Abstract Artificial language philosophy (also called ‘ideal language philosophy’) is the position that philosophical problems are best solved or dissolved through a reform of language. Its underlying methodology—the development of languages for specific purposes—leads to a conventionalist view of language in general and of concepts in particular. I argue that many philosophical practices can be reinterpreted as applications of artificial language philosophy. In addition, many factually occurring interrelations between the sciences (...) and philosophy of science are justified and clarified by the assumption of an artificial language methodology. (shrink)

When one looks into the role of artificial languages in philosophy of language it seems appropriate to start with making a distinction between philosophy of language proper and formal semantics of natural language. Although the distinction between the two disciplines may not always be easy to make since there arguably exist substantial historical and systematic relationships between the two, it nevertheless pays to keep the two apart, at least initially, since the motivation commonly given (...) for the use of artificial languages in philosophy of language is often rather different from the one that drives the use of such languages in semantics. Of course, this difference in motivation should not blind us for the commonalities that exists between the two disciplines. Philosophy of language and formal semantics have a common history, and arguably also share some of their substance. Philosophy of language is by and large an outgrowth of work in the analytical tradition in philosophy in the first half of the twentieth century. Both ordinary language philosophy, with its emphasis on the description of actual language use, as well as the more logic-oriented and formally inclined school of logical positivism contributed to the definition of philosophy of language as a separate philosophical discipline, with its own set of problems and methods to solve them. Another major contributor to the establishment of philosophy of language as a distinct discipline has been modern linguistics, in particular generative grammar in the tradition of Chomsky that became the dominant paradigm in linguisticsin the fifties and sixties of the previous century. And as it happens, both the generative tradition of Chomsky and analytic philosophy in its formal and less formal guises have been important factors in the development of formal semantics as well. Thus, it should come as no surprise that the two have something in common. That the communalities go beyond a common ancestry, but are reflected in substance and methods as well, will be argued later on. However, be that as it may, it is still a good idea to keep philosophy of language and formal semantics separate, at least initially, since the role that is assigned to artificial languages and the ways in which these languages are employed in both does differ in a number of respects that are worth keeping in mind.. (shrink)

Medieval Arabic algebra is a good example of an artificial language.Yet despite its abstract, formal structure, its utility was restricted to problem solving. Geometry was the branch of mathematics used for expressing theories. While algebra was an art concerned with finding specific unknown numbers, geometry dealtwith generalmagnitudes.Algebra did possess the generosity needed to raise it to a more theoretical level—in the ninth century Abū Kāmil reinterpreted the algebraic unknown “thing” to prove a general result. But mathematicians had no (...) motive to rework their theories in algebraic form. Because it offered no advantage over geometry, algebra remained a practical art in both the Islamic world and in Europe until the scientific uphevals of the 17th–18th centuries. (shrink)

This series will include monographs and collections of studies devoted to the investigation and exploration of knowledge, information and data-processing systems of all kinds, no matter whether human, (other) animal or machine. Its scope is intended to span the full range of interests from classical problems in the philosophy of mind and phi losophical psychology through issues in cognitive psychology and socio biology (concerning the mental capabilities of other species) to ideas related to artificial intelligence and computer science. (...) While primary emphasis will be placed upon theoretical, conceptual and epistemologi cal aspects of these problems and domains, empirical, experimental and methodological studies will also appear from time to time. Among the most challenging and difficult projects within the scope of artificial intelligence is the development and implementation of com puter programs suitable for processing natural language. Our purpose in compiling the present volume has been to contribute to the foundations of this enterprise by bringing together classic papers devoted to crucial problems involved in understanding natural language, which range from issues of formal syntax and logical form to those of possible-worlds and situation semantics. The book begins with a comprehensive introduc tion composed by Jack Kulas, the senior editor of this work, which pro vides a systematic orientation to this complex field, and ends with a selected bibliography intended to promote further research. If our efforts assist others in dealing with these problems, they will have been worthwhile. J. H. F. (shrink)

