always tried to explain what concepts are. Currently, most neo- Fregean philosophers identify concepts with abilities peculiar to cognitive agents. Philosophers who defend a psychological view, in contrast, identify concepts with representations located in the mind. In this paper, I argue that concepts should be understood neither in terms of mental representations nor in terms of abilities. Concepts, I argue, are rules for sorting an inferring. To support this, I follow Ginsborg’s Kantian conception of concepts. Nevertheless, unlike Ginsborg, I provide an explanation of the cognitive relationship between concepts and thinkers that presupposes no linguistic awareness of any normative concept. In doing so, a dispositional approach to the normativity of concepts is proposed.

Realists about possible worlds typically identify possible worlds with abstract objects, such as propositions or properties. However, they face a significant objection due to Lewis (1986), to the effect that there is no way to explain how possible worlds-as-abstract objects represent possibilities. In this paper, I describe a response to this objection on behalf of realists. The response is to identify possible worlds with propositions, but to deny that propositions are abstract objects, or indeed objects at all. Instead, I argue that realists should follow Prior (1971) and others in treating higher-order quantification (e.g. quantification into predicate or sentence position) as a genuine form of quantification in its own right, and so in particular, not to be analysed in terms of first-order quantification over abstract objects. I argue that from this ‘higher-orderist’ perspective, there is a relatively straightforward answer to the question of how possible worlds-as-propositions succeed in representing possibilities.

According to constructive empiricists, accepting a scientific theory involves belief only that it is true of the observable world, where observability is defined in terms of what is detectable by the unaided senses. On this view, scientific instruments are machines that generate new observable data, but this data need not be interpreted as providing access to a realm of phenomena beyond what is revealed by the senses. A recent challenge to the constructive empiricist account of instruments appeals to the extended mind thesis, according to which cognitive processes are sometimes constituted not just by brain activity, but can extend into the rest of the body and the surrounding environment. If this is right, scientific instruments may, in the right circumstances, literally become part of our perceptual processes. In this article, I examine this extended perception argument, and I find that it fails for the vast majority of scientific instruments. Even if the extended mind thesis is accepted, the constructive empiricist can draw a line between observables and unobservables that makes very few concessions to the realist.

Data-intensive techniques, now widely referred to as 'big data', allow for novel ways to address complexity in science. I assess their impact on the scientific method. First, big-data science is distinguished from other scientific uses of information technologies, in particular from computer simulations. Then, I sketch the complex and contextual nature of the laws established by data-intensive methods and relate them to a specific concept of causality, thereby dispelling the popular myth that big data is only concerned with correlations. The modeling in data-intensive science is characterized as 'horizontal'—lacking the hierarchical, nested structure familiar from more conventional approaches. The significance of the transition from hierarchical to horizontal modeling is underlined by a concurrent paradigm shift in statistics from parametric to non-parametric methods.

Biomedical deployments of data science capitalise on vast, heterogeneous data sources. This promotes a diversified understanding of what counts as evidence for health-related interventions, beyond the strictures associated with evidence-based medicine. Focusing on COVID-19 transmission and prevention research, I consider the epistemic implications of this diversification of evidence in relation to: (1) experimental design, especially the revival of natural experiments as sources of reliable epidemiological knowledge; and (2) modelling practices, particularly the recognition of transdisciplinary expertise as crucial to developing and interpreting data models. Acknowledging such shifts in evidential, experimental and modelling practices helps avoid harmful applications of data-intensive methods.

A rational person doesn’t believe just anything. There are limits on what it is rational to believe. How wide are these limits? That’s the main question that interests me here. But a secondary question immediately arises: What factors impose these limits? A first stab is to say that one’s evidence determines what it is epistemically permissible for one to believe. Many will claim that there are further, non-evidentiary factors relevant to the epistemic rationality of belief. I will be ignoring the details of alternative answers in order to focus on the question of what kind of rational constraints one’s evidence puts on belief. Our main question concerns how far epistemic permission and obligation can come apart.1 Suppose I am epistemically permitted to believe P, i.e., it would not be irrational for me to believe it. Am I thereby obliged to believe P, or are other options rationally available to me?2 Might I be equally rational in remaining agnostic about P, or even believing not-P? Or could even a slightly stronger or weaker degree of confidence be just as reasonable?

