Barry Smith

How, as Caldwell puts it, does one choose between rival systems all of which claim to rest on a priori foundations? On the nonfallibilistic conception it is difficult to make sense even of the possibility of rival systems of this sort. On the conception here defended, in contrast, the existence of such rival systems can be seen to be a perfectly natural and acceptable consequence of the just-mentioned difficulties we will often fact in coming to know even the intelligible traits of reality: one adjudicates between such systems in the same way in which one adjudicates between all rival scientific hypotheses, namely via a complex mixture of empirical and a priori considerations.

One famous scene in The Wolf of Wall Street (2013) is the dialogue between the young Jordan Belfort (Leonardo DiCaprio) and the expert trader Mark Hanna (Matthew McConaughey). Hanna is complaining that the stock market is unpredictable; it’s “fugazi … it’s fairy dust. It doesn’t exist. It’s never landed. It is not matter. It’s not on the element chart. It’s not real”. But the fact that something is unpredictable and non-physical does not imply that it does not exist. On the other hand, its unpredictability, non-physicality, and the fact that the stock market trend is largely determined by investors’ beliefs, do make its nature difficult to grasp. This special issue of Rivista di Estetica aims to explore the financial sector from an ontological point of view. While the ontology of money has been extensively studied, few scholars have focused on the stock market and, more generally, on those entities belonging to the investment landscape. Matters are made more complicated by the fact that the financial sector is characterized by an ever-increasing use of digital technology, including software elements that trade in the market themselves. We believe that a careful study of this phenomenon may help us better to understand the role of artificial agents in the social world.

In our book, Why Machines Will Never Rule the World, Jobst Landgrebe and I argue that we can engineer machines that can emulate the behaviours only of simple systems, which means: only of those systems whose behaviour we can predict mathematically. The human brain is an example of a complex system, and thus its behaviour cannot be emulated by a machine. We use this argument to debunk the claims of those who believe that large language models are poised to achieve a level of intelligence that will equal or even surpass that of the human brain. The essay was published under the title "Why Machines Will Never Rule the World"

Here we present what we believe is a novel account of what languages are, along with an axiomatically rich representation of languages and language-related data that is based on this account. We propose an account of languages as aggregates of dispositions distributed across aggregates of persons, and in doing so we address linguistic competences and the processes that realize them. This paves the way for representing additional types of language-related entities. Like demographic data of other sorts, data about languages may be of use to researchers in a number of areas, including biomedical research. Data on the languages used in clinical encounters are typically included in medical records, and capture an important factor in patient-provider interactions. Like many types of patient and demographic data, data on a person’s preferred and primary languages are organized in different ways by different systems. This can be a barrier to data integration. We believe that a robust framework for representing language in general and preferred and primary language in particular – which has been lacking in ontologies thus far – can promote more successful integration of language-related data from disparate data sources.

In a development that has still been hardly noticed by philosophers, a conception of ontology has been advanced in recent years in a series of extra-philosophical disciplines as researchers in linguistics, psychology, geography and anthropology have sought to elicit the ontological commitments (‘ontologies’, in the plural) of different cultures or disciplines. Exploiting the terminology of Quine, researchers in psychology and anthropology have sought to establish what individual human subjects, or entire human cultures, are committed to, ontologically, in their everyday cognition, in much the same way in which philosophers of science have attempted to elicit the ontological commitments of the natural sciences. Thus they have engaged in inquiries designed to establish how folk ontologies (or folk biologies, folk theories of physics, folk psychologies, and so on) develop through infancy and childhood, or to establish the degree to which given elements of folk ontologies reflect universal features of the human cognitive system. In a parallel development, researchers in biomedicine developed what they called 'ontologies' as controlled vocabularies to ensure interoperability of the way in which, for example, genomic data is described in different biological disciplines. We describe in what follows some of the relations between these efforts and the more traditional concerns of philosophers.

Some defenders of so-called `artificial intelligence' believe that machines can understand language. In particular, Søgaard has argued in his "Understanding models understanding language" (2022) for a thesis of this sort. His idea is that (1) where there is semantics there is also understanding and (2) machines are not only capable of what he calls `inferential semantics', but even that they can (with the help of inputs from sensors) `learn' referential semantics. We show that he goes wrong because he pays insufficient attention to the difference between language as used by humans and the sequences of inert symbols which arise when language is stored on hard drives or in books in libraries.