Western language philosophy studies meaning from diverse aspects, with a core concern for how meaning is formulated and interpreted. The artificial-language and natural-language schools are two camps in this philosophical undertaking, the former insisting on scientific logic and positivism in meaning verification while the latter emphasizing subjective intention and context in meaning interpretation. Semiotics provides another semantic perspective that tips toward the theory of the natural-language school. This article compares the semantic thought of analytical (...) language philosophers with that of a Chinese semiotician – Yiheng Zhao, who defines meaning as the interpretative potential between any two signs, and, being the product of signifying activities, meaning should be stipulated as dynamic process instead of a static essence. Thus, the interpretation of meaning is totally free of the shackles of logical positivism and radical interpretation required by the artificial-language school. On the other hand, differing from the natural-language school, meaning in Zhao’s semiotic theory can be either expressive or communicative, which means meaning that has originated from an expresser does not necessarily need an interpreter like the utterer-audience binary in Grice’s theory. Compared with Anglo-American analytical language philosophers, Zhao shows more affinities in semantic thought with the continental philosophers – Husserl, Heidegger, Gadamer, Habermas, and Ricoeur. (shrink)

Although philosophising about given languages had been going on ever since the time of Plato's Kratylos, the idea of an artificial philosophical language or system of signs began to take shape in the seventeenth century. Both Descartes and Mersenne explored the ground for the foundations of a system of expressions which could meet all the requirements of logical thought; but the merit of presenting the first elaborate plans goes to the British authors George Dalgarno and John Wilkins. 1 (...) Leibniz followed soon after with his characteristica universalis. (shrink)

Although philosophising about given languages had been going on ever since the time of Plato's Kratylos , the idea of an artificial philosophical language or system of signs began to take shape in the seventeenth century. Both Descartes and Mersenne explored the ground for the foundations of a system of expressions which could meet all the requirements of logical thought; but the merit of presenting the first elaborate plans goes to the British authors George Dalgarno and John Wilkins. (...) 1 Leibniz followed soon after with his characteristica universalis. (shrink)

I try to show that there is much sap (synthetic a priori) knowledge although one may not find many, or even any, sap true statements in most natural languages. Reasons are given for the difficulty of expressing sap truths in natural languages, but it is argued that these are not necessary features of language as such. There are, then, sap true statements in some possible languages.Admission of the sap gives one a way of distinguishing logical from metaphysical possiblity. Something (...) is metaphysically impossible just in case it is a sap truth that it is impossible. I argue that the realm of logically possible entities is vastly larger than the realm of metaphysically possible entities.My.defence of the sap begins with a partial defence of the analytic/synthetic distinction. In particular, I attack the intentionality circle argument,Quine's indeterminacy thesis and some applications of the Kripke/Putnam theory of meaning for natural kind terms. Without being a conclusive defence of the analytic/synthetic distinction, this section makes the defence of the sap more secure. Arguments are then mounted in direct defence of the sap by showing that the purported reduction of sap truths to analytic truths is mistaken. Examples are adduced to show that, even where expressions of natural language fail, one can stipulate usage in ways that allow expression of sap truths. I then consider and reply to various objections, giving an example of the sort of meaning theory which illegitimately legislates the analyticity of any purported sap true statement. (shrink)

What is philosophy about and what are its methods? _Philosophy and Ordinary Language_ is a defence of the view that philosophy is largely about questions of language, which to a large extent means _ordinary_ language. Some people argue that if philosophy is about ordinary language, then it is necessarily less deep and difficult than it is usually taken to be but Oswald Hanfling shows us that this isn't true. Hanfling, a leading expert in the (...) development of analytic philosophy, covers a wide range of topics, including scepticism and the definition of knowledge, free will, empiricism, folk psychology, ordinary versus artificial logic, and philosophy versus science. Drawing on philosophers such as Austin, Wittgenstein, and Quine, this book explores the nature of ordinary language in philosophy. (shrink)

The theory and philosophy of artificial intelligence has come to a crucial point where the agenda for the forthcoming years is in the air. This special volume of Minds and Machines presents leading invited papers from a conference on the “Philosophy and Theory of Artificial Intelligence” that was held in October 2011 in Thessaloniki. Artificial Intelligence is perhaps unique among engineering subjects in that it has raised very basic questions about the nature of computing, perception, (...) reasoning, learning, language, action, interaction, consciousness, humankind, life etc. etc. – and at the same time it has contributed substantially to answering these questions. There is thus a substantial tradition of work, both on AI by philosophers and of theory within AI itself. - The volume contains papers by Bostrom, Dreyfus, Gomila, O'Regan and Shagrir. (shrink)

