Natural language processing is a critical part of the digital transformation. NLP enables user-friendly interactions between machine and human by making computers understand human languages. Intelligent chatbot is an essential application of NLP to allow understanding of users’ utterance and responding in understandable sentences for specific applications simulating human-to-human conversations and interactions for problem solving or Q&As. This research studies emerging technologies for NLP-enabled intelligent chatbot development using a systematic patent analytic approach. Some intelligent text-mining techniques are applied, including document (...) term frequency analysis for key terminology extractions, clustering method for identifying the subdomains, and Latent Dirichlet Allocation for finding the key topics of patent set. This research utilizes the Derwent Innovation database as the main source for global intelligent chatbot patent retrievals. (shrink)

As available intelligence data and information expand in both quantity and variety, new techniques must be deployed for search and analytics. One technique involves the semantic enhancement of data through the creation of what are called ‘ontologies’ or ‘controlled vocabularies.’ When multiple different bodies of heterogeneous data are tagged by means of terms from common ontologies, then these data become linked together in ways which allow more effective retrieval and integration. We describe a simple case study to show how (...) these benefits are being achieved, and we describe our strategy for developing a suite of ontologies to serve the needs of the war-fighter in the ever more complex battlespace environments of the future. (shrink)

We describe on-going work on IAO-Intel, an information artifact ontology developed as part of a suite of ontologies designed to support the needs of the US Army intelligence community within the framework of the Distributed Common Ground System (DCGS-A). IAO-Intel provides a controlled, structured vocabulary for the consistent formulation of metadata about documents, images, emails and other carriers of information. It will provide a resource for uniform explication of the terms used in multiple existing military dictionaries, thesauri and (...) metadata registries, thereby enhancing the degree to which the content formulated with their aid will be available to computational reasoning. (shrink)

An application of Narrative Knowledge Representation Language (NKRL) techniques on (declassified) ‘terrorism in Southern Philippines’ documents has been carried out in the context of the IST Parmenides project. This paper describes some aspects of this work: it is our belief, in fact, that the Knowledge Representation techniques and the Intelligent Information Retrieval tools used in this experiment can be of some interest also in an ‘Ontological Modelling of Legal Events and Legal Reasoning’ context.

Formal ontology combines two ideas, one originating with Husserl, the other with Frege: that of ontology of the formal aspects of all objects, irrespective of their particular nature, and ontology pursued by employing the tools of modern formal disciplines, notably logic and semantics. These two traditions have converged in recent years and this is the first collection to encompass them as a whole in a single volume. It assembles essays from authors around the world already widely known (...) for their work in formal ontology, and illustrates that through the application of formal methods the ancient discipline of ontology may be put on a firm methodological basis. The essays not only illuminate the nature of ontology and its relation to other areas, in language, logic and everyday life, but also demonstrate that common issues from the analytical and phenomenological traditions may be discussed without ideological barriers. Audience: advanced students of and specialists in philosophy, linguistics, cognitive science, computer science, database engineering. (shrink)

In this paper, we describe the use of legal ontologies as a basis to improve IT support for professional judges. As opposed to most legal ontologies designed so far, which are mostly based on dogmatic and normative knowledge, we emphasize the importance of professional knowledge and experience as an important pillar for constructing the ontology. We describe an intelligent FAQ system for junior judges that intensively use the ontology.

This article is devoted to discussion of the philosophical aspects of the problems associated with the creation of a strong artificial intelligence. It is noted that even taking into account the fact that the study of the brain and consciousness are represented by separate areas, such as cognitive neuroscience and philosophy of consciousness, there are a number of problems of an ontological nature, which indicate that research in the field of creating systems with artificial intelligence, although actively developing, (...) is rather related to its weak definition and does not provide prerequisites for confidence in the possibility of creating a strong artificial intelligence. (shrink)

