Barry Smith

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.

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.

Research and engineering in the quantum domain involve long chains of activity involving theory development, hypothesis formation, experimentation, device prototyping, device testing, and many more. At each stage multiple paths become possible, and of the paths pursued, the majority will lead nowhere. Our quantum metascience approach provides a strategy which enables all stakeholders to gain an overview of those developments along these tracks, that are relevant to their specific concerns. It provides a controlled vocabulary, built out of terms that are designed to be maximally comprehensible to all groups of stakeholders and across all the sub-fields of the quantum domain.

COVID-19 often manifests with different outcomes in different patients, highlighting the complexity of the host-pathogen interactions involved in manifestations of the disease at the molecular and cellular levels. In this paper, we propose a set of postulates and a framework for systematically understanding complex molecular host-pathogen interaction networks. Specifically, we first propose four host-pathogen interaction (HPI) postulates as the basis for understanding molecular and cellular host-pathogen interactions and their relations to disease outcomes. These four postulates cover the evolutionary dispositions involved in HPIs, the dynamic nature of HPI outcomes, roles that HPI components may occupy leading to such outcomes, and HPI checkpoints that are critical for specific disease outcomes. Based on these postulates, an HPI Postulate and Ontology (HPIPO) framework is proposed to apply interoperable ontologies to systematically model and represent various granular details and knowledge within the scope of the HPI postulates, in a way that will support AI-ready data standardization, sharing, integration, and analysis. As a demonstration, the HPI postulates and the HPIPO framework were applied to study COVID-19 with the Coronavirus Infectious Disease Ontology (CIDO), leading to a novel approach to rational design of drug/vaccine cocktails aimed at interrupting processes occurring at critical host-coronavirus interaction checkpoints. Furthermore, the host-coronavirus protein-protein interactions (PPIs) relevant to COVID-19 were predicted and evaluated based on prior knowledge of curated PPIs and domain-domain interactions, and how such studies can be further explored with the HPI postulates and the HPIPO framework is discussed.

Sam Harris is a contemporary illustration of the difficulties standing in the way of coherent interdisciplinary thinking in an age where science and the humanities have drifted so far apart. We are here with Harris’s views on AI, and specifically with his view according to which, with the advance of AI, there will evolve a machine superintelligence with powers that far exceed those of the human mind. This he sees as something that is not merely possible, but rather a matter of inevitability. If, however, we look carefully at what intelligence is, and at how computers really work on the basis of mathematical models, then we can see that it is forever impossible to emulate inside a computer even the intelligence of crows or rabbits, let alone that of human beings.

The three young philosophers Kevin Mulligan, Peter Simons and Barry Smith have become well-known in the last few years especially in German-speaking analytical philosophy and phenomenology circles. This is on the one hand as a result of their historical and systematic philosophical work; but it is also because of the provocative way in which they represent their philosophy. Because they often appear in threes, they have become known as the "gang of three" or "three musketeers" or even – and this in an admiring sense – "mafiosi" (Rescher). They are known primarily for the small workshops they have been organizing in uncomplicated Anglo-Saxon manner all over Europe. Their goal has been to show that analytic philosophy as it has been pursued up to now is in need of reform and also that the history of scientific philosophizing is not identical to the history of Anglo-Saxon analytic philosophy. Above all, they have tried to make more well known the Austrian tradition of scientific philosophy, and they can be given credit for having done much to promote the current interest in this tradition, which means: the history of Austrian philosophy of the 19th and early 20th centuries and its historical context in the Austro-Hungarian Empire.

When governments collect data relating to economic activity they commonly employ a distinction between goods and services. Both goods and services have economic value. Goods (cars, houses, bottles of milk) are, very roughly, independent continuants which can be alienated (sold, gifted, rented, and so forth). Services (hairdressing, gardening, teaching) are, again very roughly, occurrents. They are occurrents which are further often said to be marked by the fact that production and consumption coincide. Social services under both headings typically involve combinations of service provision. The patterns in question are primarily those of the family (where the patterns to be maintained or restored are, roughly, those characteristic of a healthy family life) and of the child (patterns characteristic of healthy development, provided by child protective services, who work to prevent child abuse).