Large-scale language technologies are increasingly used in various forms of communication with humans across different contexts. One particular use case for these technologies is conversational agents, which output natural language text in response to prompts and queries. This mode of engagement raises a number of social and ethical questions. For example, what does it mean to align conversational agents with human norms or values? Which norms or values should they be aligned with? And how can this be accomplished? (...) In this paper, we propose a number of steps that help answer these questions. We start by developing a philosophical analysis of the building blocks of linguistic communication between conversational agents and human interlocutors. We then use this analysis to identify and formulate ideal norms of conversation that can govern successful linguistic communication between humans and conversational agents. Furthermore, we explore how these norms can be used to align conversational agents with human values across a range of different discursive domains. We conclude by discussing the practical implications of our proposal for the design of conversational agents that are aligned with these norms and values. (shrink)

This paper explores the challenges posed by the rapid advancement of artificial intelligence specifically Large Language Models (LLMs). I show that traditional linguistic theories and corpus studies are being outpaced by LLMs’ computational sophistication and low perplexity levels. In order to address these challenges, I suggest a focus on language as a cognitive tool shaped by embodied-environmental imperatives in the context of _Agentive Cognitive Construction Grammar_. To that end, I introduce an _Embodied Human Language_ Model (EHLM), inspired (...) by Active Inference research, as a promising alternative that integrates sensory input, embodied representations, and adaptive strategies for contextualized analysis and conceptual utility maximization. By incorporating Active Inference, which sees perception as inferring the world's state from sensory data, the findings reveal that the characterization of the English modal _be able to_, as a triadic construction encoding biological intelligent agency, introduces a more plausible theoretical basis for the positing of linguistic constructions. This emphasizes the crucial role of embodied human language models in the comprehension of how humans construct preferred futures through language. (shrink)

A wide-ranging history of the intellectual developments that produced the modern idea of the algorithm. Bringing together the histories of mathematics, computer science, and linguistic thought, Language and the Rise of the Algorithm reveals how recent developments in artificial intelligence are reopening an issue that troubled mathematicians long before the computer age. How do you draw the line between computational rules and the complexities of making systems comprehensible to people? Here Jeffrey M. Binder offers a compelling tour of (...) four visions of universal computation that addressed this issue in very different ways: G. W. Leibniz's calculus ratiocinator; a universal algebra scheme Nicolas de Condorcet designed during the French Revolution; George Boole's nineteenth-century logic system; and the early programming language ALGOL, whose name is short for algorithmic language. These episodes show that symbolic computation has repeatedly become entangled in debates about the nature of communication. To what extent can meaning be controlled by individuals, like the values of a and b in algebra, and to what extent is meaning inevitably social? By attending to this long-neglected question, we come to see that the modern idea of the algorithm is implicated in a long history of attempts to maintain a disciplinary boundary separating technical knowledge from the languages people speak day to day. Machine learning, in its increasing dependence on words, now places this boundary in jeopardy, making its stakes all the more urgent to understand. The idea of the algorithm is a levee holding back the social complexity of language, and it is about to break. This book is about the flood that inspired its construction. (shrink)

As China and the United States strive to be the primary global leader in AI, their visions are coming into conflict. This is frequently painted as a fundamental clash of civilisations, with evidence based primarily around each country’s current political system and present geopolitical tensions. However, such a narrow view claims to extrapolate into the future from an analysis of a momentary situation, ignoring a wealth of historical factors that influence each country’s prevailing philosophy of technology and thus their (...) overarching AI strategies. In this article, we build a philosophy-of-technology-grounded framework to analyse what differences in Chinese and American AI policies exist and, on a fundamental level, why they exist. We support this with Natural Language Processing methods to provide an evidentiary basis for our analysis of policy differences. By looking at documents from three different American presidential administrations––Barack Obama, Donald Trump, and Joe Biden––as well as both national and local policy documents (many available only in Chinese) from China, we provide a thorough comparative analysis of policy differences. This article fills a gap in US–China AI policy comparison and constructs a framework for understanding the origin and trajectory of policy differences. By investigating what factors are informing each country’s philosophy of technology and thus their overall approach to AI policy, we argue that while significant obstacles to cooperation remain, there is room for dialogue and mutual growth. (shrink)