In this paper, we examine the meta-ontology of AI systems with human-level intelligence, with us denoting such AI systems as AI E. Meta-ontology in philosophy is a discourse centered on ontology, ontological commitment, and the truth condition of ontological theories. We therefore discuss how meta-ontology is conceptualized for AI E systems. We posit that the meta-ontology of AI E systems is not concerned with computational representations of reality in the form of structures, data constructs, (...) or computational concepts, while the ontological commitment of AI E systems is directed toward what exists in the outside world. Furthermore, the truth condition of the ontology (which is meta-ontological assumption) of AI E systems does not require consistency with closed conceptual schema or ontological theories but rather with reality, or in other words, “what is the world” (Smith, 2019, p.57). In addition, the truth condition of AI E systems is verified through operational success rather than by coherence with theories. This work builds on ontological postulates about AI systems that were formulated by Brian Cantwell Smith (2019). (shrink)

PurposeTo use the power of knowledge acquisition and machine learning in the development of a collaborative computer classification system based on the features of age-related macular degeneration (AMD).MethodsA vocabulary was acquired from four AMD experts who examined 100 ophthalmoscopic images. The vocabulary was analyzed, hierarchically structured, and incorporated into a collaborative computer classification system called IDOCS. Using this system, three of the experts examined images from a second set of digital images compiled from more than 1000 patients with AMD. Images (...) were annotated, and features were identified and defined. Decision trees, a machine learning method, were trained on the data collected and used to extract patterns. Interrelationships between the data from the different clinicians were investigated.ResultsSix drusen classes in the structured vocabulary were largely sufficient to describe all the identified features. The decision trees classified the data with 76.86% to 88.5% accuracy and distilled patterns in the form of hierarchical trees composed of 5 to 15 nodes. Experts were largely consistent in their characterization of soft, and to a lesser extent, hard drusen, but diverge in definition of other drusen. Size and crystalline morphology were the main determinants of drusen type across all experts.ConclusionsMachine learning is a powerful tool for the characterization of disease phenotypes. The creation of a defined feature set for AMD will facilitate the development of an IDOCS-based classification system. (shrink)

Formal ontology as a main branch of metaphysics investigates categories of being. In the formal ontological approach to metaphysics, these ontological categories are analysed by ontological forms. This analysis, which we illustrate by some category systems, provides a tool to assess the clarity, exactness and intelligibility of different category systems or formal ontologies. We discuss critically different accounts of ontological form in the literature. Of ontological form, we propose a character- neutral relational account. In this metatheory, ontological forms of (...) entities are their standings in internal relations whose holding is neutral on the character of their relata. These relations are “formal ontological relations”. Entities belong to ontological categories because they stand in the same formal ontological relations. We hold a nominalist relationalism about ontological categories and forms. We conclude by showing that our metatheory is useful for understanding categorial fundamentality/non- fundamentality, different formal ontologies, and for unifying metaphysical questions. (shrink)

이 논문은 인공지능의 존재론적 지위를 평가하기 위한 두 개의 물음을 던진다. 하나는 인공지능이 개념을 가질 수 있는가 하는 물음이고, 다른 하나는 인공지능이 행위의 능력을 가졌는가 하는 물음이다. 개념 능력은 인공지능이 통상적인 언어의 형태로 주어지는 명령이나 정보를 능동적으로 이해하고 그것의 작동에 반영할 수 있기 위한 필요조건이다. 최근 인공지능 기술은 영상자료에서 비지도 학습의 방식으로 특정 범주의 사물을 식별해내는 성과를 거뒀지만, 이런 식별의 능력은 개념 능력의 필요조건도 충분조건도 아니다. 인공지능이 우리와 같은 개념을 가지도록 하는 것은 인간 사회의 언어 관행을 고스란히 학습시키는 방식으로 가능할 (...) 것인데, 이것은 불가능하지는 않지만 사실상 완결되기 어려운 목표다. 한편, 행위 능력에 대한 평가는 인공지능 프로그램이나 지능형 로봇 같은 인공물을 모종의 책임과 권리를 지닌 행위 주체로 간주할 수 있는가 하는 물음을 다루기 위한 열쇠다. 행위는 행위 주체와 환경 간의 상호작용으로, 행위의 실현에는 환경 속 대상에 대한 지각과 그것에 관한 동작의 협응, 달리 말하자면 감수자 체계와 행위자 체계의 통합이 필수적이다. 그리고 이러한 협응이나 통합의 배후에는 행위를 낳는 의도를 가지고 협응을 조율하는 존재, 즉 자아가 있다. 행위는 감수자 체계와 행위자 체계의 협응을 통해 자아의 의도를 실현하는 과정이다. 인공물의 작동이 이런 조건을 충족한다고 볼 이유는 아직 없다. 결국, 두 물음에 대한 이 논문의 대답은 부정적이거나 기껏해야 유보적이다. (shrink)