We can illustrate the phenomenon of model-induced escape by examining the phenomenon of spam filters. Spam filter A is, we can assume, very effective at blocking spam. Indeed it is so effective that it motivates the authors of spam to invent new types of spam that will beat the filters of spam filter A. An example of this phenomenon in the realm of philosophy is illustrated in the work of Nyíri on Wittgenstein's political beliefs. Nyíri writes a paper demonstrating convincingly that there are strong signals of a conservative strain of thought in the writings of Wittgenstein. This has initially a tiny effect; but then a more significant effect sets in as the authors of Wittgenstein secondary literature, consciously or unconsciously, draw attention to features of Wittgenstein which cast the conservatism thesis in a negative light. This is an example of what I call model-induced escape: the Nyíri model of Wittgenstein initiates a process which undermines the Nyíri model of Wittgenstein. Wittgenstein, as obiectum philosophiae, is reduced to a constantly mutating set of interpretations of a certain body of work. Perhaps this is what makes philosophy so problematic when viewed from the perspective of results, or in other words of signs of progress commonly accepted across the discipline. There are just too many ways of inducing escape from any given putative discovery; too many dimensions along which an interpretative or definitional arms race can be triggered.

The Industrial Ontologies Foundry (IOF) was formed to create a suite of interoperable ontologies. Ontologies that would serve as a foundation for data and information interoperability in all areas of manufacturing. To ensure that each ontology is developed in a structured and mutually coherent manner, the IOF has committed to the tiered architecture of ontology building based on the Basic Formal Ontology (BFO) as top level. One of the critical elements of a successful tiered architecture build is the domain mid-level ontologies. However, thus far there has been no mid-level manufacturing ontology that is based on BFO. The IOF has recently released the IOF Core version 1 beta to fill this gap. This paper documents the development process and gives an overview of the current content of the IOF Core. Finally, the paper describes how the IOF Core can be used as the basis for a more domain-specific Supply Chain Ontology.

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity — for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. According to the theory, advances in AI — specifically of the machine learning type that’s able to take on new information and rewrite its code accordingly — will eventually catch up with the wetware of the biological brain. In this interpretation of events, every AI advance from Jeopardy-winning IBM machines to the massive AI language model GPT-3 is taking humanity one step closer to an existential threat. We’re literally building our soon-to-be-sentient successors. Except that it will never happen. At least, according to the authors of the new book Why Machines Will Never Rule the World: Artificial Intelligence without Fear.

Das in diesem Aufsatz vorgebrachte Argumentationsmuster hat folgende Schritte: 1. Der menschliche Geist ist vom Körper nicht trennbar, sie bilden ein Kontinuum. 2. Unser Bewusstsein und alle darauf aufbauenden geistigen Phänomene sind die Emanation eines materiellen Prozesses, den ein komplexes System verursacht. 3. Komplexe Systeme lassen sich mathematisch nicht modellieren und nicht kausal verstehen. 4. Computer sind Turing-Maschinen. Sie können nur mathematische Modelle berechnen. Es wird niemals Hyper-Turing Maschinen geben, und wenn es sie gäbe, könnten sie auch nur mathematische Modelle berechnen. 5. Es ist nicht möglich, den Körper als Substrat des Geistes durch einen Computer zu ersetzen. Die digitale Unsterblichkeit ist demzufolge ein Ding der Unmöglichkeit.

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.

There is a common misconception across the lntelligence Community (IC) to the effect that information trapped within multiple heterogeneous data silos can be semantically integrated by the sorts of meaning-blind statistical methods employed in much of artificial intelligence (Al) and natural language processlng (NLP). This leads to the misconception that incoming data can be analysed coherently by relying exclusively on the use of statistical algorithms and thus without any shared framework for classifying what the data are about. Unfortunately, such approaches do not yield sustainable results where we are dealing with widely distributed, highly heterogeneous and often changing bodies of data. We argue here that the needed integration requires the use of what we call an lntegrating Semantic Framework (ISF), which provldes a consistent set of categories and relationships that can be reused over and over again to tag successive bodies of data in ways which foster more coherent representation and reasoning.

Ontologies and standards are strongly interrelated and can often be seen as two sides of the same coin. Indeed, standards reflect consensus on the semantics of terms, though different standards might employ different ways to explain the same or very similar concepts semantically. Given the crucial role played by both aspects in OntoCommons, we have interviewed Barry Smith, SUNY Distinguished Professor of Philosophy at the University at Buffalo and one of the External Advisory Board (EAB) members of the OntoCommons project, to better understand what are the challenges and priorities to focus on in this context.

The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform and new ontologies being created on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved.