We discuss the difference between formal and natural languages, and argue that should the language metaphor have any foundation, it’s analogy with natural languages that should be taken into account. We discuss how such operation like reading, writing, sign, interpretation, etc., can be applied in the realm of the living and what can be gained, by such an approach, in order to understand the phenomenon of life.

Some of the systems used in natural language generation (NLG), a branch of applied computational linguistics, have the capacity to create or assemble somewhat original messages adapted to new contexts. In this paper, taking Bernard Williams’ account of assertion by machines as a starting point, I argue that NLG systems meet the criteria for being speech actants to a substantial degree. They are capable of authoring original messages, and can even simulate illocutionary force and speaker meaning. Background intelligence embedded (...) in their datasets enhances these speech capacities. Although there is an open question about who is ultimately responsible for their speech, if anybody, we can settle this question by using the notion of proxy speech, in which responsibility for artificial speech acts is assigned legally or conventionally to an entity separate from the speech actant. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding.There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial intelligence (...) and neuroscience. John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Presupposing no familiarity with the technical concepts of either philosophy or computing, this clear introduction reviews the progress made in AI since the inception of the field in 1956. Copeland goes on to analyze what those working in AI must achieve before they can claim to have built a thinking machine and appraises their prospects of succeeding. There are clear introductions to connectionism and to the language of thought hypothesis which weave together material from philosophy, artificial (...) intelligence and neuroscience. John Searle's attacks on AI and cognitive science are countered and close attention is given to foundational issues, including the nature of computation, Turing Machines, the Church-Turing Thesis and the difference between classical symbol processing and parallel distributed processing. The book also explores the possibility of machines having free will and consciousness and concludes with a discussion of in what sense the human brain may be a computer. (shrink)

Historically, the dispute between empiricists and nativists in philosophy and cognitive science has concerned human and animal minds (Margolis and Laurence in Philos Stud: An Int J Philos Anal Tradit 165(2): 693-718, 2013, Ritchie in Synthese 199(Suppl 1): 159–176, 2021, Colombo in Synthese 195: 4817–4838, 2018). But recent progress has highlighted how empiricist and nativist concerns arise in the construction of artificial systems (Buckner in From deep learning to rational machines: What the history of philosophy can teach (...) us about the future of artificial intelligence. Oxford University Press.). This paper uses nativism and empiricism to address questions about the nature of artificial intelligence and its trajectory. It begins by defining the nativism/empiricism debate in terms of the generality of a system. Nativist systems have initial states with domain-specific features; empiricist systems have initial states with only domain-general features. With the debate framed in this way, it then explores a variety of arguments for nativism and empiricism in AI. These arguments revolve around two different questions which must be distinguished: whether nativism the only possible approach to developing human-level AI (HLAI); and whether nativism is the most practical approach to developing HLAI. On the first question, it argues that nativism is quite clearly not the only possible approach to developing HLAI, as is sometimes suggested. It argues that existing arguments for the necessity of nativism are unconvincing, because they analogize from poverty of the stimulus arguments about humans, while AIs often have access to much more data than humans. Then it argues that the case of evolution gives us a compelling argument against nativism. On the second, practical question, the paper argues that there is a tradeoff between the advantages of encoding innate machinery directly, and the advantages of evolving or learning it. However, as the past decade has shown, empiricism is a much more viable path to greater capability levels, given the ‘bitter lesson’ (Sutton in _Reinforcement Learning: An Introduction_. MIT press.) that encoding the ‘correct’ knowledge in AI systems is perennially outperformed by more empiricist methods that leverage large-scale data and computation. (shrink)