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. (shrink)

The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding (...) of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts. Of particular importance to the Cognitive Process Ontology is the class Representation that is Warranted. Such a representation is descriptive in nature and deserving of trust in its veridicality. The latter is because a Representation that is Warranted is always produced by a process that was vetted (or successfully designed) to reliably produce veridical representations. As such, Representations that are Warranted are what in other contexts we might refer to as ‘items of knowledge’. (shrink)

We describe a prototype ontology-driven information system (ODIS) that exploits what we call Portion of Reality (POR) representations. The system takes both sensor data and natural language text as inputs and composes on this basis logically structured POR assertions. The goal of our prototype is to represent both natural language and sensor data within a single framework that is able to support both axiomatic reasoning and computation. In addition, the framework should be capable of discovering and representing new kinds (...) of situations and thematic roles, (e.g., roles such as agent, patient and instrument), based on new compositions of existing representations. We applied our prototype in an intelligence analysis use case to test the hypothesis that a framework of this sort can produce usefully structured information from combined natural language and sensor data inputs. We further tested our hypothesis by adding an enhanced US Air Force ontology framework to our ODIS in order to (1) process a collection of sensor data, intel reports, and mission plans; (2) build composite POR representations from these data; and (3) machine analyze the fused results to infer mission threats. (shrink)

The aim of this article is to argue that ontological choices in scientific practice undermine common formulations of the value-free ideal in science. First, I argue that the truth values of scientific statements depend on ontological choices. For example, statements about entities such as species, race, memory, intelligence, depression, or obesity are true or false relative to the choice of a biological, psychological, or medical ontology. Second, I show that ontological choices often depend on non-epistemic values. On the (...) basis of these premises, I argue that it is often neither possible nor desirable to evaluate scientific statements independently of non-epistemic values. Finally, I suggest that considerations of ontological choices do not only challenge the value-free ideal but also help to specify positive roles of non-epistemic values in an often neglected area of scientific practice. (shrink)

I give shape to a neglected debate in metaphysics, the debate over the ontological priority between individuality and collectivity. I distinguish the debate from more familiar ones in the recent literature and articulate what I call ontological collectivism, the view that collectivity is prior to individuality. I defend the in-principle intelligibility of the view from forceful general objections and argue that not only is it coherent but also of significant interest to the literature: it allows for overlooked alternatives on a (...) variety of central discussions. (shrink)

The aim of this article is to argue that ontological choices in scientific practice undermine common formulations of the value-free ideal in science. First, I argue that the truth values of scientific statements depend on ontological choices. For example, statements about entities such as species, race, memory, intelligence, depression, or obesity are true or false relative to the choice of a biological, psychological, or medical ontology. Second, I show that ontological choices often depend on non-epistemic values. On the (...) basis of these premises, I argue that it is often neither possible nor desirable to evaluate scientific statements independently of non-epistemic values. Finally, I suggest that considerations of ontological choices do not only challenge the value-free ideal but also help to specify positive roles of non-epistemic values in an often neglected area of scientific practice. (shrink)