A probabilistic logic of induction is unable to separate cleanly neutral support from disfavoring evidence (or ignorance from disbelief). Thus, the use of probabilistic representations may introduce spurious results stemming from its expressive inadequacy. That such spurious results arise in the Bayesian “doomsday argument” is shown by a reanalysis that employs fragments of an inductive logic able to represent evidential neutrality. Further, the improper introduction of inductive probabilities is illustrated with the “self-sampling assumption.”.

Elementary results concerning the connections between deductive relations and probabilistic support are given. These are used to show that Popper-Miller's result is a special case of a more general result, and that their result is not very unexpected as claimed. According to Popper-Miller, a purely inductively supports b only if they are deductively independent — but this means that a b. Hence, it is argued that viewing induction as occurring only in the absence of deductive relations, as Popper-Miller sometimes do, is untenable. Finally, it is shown that Popper-Miller's claim that deductive relations determine probabilistic support is untrue. In general, probabilistic support can vary greatly with fixed deductive relations as determined by the relevant Lindenbaum algebra.

Contrary to popular opinion, non-natural realism can explain both why normative properties supervene on descriptive properties, and why this pattern is analytic. The explanation proceeds by positing a subtle polysemy in normative predicates like “good”. Such predicates express slightly different senses when they are applied to particulars (like Florence Nightingale) and to kinds (like altruism). The former sense, “goodPAR”, can be defined in terms of the latter, “goodKIN”, as follows: x is goodPAR iff there is a kind K such that x is a token of K, and K is goodKIN. Now if x and y are descriptively exactly similar, then they are tokens of exactly the same kinds, so x is a token of a goodKIN kind if and only if y is. Therefore, by the definition, x is goodPAR if and only if y is. Supervenience just falls out of the definition of “goodPAR”

This chapter proceeds from the standard picture of the relation between intuitions and experimental philosophy: the alleged evidential role of intuitions about hypothetical cases, and experimental philosophy’s challenge to these judgments, based on their variation with philosophically irrelevant factors. I will survey some of the main defenses of this standard picture against the x-phi challenge, most of which fail. Concerning the most popular defense, the expertise defense, I will draw the bleak conclusion that intuitive expertise of the envisaged kind is largely a myth. Next, I will consider the mischaracterization objection, which has mainly been developed by Deutsch and Cappelen on the basis of textual evidence: that philosophers do not appeal to intuitions as evidence for their case judgments, but instead argue for them. This would render the x-phi challenge mostly irrelevant, due to its focus on intuitions about hypothetical cases. I will then consider a few instructive replies to the mischaracterization objection, which are all unconvincing on further inspection. Finally, I will discuss some potential normative consequences of the mischaracterization objection, and I will argue that it recommends a shift away from the excessive focus on intuitions about cases in metaphilosophy and experimental philosophy, towards more work on the role of argumentation in the method of cases. More speculatively, I claim that philosophers should always argue for their case judgments, even if they have strong intuitions about them, because an argument-based methodology would be more transparent and philosophically fruitful than one that mainly relies on intuition.

We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use of the term 'concept' in ontology development, influenced SNOMED CT and other medical terminologies. Against this background we then show how the Foundational Model of Anatomy, the Gene Ontology, Basic Formal Ontology and other OBO Foundry ontologies came into existence and discuss their role in the development of contemporary biomedical informatics.