The view of nature we adopt in the natural attitude is determined by common sense, without which we could not survive. Classical physics is modelled on this common-sense view of nature, and uses mathematics to formalise our natural understanding of the causes and effects we observe in time and space when we select subsystems of nature for modelling. But in modern physics, we do not go beyond the realm of common sense by augmenting our knowledge of what is going on in nature. Rather, we have measurements that we do not understand, so we know nothing about the ontology of what we measure. We help ourselves by using entities from mathematics, which we fully understand ontologically. But we have no ontology of the reality of modern physics; we have only what we can assert mathematically. In this paper, we describe the ontology of classical and modern physics against this background and show how it relates to the ontology of common sense and of mathematics.

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs.

The application of ontology has thus far [in 1998] been confined almost exclusively to the field of knowledge representation. Ontology has been applied, for example, in the design of medical databases and in the construction of geographical information systems. One area which is naturally suited to ontological analysis is that of the law and of social institutions in general. Legal systems are composed of legal entities, such as laws, contracts, obligations, and rights. Their application yields new categories of entities such as: corporations, trademarks, marriages, and parcels of real estate. The categorization of these entities by different legal systems has not, by-and-large, been conducted in ways which exploit the tools of modern ontology. Consequently, contradictions and inconsistencies often arise in the law when, for instance, one type of entity is forced into two mutually exclusive categories (e.g., when software is considered both patentable and copyrightable). Papers are invited which consider these and related issues from a philosophical point of view.

We have now reached the point at which cloud computing and other types of advanced infrastructure are bringing about a situation in which knowledge objects can be delivered in an efficient manner to hose who need to consume them. And just as highways were the infrastructure necessary for a manufacturing economy, serving as the arteries along which raw materials and manufactured goods coming in from all directions could flow, so we believe that ontologies will in the future provide an important pillar in the infrastructure that is necessary for the accurate and safe delivery of all varieties of knowledge.

The computational genomics community has come increasingly to rely on the methodology of creating annotations of scientific literature using terms from controlled structured vocabularies such as the Gene Ontology (GO). We here address the question of what such annotations signify and of how they are created by working biologists. Our goal is to promote a better understanding of how the results of experiments are captured in annotations in the hope that this will lead to better representations of biological reality through both the annotation process and ontology development, and in more informed use of the GO resources by experimental scientists.

Material things have material (spatial) parts. Acts, events, occurrences, have phases, which we can view as their temporal parts. Spatial surfaces and volumes, stretches of time, they all have parts again; they can all be considered "extended". Entities, on the other hand, such as directions, numbers, temperatures, colors, tones, fictional characters, prices, numbers, values, ideologies, goals, are all unextended; they are partless. Let us call such non-extended objects “nodes”, in order to express the fact that we have a tendency to grasp them as pure correlates of our thinking, often at the same time viewing them effectively as placeholders within systems of coordinates in various intellectual and social domains. My aim, here, is to clarify this rough idea of “node” for the purpose of providing a corresponding clarification of Ingarden's idea of a purely intentional object. I first consider purely intentional objects in general, and then focus in particular on fictional examples.

An interview by Alex Thomson of UKColumn on Landgrebe and Smith's book: Why Machines Will Never Rule the World. The subtitle of the book is Artificial Intelligence Without Fear, and the interview begins with the question of the supposedly imminent takeover of one profession or the other by artificial intelligence. Is there truly reason to be afraid that you will lose your job? The interview itself is titled 'Where this is no will there is no way', drawing on one thesis of the book to the effect that there will never be anything like the human will on the part of a machine. This is for mathematical reasons. AI is always and in every case a matter of algorithms; thus of applied mathematics. And algorithms are not the sorts of things that can want or desire or intend to do something. It is the human will of those who work at OpenAI or google or microsoft which have given rise to the new shiny objects of AI (ChatGPT and other Large Language Models). And it is the human will of the users of these shiny objects which gives them the resources and power to work their apparent miracles.

Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ in this sense is often used by philosophers as a synonym of ‘metaphysics’ (a label meaning literally: ‘what comes after the Physics’), a term used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. But in recent years, in a development hardly noticed by philosophers, the term ‘ontology’ has gained currency in the field of computer and information science, and in information-driven research in bioinformatics and related areas. We examine these new developments in applied ontology, and show what lessons they might have for both philosophers and information scientists.