Theories that posit metaphysical structure are able to do much work in philosophy. Some, however, find the notion of ‘metaphysical structure’ unintelligible. In this paper, I argue that their charge of unintelligibility fails. There is nothing distinctively problematic about the notion. At best, their charge of unintelligibility is a mere reiteration of previous complaints made toward similar notions. In developing their charge, I clarify several important concepts, including primitiveness, intelligibility, and the Armstrong-inspired “ontologism” view of the world. I argue that, (...) ultimately, their charge is best understood as an objection whose central premise is that the notion of ‘structure’ runs contrary to an important presupposition of contemporary metaphysics. But that central premise is, on closer inspection, implausible. I respond to the objection by identifying three popular metaphysical theories that violate the alleged presupposition but are still generally regarded as intelligible. The objection thus fails to show that a theory that posits metaphysical structure is unintelligible. (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)

The presence of intelligence and rationality in Artificial Intelligence and the Internet requires a new context of analysis in which Herbert Simon’s approach to the sciences of the artificial is surpassed in order to grasp the role of information in our contemporary setting. This new framework requires taking into account some relevant aspects. In the historical endeavor of building up AI and the Internet, minds and machines have interacted over the years and in many ways through the interrelation (...) between scientific creativity and technological innovation. Philosophically, minds and machines can have epistemological, methodological and ontological differences, which are based on the distinct configuration of human intelligence and artificial intelligence. Their comparison with rationality and its various forms is particularly relevant. Scientifically, AI and the Internet belong to the sciences of the artificial, because they work on designs that search for specific aims, following selected processes in order to achieve expected results. Technologically, AI and the Internet require the support of information and communication technologies. These have an instrumental role regarding the existence of AI and the Internet. Also ICT shape their diverse forms of configuration over the years.Within this framework, this paper offers a new context of analysis that goes beyond Simon’s and follows four main steps: the interaction between scientific creativity and technological innovation as the philosophico-methodological setting for Artificial Intelligence and the Internet; artificial intelligence and human intelligence as epistemological basis for machines and minds, where the differences between artificial intelligence and human intelligence are made explicit and the analysis of minds and machines is made from the perspective of rationality ; intention and its difference with design of machine learning are considered to distinguish human intelligence from artificial intelligence; and the internal and external aspects of artificial designs in contemporary society are considered through the perspective of rationality, which leads to the transition from intelligence to rationality in the Internet as well as to the historicity of information. (shrink)

A central thesis of intelligent‐design theorists is that physical and chemical laws and chance are insufficient to account for irreducibly complex biological structures and that intelligent design is necessary to account for such phenomena. This assertion, however, still implies a reductionist ontology. We need to recognize that reality displays multiple modes of being beyond simply chemical and physical modes of being, each of which is governed by laws for that mode of being. This essay argues for an alternate framework (...) for understanding life phenomena that is neither philosophical materialism nor intelligent‐design theory. (shrink)

The notion of intelligence is relevant to several fields of research, including cognitive and comparative psychology, neuroscience, artificial intelligence, and philosophy, among others. However, there is little agreement within and across these fields on how to characterise and explain intelligence. I put forward a behavioural, operational characterisation of intelligence that can play an integrative role in the sciences of intelligence, as well as preserve the distinctive explanatory value of the notion, setting it apart from the (...) related concepts of cognition and rationality. Finally, I examine a popular hypothesis about the underpinnings of intelligence: the capacity to manipulate internal representations of the environment. I argue that the hypothesis needs refinement, and that so refined, it applies only to some forms of intelligence. (shrink)

The emerging crealectic frame posits that there are three complementary and effectual domains of intelligence, namely analytic, dialectic, and crealectic, being alternatively or complementarily used in human interactions with the world. The focus of crealectic intelligence is the relative possibilization and local realization of absolute possibility, the becoming real, biological, and social of creation. This multimodal externalization and asymptotic unification of a cosmological flux expresses itself via three realms of possibilization: physical, psychological, and philosophical. But the philosophical possible (...) is not merely abstract; it originates a generative process of exteriorizations, interiorizations, dissolutions, and unifications transforming the possible into realities. The term “crealectics,” coined by philosopher and author Luis de Miranda, is a compound of “Creal” and of two possible suffixes: “logos” or “ektos”. The ontological core of crealectics, the Creal, is the immanent process of creation understood as a ubiquitous stream of absolute possibility exteriorizing itself. For a crealectician, philosophy is not the mere logical analysis of truth conditions, but the self-questioning enterprise of thought regarding its own possibilizing and world-making power. (shrink)