Foundational ontologies, central constructs in ontological investigations and engineering alike, are based on ontological categories. Firstly proposed by Aristotle as the very ur- elements from which the whole of reality can be derived, they are not easy to identify, let alone partition and/or hierarchize; in particular, the question of their number poses serious challenges. The late medieval philosopher Dietrich of Freiberg wrote around 1286 a tutorial that can help us today with this exceedingly difficult task. In this paper, I discuss ontological categories and their importance for foundational ontologies from both the contemporary perspective and the original Aristotelian viewpoint, I provide the translation from the Latin into English of Dietrich's De origine II with an introductory elaboration, and I extract a foundational ontology–that is in fact a single-category one–from this text rooted in Dietrich's specification of types of subjecthood and his conception of intentionality as causal operation.

Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.

This paper examines whether biblical descriptions of the intermediate state imply dualism of the sort that rules out physicalism. Certain passages in the Bible seem to describe persons or souls existing without their bodies in an intermediate state between death and resurrection. For this reason, these passages appear to imply a form of dualism. Some Christian physicalists have countered that the passages in question are in fact compatible with physicalism. For it is compatible with physicalism that, although we are necessarily constituted by physical bodies, we can continue to exist without our current bodies in the intermediate state by being constituted by replacement bodies. I argue that broadly Gricean considerations significantly weaken this response. In its place, I propose a new, linguistic objection to the biblical argument for dualism. The linguistic objection says that biblical descriptions of an intermediate state cannot imply dualism in the sense that contradicts physicalism because physicalism is defined by a concept of the physical derived from modern physics, and no term in the biblical languages expresses that concept. I argue that the linguistic objection is less vulnerable to Gricean considerations than the constitution objection. On the other hand the linguistic objection also makes concessions to dualism that some Christian physicalists will find unacceptable. And it may be possible to reinforce the biblical argument for dualism by appeal to recent research on ‘common-sense dualism’. The upshot for Christian physicalists who wish to remain open to the biblical case for an intermediate state is therefore partly good, partly bad. The prospects for a Biblical argument for dualism in the sense that contradicts physicalism are limited but remain open.

This book evaluates the widespread preference in philosophy of mind for varieties of property dualism over other alternatives to physicalism. It takes the standard motivations for property dualism as a starting point and argues that these lead directly to nonphysical substances resembling the soul of traditional metaphysics. In the first half of the book, the author clarifies what is at issue in the choice between theories that posit nonphysical properties only and those that posit nonphysical substances. The crucial question, he argues, is whether one posits nonphysical things that satisfy an Aristotelian-Cartesian independence definition of substance: nonphysical things that could exist in the absence of anything else. In the second half, the author argues that standard and Russellian monist forms of property dualism are far less plausible than we usually suppose. Most significantly, the presuppositions of one of the leading arguments for property dualism, the conceivability argument, lead by parity of reasoning to the view that conscious subjects are nonphysical substances. He concludes that if you posit nonphysical properties in response to the mind-body problem then you should be prepared to posit nonphysical substances as well. Mainstream philosophy of mind must take nonphysical substances far more seriously than it has done for the best part of a century. The Mind-Body Problem and Metaphysics will be of interest to scholars and advanced students working in philosophy of mind, metaphysics, and the history of philosophy.

In Galileo's Error, Philip Goff sets out a manifesto for a post-Galilean science of consciousness. Article four of the manifesto reads: 'Anti-Dualism: Consciousness is not separate from the physical world; rather consciousness is located in the intrinsic nature of the physical world.' I argue that there is an important sense of ‘dualism’ in which Goff’s arguments are not only compatible with but entail dualism, and not only dualism but substance dualism. Substance dualism, in the sense I have in mind, is the view that (i) there are two sides to reality, a fundamentally mental side and a fundamentally nonmental side; and (ii) the fundamentally mental side consists of mental substances resembling Cartesian souls. I do not suggest that this is bad news for post-Galilean science of consciousness. My aim is rather to push for a certain view about what a theory of consciousness motivated by Goff’s arguments must look like.

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research.