Achieving space situational awareness requires, at a minimum, the identification, characterization, and tracking of space objects. Leveraging the resultant space object data for purposes such as hostile threat assessment, object identification, and conjunction assessment presents major challenges. This is in part because in characterizing space objects we reference a variety of identifiers, components, subsystems, capabilities, vulnerabilities, origins, missions, orbital elements, patterns of life, operational processes, operational statuses, and so forth, which tend to be defined in highly heterogeneous and sometimes inconsistent ways. The Space Domain Ontologies are designed to provide a consensus-based realist framework for formulating such characterizations in a way that is both consistent and computable. Space object data are aligned with classes and relations in a suite of ontologies built around the existing Space Object Ontology. They are stored in a dynamically updated Resource Description Framework triple store, which can be queried to support space situational awareness and the needs of spacecraft operators and analysts. This paper provides an overview of the Space Domain Ontologies and their development and use. It presents the motivation for and advantages of the Space Domain Ontologies, including the benefits they provide for enhancing and maintaining long-term space situational awareness.

This paper describes an AFOSR-supported basic research program that focuses on developing a new framework for combining hard with soft data in order to improve space situational awareness. The goal is to provide, in an automatic and near real-time fashion, a ranking of possible threats to blue assets (assets trying to be protected) from red assets (assets with hostile intentions). The approach is based on Conceptual Spaces models, which combine features from traditional associative and symbolic cognitive models. While Conceptual Spaces are revolutionary, they lack an underlying mathematical framework. Several such frameworks have attempted to represent Conceptual Spaces, but by far the most robust is the model developed by Holender. His model utilizes integer linear programming in order to obtain an overall similarity value between observations and concepts that support the formation of hypotheses. This paper will describe a method for building Conceptual Spaces models for threats that utilizes ontologies as a means to provide a clear semantic foundation for this inferencing process; in particular threat ontologies and space domain ontologies are developed and employed in this approach. A space situational awareness use-case is presented involving a kinetic kill scenario and results are shown to assess the performance of this fusion-based inferencing framework.

The list which follows is intended as a comprehensive bibliographical survey of the wider Gestalt tradition from Graz and Berlin to Padua, Frankfurt and New York. It presents diagrammatically the main influence and teacher-pupil relationships also groupings into schools. It includes the classical texts of the Gestalt psychological tradition, together with the more important translations and reprints thereof. Special attention is paid to works on the following topics: - the concept of Prägnanz or `good form' and related treatments of aesthetic phenomena and of pattern recognition - cognitive processes and problem-solving - criticisms of Gestalt theoru - phenomenal causation - figure-ground relations , phenomenaI boundaries, subjective contours - phenomenal motion - perceptual constancies - philosophical inquiries into the nature of `configuration'. `relation', `structure', etc . , and writing on psychological holism (but only insofar as they relate directly to the literature of Gestalt psychology proper)

Previous work has shown that the Complex Conceptual Spaces − Single Observation Mathematical framework is a useful tool for event characterization. This mathematical framework is developed on the basis of Conceptual Spaces and uses integer linear programming to find the needed similarity values. The work of this paper is focused primarily on space event characterization. In particular, the focus is on the ranking of threats for malicious space events such as a kinetic kill. To make the Conceptual Spaces framework work, the similarity values between the contents of observations on the one hand and the properties of the entities observed on the other needs to be found. This paper shows how to exploit Dempster-Shafer theory to implement a statistical approach for finding these similarities values. This approach will allow a user to identify the uncertainty involved in similarity value data, which can later be propagated through the developed mathematical model in order for the user to know the overall uncertainty in the observation-to-concept mappings needed for space event characterization.

We propose a domain-level ontology of plays for the facilitation of play-based collaborative autonomy among unmanned and manned-unmanned aircraft teams in the Army’s Unmanned Aircraft System (UAS) mission domain. We define a play as a type of plan that prescribes some pattern of intentional acts that are intended to reliably result in some goal in some competitive context, and which specifies one or more roles that are realized by those prescribed intentional acts. The ontology is well suited to be extended to other types of military and nonmilitary unmanned vehicle operations.

We propose a definition of capability as a class intermediate between function and disposition as the latter are defined in Basic Formal Ontology (BFO). A disposition inheres in a material entity and is realized in a certain kind of process. An example is the disposition of a glass to break when struck, which is realized when it shatters. A function is a disposition which is (simply put) the rationale for the existence of its bearer. To say for example that a water pump has the function to pump water is to say that the pump exists because something was needed that would pump water. Capabilities are a special sort of disposition in that that, like functions, they can be evaluated on the basis of how well they are realized. They differ from functions in that their realizations are not the rationale – not the primary reason – for the existence of their bearers. Thus, a water pump may have many capabilities, including to be weatherproof, to run without lubricant, and so forth, but only one function. All functions are capabilities on the view we defend, but not all capabilities are functions. We develop a series of axioms to distinguish capabilities formally from both dispositions and functions and provide examples of the use of ‘capability’ in a variety of domains.