Disagreement over what exists is so fundamental that it tends to hinder or even to block dialogue among disputants. The various controversies between believers and atheists, or realists and nominalists, are only two kinds of examples. Interested in contributing to the intelligibility of the debate on ontology, in 1939 Willard van Orman Quine began a series of works which introduces the notion of ontological commitment and proposes an allegedly objective criterion to identify the exact conditions under which a theoretical (...) discourse signals an assumption of existence. I intend to present the concept of ontological commitment and the Quinean criterion, to expose and evaluate some of the many criticisms to which the criterion has subject and to situate it in the context of Quine’s philosophy. As a product of such analyses, I hope to contribute to the discussion on the application and relevance of the notion of ontological commitment. (shrink)

The common conception of intelligence in terms of information processing has its origin in cybernetics and information technology. Its import into cognitive science and the humanities not only generates theoretical problems, but also constitutes the basis of methods and policies that have adverse impacts on intelligent agents. In order to demonstrate why this technological conception of intelligence is not suitable for addressing the intelligence of living agents, and why natural intelligence is artificially not imitable, first, the (...) basic notions, formalisms, and assumptions of the technological context are presented. Next, the cybernetic feedback scheme is modified by dropping the technological assumptions, and a more sophisticated closed-loop scheme is developed that is better suited for representing cognitive processes in living agents. With reference to the thus developed scheme, the capacity of achieving ontological expansion, via what will be called “internal restructuring,” is proposed as the criterion for intelligence. (shrink)

일상의 자연어로 인간과 소통하는 기계가 우리 주변을 채우고 있다. 이는 기계가 인간의 언어를 이해하는 것처럼 보이게 만든다. 물론 기계가 인간의 언어를 실제로 이해하는 것은 아니다. 기계가 인간의 언어를 이해하는 것처럼 보이는 까닭은 기계가 인간의 자연어가 갖고 있는 구조와 어휘들을 학습함으로써 자연어의 의미론적 구조를 모방할 수 있게 되었기 때문이다. 정보공학에서는 기계가 인간과 의사소통할 수 있도록 정보를 구조화하는 작업을 ‘온톨로지(ontology)’라고 부른다. 최근 정보공학 분야에서 사용하는 온톨로지는 후설의 형식적 존재론과 영역 존재론의 프로젝트와 구조적으로 대단히 유사하다. 이는 정보공학과 후설 현상학이 서로 협업할 수 (...) 있는 접점이 있음을 뜻한다. 이 논문은 기계가 인간의 자연어를 이해하는 것처럼 보이는 일이 어떻게 가능한지를 살펴보고 현상학과 정보공학이 협업할 수 있는 접점을 시사하고자 한다. (shrink)

The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR (...) systems (AS-CATO, which is based on CATO, and IBP) are analysed in terms of basic and intermediate concepts. Central components of the OWL ontologies for these systems are presented, pointing out differences and similarities. The main novelty of the paper is the ontological analysis and representation in OWL of LCBR systems. The paper also emphasises the important issues concerning the representation and reasoning of intermediate concepts. (shrink)

Researchers in areas such as artificial intelligence, formal and computational linguistics, biomedical informatics, conceptual modeling, knowledge engineering and information retrieval have come to realise that a solid foundation for their research calls for serious work in ontology, understood as a general theory of the types of entities and relations that make up their respective domains of inquiry. In all these areas, attention is now being focused on the content of information rather than on just the formats and languages (...) used to represent information. The clearest example of this development is provided by the many initiatives growing up around the project of the Semantic Web. And, as the need for integrating research in these different fields arises, so does the realisation that strong principles for building well-founded ontologies might provide significant advantages over ad hoc, case-based solutions. The tools of formal ontology address precisely these needs, but a real effort is required in order to apply such philosophical tools to the domain of information systems. Reciprocally, research in the information sciences raises specific ontological questions which call for further philosophical investigations. The purpose of FOIS is to provide a forum for genuine interdisciplinary exchange in the spirit of a unified effort towards solving the problems of ontology, with an eye to both theoretical issues and concrete applications. This book contains a wide range of areas, all of which are important to the development of formal ontologies. (shrink)

Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often (...) misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems. (shrink)

The ability to access and share data is key to optimizing and streamlining any industrial production process. Unfortunately, the manufacturing industry is stymied by a lack of interoperability among the systems by which data are produced and managed, and this is true both within and across organizations. In this paper, we describe our work to address this problem through the creation of a suite of modular ontologies representing the product life cycle and its successive phases, from design to end of (...) life. We call this suite the Product Life Cycle (PLC) Ontologies. The suite extends proximately from The Common Core Ontologies (CCO) used widely in defense and intelligence circles, and ultimately from the Basic Formal Ontology (BFO), which serves as top level ontology for the CCO and for some 300 further ontologies. The PLC Ontologies were developed together, but they have been factored to cover particular domains such as design, manufacturing processes, and tools. We argue that these ontologies, when used together with standard public domain alignment and browsing tools created within the context of the Semantic Web, may offer a low-cost approach to solving increasingly costly problems of data management in the manufacturing industry. (shrink)

“Ontological emergence” of inherent high-level properties with causal powers is witnessed nowhere. A non-substantialist conception of emergence works much better. It allows downward causation, provided our concept of causality is transformed accordingly.

Will existing forms of artificial intelligence (AI) lead to genuine intelligence? How is AI changing our society and politics? This essay examines the answers to these questions in Brian Cantwell Smith's The Promise of Artificial Intelligence and Mark Coeckelbergh's The Political Philosophy of AI with a focus on their central concern with judgment—whether AI can possess judgment and how developments in AI are affecting human judgment. First, I argue that the existentialist conception of judgment that Smith defends (...) is highly idealized. While it may be an appropriate standard for intelligence, its implications for when and how AI should be deployed are not as clear as Smith suggests. Second, I point out that the concern with the displacement of judgment in favor of “reckoning” (or calculation) predates the rise of AI. The meaning and implications of this trend will become clearer if we move beyond ontology and metaphysics and into political philosophy, situating technological changes in their social context. Finally, I suggest that Coeckelbergh's distinctly political conception of judgment might offer a solution to the important boundary-drawing problem between tasks requiring judgment and those requiring reckoning, thus filling a gap in Smith's argument and clarifying its political stakes. (shrink)

The tendency to idealise artificial intelligence as independent from human manipulators, combined with the growing ontological entanglement of humans and digital machines, has created an “anthrobotic” horizon, in which data analytics, statistics and probabilities throw our agential power into question. How can we avoid the consequences of a reified definition of intelligence as universal operation becoming imposed upon our destinies? It is here argued that the fantasised autonomy of automated intelligence presents a contradistinctive opportunity for philosophical consciousness (...) to understand itself anew as holistic and co-creative, beyond the recent “analytic” moment of the history of philosophy. Here we introduce the concept of “crealectic intelligence”, a meta-analytic and meta-dialectic aspect of consciousness. Intelligent behaviour may consist in distinguishing discrete familiar parts or reproducible functions in the midst of noise via an analytic process of segmentation; intelligence may also manifest itself in the constitution of larger wholes and dynamic unities through a dialectic process of association or assemblage. But, by contrast, crealectic intelligence co-creates realities in the image of an ideal or truth, taking into account the desiring agent imbued with a sense of possibility, in a relationship not only with the Real but also with the creative sublime or “Creal”. (shrink)

The exploration of metaphysical arguments in the symbolic AI environment provides clarification and raises unexpected questions about notions in philosophy of religion and theology. Recent attempts to apply automatic theorem prover technology to Anselm's ontological argument have led to a simplification of the argument. This computationally discovered simplification has given rise to logical observations. The article assesses one of these observations: the application of the diagonal method (in Cantor's version) to Anselm's argument. The evaluation of the applications of theorem provers (...) to metaphysical and theological arguments contributes to the following topics in philosophy of religion: the limits of natural theology, the relationship between religion and STEM, and theology's scientificity. (shrink)