Language, one of humanity’s major transformative innovations, is foundational for many cultural, artistic, scientific, and economic advancements, including the creation of artificial intelligence (AI). However, in the fight against climate change, the power of such innovation is constrained due to the boring language of climate science and science communication. In this essay, we encapsulated the situation and risks of boring language in communicating climate information to the public and countering climate denialism and disinformation. Based on the (...) Serendipity-Mindsponge-3D knowledge management framework, we recommend several strategies for climate scientists and science communicators to be more creative and make their communication more interesting, including collaboration with other cultural sectors (e.g., stand-up comedians, climate fiction, etc.) and AI. (shrink)

Human beings express affinity (Shinwa‐kan in Japanese language) in communicating transactive engagements among healthcare providers, patients and healthcare robots. The appearance of healthcare robots and their language capabilities often feature characteristic and appropriate compassionate dialogical functions in human–robot interactions. Elements of healthcare robot configurations comprising its physiognomy and communication properties are founded on the positivist philosophical perspective of being the summation of composite parts, thereby mimicking human persons. This article reviews Mori's theory of the Uncanny Valley and its (...) consequent debates, and examines "Uncanny" relations with generating healthcare robot conversational content with artificial affective communication (AAC) using natural language processing. With healthcare robots provoking influential physical composition and sensory expressions, the relations in human–healthcare robot transactive engagements are argued as supportive of the design and development in natural language processing. This implies that maintaining human–healthcare robot interaction and assessing the eeriness situations explained in the Uncanny Valley theory are crucial positions for healthcare robot functioning as a valuable commodity in health care. As such, physical features, language capabilities and mobility of healthcare robots establish the primacy of the AAC with natural language processing as integral to healthcare robot–human healthcare practice. (shrink)

Will language as we know it cease to exist? What could this mean for the way we live our lives? Shining a light on the technology currently being developed to revolutionise communication, The Future of Language distinguishes myth from reality and superstition from scientifically-based prediction as it plots out the importance of language and raises questions about its future.From the rise of artificial intelligence and speaking robots, to brain implants and computer-facilitated telepathy, language and communications (...) expert Philip Seargeant surveys the development of new digital 'languages', such as emojis, animated gifs and memes, and investigates how conventions of spoken and written language are being modified by new trends in communication. From George Orwell's fictional predictions in Nineteen Eighty-Four to the very real warnings of climate activist Greta Thunberg, Seargeant explores language through time, traversing politics, religion, philosophy, literature, and of course technology, in the process. Tracing how previous eras have imagined the future of language, from the Bible to the works H. G. Wells, and from Star Wars to Star Trek, the book reveals how perfecting language and communication has always been a vital component of utopian dreams of the future. Questioning the potential ramifications of recent and future developments in communication on society and its ideals, The Future of Language is a no holds barred investigation into the state of civilisation and the impact that changes in language could have on our lives"--Publisher's description. (shrink)

Methodological nationalism in sociological theory is unfit for the current globalized era, and should be discarded. In light of this contention, the present article discusses Max Weber’s view of language as a way to relativize the frame of the national society. While a “linguistic turn” in sociology since the 1960s has assumed that the sharing of language—linguistic community—stands as an intersubjective foundation for understanding of meaning, Weber saw linguistic community as constructed. From Weber’s rationalist, subjectivist, individualist viewpoint, linguistic (...) community was a result of social actions, not a prior entity as assumed by German metaphysical organicism (and historicist holism). Indeed, Central Europe in Weber’s era was a battlefield of linguistic nationalism(s); in contrast to the national societies of the Cold War period, national borders were unstable and ultimately the multiethnic empires of the region were dismantled after World War I into ethnolinguistic nation-states. Experience of this contemporary reality brought Weber to the core of the relationship between language and politics: A language community is an imaginary one demarcated not by language itself but by conscious opposition against outsiders, with monolingual contexts within borders created artificially by homogenizing policies like linguistic standardization and national education—the first modernity of language. In this way, Weber felt, language can be a means to domination. (shrink)