Smart parsimonious and economical ways of irrigation have build up to fulfill the sweet water requirements for the habitants of this world. In other words, water consumption should be frugal enough to save restricted sweet water resources. The major portion of water was wasted due to incompetent ways of irrigation. We utilized a smart approach professionally capable of using ontology to make 50% of the decision, and the other 50% of the decision relies on the sensor data values. The (...) decision from the ontology and the sensor values collectively become the source of the final decision which is the result of a machine learning algorithm. Moreover, an edge server is introduced between the main IoT server and the GSM module. This method will not only avoid the overburden of the IoT server for data processing but also reduce the latency rate. This approach connects Internet of Things with a network of sensors to resourcefully trace all the data, analyze the data at the edge server, transfer only some particular data to the main IoT server to predict the watering requirements for a field of crops, and display the result by using an android application edge. (shrink)

This chapter contains sections titled: Preliminaries Ontology and Pragmatism Heidegger's Ambiguity on Pragmatism and Technology Articulating Technology.

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, (...) pathogenesis, diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

By following the arguments developed by Vygotsky and employing the cultural-historical activity theory (CHAT) in addition to dialectical logic, this paper attempts to investigate the interaction between psychology and artificial intelligence (AI) to confront the epistemological and methodological challenges encountered in AI research. The paper proposes that AI is facing an epistemological and methodological crisis inherited from psychology based on dualist ontology. The roots of this crisis lie in the duality between rationalism and objectivism or in the mind-body (...) rupture that has governed the production of scientific thought and the proliferation of approaches. In addition, by highlighting the sociohistorical conditions of AI, this paper investigates the historical characteristics of the shift of the crisis from psychology to AI. Additionally, we examine the epistemological and methodological roots of the main challenges encountered in AI research by noting that empiricism is the dominant tendency in the field. Empiricism gives rise to methodological and practical challenges, including challenges related to the emergence of meaning, abstraction, generalization, the emergence of symbols, concept formation, functional reflection of reality, and the emergence of higher psychological functions. Furthermore, through discussing attempts to formalize dialectical logic, the paper, based on contradiction formation, proposes a qualitative epistemological, methodological, and formal alternative by using a preliminary algorithmic model that grasps the formation of meaning as an essential ability for the qualitative reflection of reality and the emergence of other mental functions. (shrink)

We describe a strategy for integration of data that is based on the idea of semantic enhancement. The strategy promises a number of benefits: it can be applied incrementally; it creates minimal barriers to the incorporation of new data into the semantically enhanced system; it preserves the existing data (including any existing data-semantics) in their original form (thus all provenance information is retained, and no heavy preprocessing is required); and it embraces the full spectrum of data sources, types, models, and (...) modalities (including text, images, audio, and signals). The result of applying this strategy to a given body of data is an evolving Dataspace that allows the application of a variety of integration and analytic processes to diverse data contents. We conceive semantic enhancement (SE) as a lightweight and flexible process that leverages the richness of the structured contents of the Dataspace without adding storage and processing burdens to what, in the intelligence domain, will be an already storage- and processing-heavy starting point. SE works not by changing the data to which it is applied, but rather by adding an extra semantic layer to this data. We sketch how the semantic enhancement approach can be applied consistently and in cumulative fashion to new data and data-models that enter the Dataspace. (shrink)

McAllestar (computer science, MIT) describes ONTIC, the interactive system for verifying represents a significant change of direction in the field of mechanical deduction, a key area in computer science and artificial intelligence. Fourteen interrelated essays comprise a multifaceted dialogue about intersubjectivity, reciprocity, and the nature of self and other, especially as these themes are developed in Merleau-Ponty's The Visible and the invisible. The question they explore is whether the reversible alterity of sensing and being sensed, a theme at the (...) heart of Merleau-Ponty's thought, is sufficient for understanding the alterity of other persons and of nature. Annotation copyrighted by Book News, Inc., Portland, OR. (shrink)