Transformer-based language models (TLMs) have widely been recognized to be a cutting-edge technology for the successful development of deep-learning-based solutions to problems and applications that require natural language processing and understanding. Like for other textual domains, TLMs have indeed pushed the state-of-the-art of AI approaches for many tasks of interest in the legal domain. Despite the first Transformer model being proposed about six years ago, there has been a rapid progress of this technology at an unprecedented rate, whereby (...) BERT and related models represent a major reference, also in the legal domain. This article provides the first systematic overview of TLM-based methods for AI-driven problems and tasks in the legal sphere. A major goal is to highlight research advances in this field so as to understand, on the one hand, how the Transformers have contributed to the success of AI in supporting legal processes, and on the other hand, what are the current limitations and opportunities for further research development. (shrink)

Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5’s legal reasoning and ChatGPT’s legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on (...) complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5’s legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors’ decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT’s drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM’s legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct. (shrink)

Temporal Logic: From Ancient Ideas to Artificial Intelligence deals with the history of temporal logic as well as the crucial systematic questions within the field. The book studies the rich contributions from ancient and medieval philosophy up to the downfall of temporal logic in the Renaissance. The modern rediscovery of the subject, which is especially due to the work of A. N. Prior, is described, leading into a thorough discussion of the use of temporal logic in computer science (...) and the understanding of natural language. Temporal Logic: From Ancient Ideas to Artificial Intelligence thus interweaves linguistic, philosophical and computational aspects into an informative and inspiring whole. (shrink)

This article sets in with the question whether current or foreseeable transformer-based large language models (LLMs), such as the ones powering OpenAI’s ChatGPT, could be language users in a way comparable to humans. It answers the question negatively, presenting the following argument. Apart from niche uses, to use language means to act. But LLMs are unable to act because they lack intentions. This, in turn, is because they are the wrong kind of being: agents with intentions need (...) to be autonomous organisms while LLMs are heteronomous mechanisms. To conclude, the article argues, based on structural aspects of transformer-based LLMs, that these LLMs have taken a first step away from mechanistic artificiality to autonomous self-constitution, which means that these models are (slowly) moving into a direction that someday might result in non-human, but equally non-artificial agents, thus subverting the time-honored Kantian distinction between organism and mechanism. (shrink)

Can large language models produce expert‐quality philosophical texts? To investigate this, we fine‐tuned GPT‐3 with the works of philosopher Daniel Dennett. To evaluate the model, we asked the real Dennett 10 philosophical questions and then posed the same questions to the language model, collecting four responses for each question without cherry‐picking. Experts on Dennett's work succeeded at distinguishing the Dennett‐generated and machine‐generated answers above chance but substantially short of our expectations. Philosophy blog readers performed similarly to the (...) experts, while ordinary research participants were near chance distinguishing GPT‐3's responses from those of an “actual human philosopher”. (shrink)

In this paper I critically examine the notion of explanation used in Artificial Intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific (...) practice. At the end of the paper I outline the desiderata for an epistemologically motivated, scientifically informed belief revision model for explanation. (shrink)

In this essay, I discuss two familiar objections to Hume's account of cognition, focusing on his ability to give a satisfactory account of the more normative dimensions of thought and language use. In doing so, I argue that Hume’s implicit account of these issues is far richer than is normally assumed. In particular, I show that Hume’s account of convention-driven artificial virtues like justice also applies to the proper use of conventional public languages. I then use this connection (...) between Hume’s conception of language and his moral theory to show how he can respond to a number of basic objections to his views. In doing so, I explore the sense in which human cognition is essentially linguistic, and so social, for Hume, as well as many other issues concerning the relationship between Hume’s philosophy of mind and language, his epistemology, and his ethics. (shrink)

The purpose of this book, originally published in 1987, was to contribute to the advance of artificial intelligence by clarifying and removing the major sources of philosophical confusion at the time which continued to preoccupy scientists and thereby impede research. Unlike the vast majority of philosophical critiques of AI, however, each of the authors in this volume has made a serious attempt to come to terms with the scientific theories that have been developed, rather than attacking superficial 'straw men' (...) which bear scant resemblance to the complex theories that have been developed. For each is convinced that the philosopher's responsibility is to contribute from his own special intellectual point of view to the progress of such an important field, rather than sitting in lofty judgement dismissing the efforts of their scientific peers. The aim of this book is thus to correct some of the common misunderstandings of its subject. The technical term Artificial Intelligence has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. has created considerable unnecessary confusion because of the ordinary meanings associated with it, and for that very reason, the term is endlessly misused and abused. The essays collected here all aim to expound the true nature of AI, and to remove the ill-conceived philosophical discussions which seek answers to the wrong questions in the wrong ways. Philosophical discussions and decisions about the proper use of AI need to be based on a proper understanding of the manner in which AI-scientists achieve their results; in particular, in their dependence on the initial planning input of human beings. The collection combines the Anglo-Saxon school of analytical philosophy with scientific and psychological methods of investigation. The distinguished authors in this volume represent a cross-section of philosophers, psychologists, and computer scientists from all over the world. The result is a fascinating study in the nature and future of AI, written in a style which is certain to appeal and inform laymen and specialists alike. inform laymen and specialists alike. (shrink)

This book deals with a major problem in the study of language: the problem of reference. The ease with which we refer to things in conversation is deceptive. Upon closer scrutiny, it turns out that we hardly ever tell each other explicitly what object we mean, although we expect our interlocutor to discern it. Amichai Kronfeld provides an answer to two questions associated with this: how do we successfully refer, and how can a computer be programmed to achieve this? (...) Beginning with the major theories of reference, Dr Kronfeld provides a consistent philosophical view which is a synthesis of Frege's and Russell's semantic insights with Grice's and Searle's pragmatic theories. This leads to a set of guiding principles, which are then applied to a computational model of referring. The discussion is made accessible to readers from a number of backgrounds: in particular, students and researchers in the areas of computational linguistics, artificial intelligence and the philosophy of language will want to read this book. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and (...) Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the (...) World Wide Web, and six apply machine learning techniques to a variety of legal applications. (shrink)

Economic value additions to knowledge and demand provide practical, embedded and extensible meaning to philosophizing cognitive systems. Evaluation of a cognitive system is an empirical matter. Thinking of science in terms of distributed cognition (interactionism) enlarges the domain of cognition. Anything that actually contributes to the specific quality of output of a cognitive system is part of the system in time and/or space. Cognitive science studies behaviour and knowledge structures of experts and categorized structures based on underlying structures. Knowledge representation (...) through understanding of ‘epistemic cultures’ is an evolutionary stage. But cognition goes beyond knowledge representation. Notwithstanding the importance of epistemology of phenomena, the practicability cum philosophical aspects of machine learning needs to be seen in dynamic behaviour in socio-economic-technical value additions if human machine interaction processes that are context specific are incorporated into strong artificial intelligent systems. Cognitive Science is also studied from both computational and biological angles. Evolution of interactive forms of reasoning through understanding of meta-language of computations or biological learning processes is possible. But the limitation of historical cultures predefines the role of interactive processes in user-networks beyond technology networks. Despite this limitation, inclusive development notions of a heterogeneous national society such as India or Europe can be tested and incorporated. (shrink)

BackgroundArtificial intelligence has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: (...) What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective?MethodsEight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed.ResultsUpon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries.ConclusionsThe ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere. (shrink)

Although what language users in different cultures say about their own language has long been recognized as of potential interest, its theoretical importance to the study of language has typically been thought to be no more than peripheral. Theorizing Language is the first book to place the reflexive character of language at the very centre both of its empirical study and of its theoretical explanation. Language can only be explained as a cultural product of (...) the reflexive application of its own creative powers to construct, regulate, and give conceptual form to objects of understanding. Language is itself, first and foremost, an object of cultural understanding. Theoretical analyses of language which have neglected its reflexive character, or simply taken its effects for granted, merely impose their own artificial structures on their analytical object. The first part of this book discusses the consequences of neglecting this reflexive character for the technical concepts and methods which are used in analysing different types of communicational phenomena. In the second part, normativity - a crucial aspect of language's reflexive nature - is examined. The book's third and final part focuses on particular issues in the history of linguistic thought which bear witness to the rhetoric of language theorizing as a reflexive form of inquiry. (shrink)

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)