In this paper I argue that human beings should reason, not in accordance with classical logic, but in accordance with a weaker ‘reticent logic’. I characterize reticent logic, and then show that arguments for the existence of fundamental Gödelian limitations on artificial intelligence are undermined by the idea that we should reason reticently, not classically.

Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive ...

ABSTRACTWhile over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating “artificial minded intelligence” that can help to unify existing approaches and provide new avenues for research. I (...) first outline three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the “generative methodology,” and Section 3 develops a version of this methodology, the “Competence Framework for AI research”. (shrink)

Artificial intelligence (AI) assurance is an umbrella term describing many approaches—such as impact assessment, audit, and certification procedures—used to provide evidence that an AI system is legal, ethical, and technically robust. AI assurance approaches largely focus on two overlapping categories of harms: deployment harms that emerge at, or after, the point of use, and individual harms that directly impact a person as an individual. Current approaches generally overlook upstream collective and societal harms associated with the development of systems, such (...) as resource extraction and processing, exploitative labour practices and energy intensive model training. Thus, the scope of current AI assurance practice is insufficient for ensuring that AI is ethical in a holistic sense, i.e. in ways that are legally permissible, socially acceptable, economically viable and environmentally sustainable. This article addresses this shortcoming by arguing for a broader approach to AI assurance that is sensitive to the full scope of AI development and deployment harms. To do so, the article maps harms related to AI and highlights three examples of harmful practices that occur upstream in the AI supply chain and impact the environment, labour, and data exploitation. It then reviews assurance mechanisms used in adjacent industries to mitigate similar harms, evaluating their strengths, weaknesses, and how effectively they are being applied to AI. Finally, it provides recommendations as to how a broader approach to AI assurance can be implemented to mitigate harms more effectively across the whole AI supply chain. (shrink)

This paper describes the SNePS knowledge-representation and reasoning system. SNePS is an intensional, propositional, semantic-network processing system used for research in AI. We look at how predication is represented in such a system when it is used for cognitive modeling and natural-language understanding and generation. In particular, we discuss issues in the representation of fictional entities and the representation of propositions from fiction, using SNePS. We briefly survey four philosophical ontological theories of fiction and sketch an epistemological theory of fiction (...) using a story operator and rules for allowing propositions to migrate into and out of story spaces. (shrink)

Founded in 1993, ELEKTRIK: Turkish Journal of Electrical Engineering and Computer Sciences, has gradually become better known and is fast establishing itself as a research oriented publication outlet with high academic standards. In a modest attempt to advance this trend, this special issue of ELEKTRIK brings together five papers exemplifying the state of the art in artificial intelligence (AI). Written by experts, the papers are especially aimed at readers interested in gaining a better appraisal of the applications side of (...) the AI enterprise. In all papers there is a strong emphasis on measuring the benefits of the proposed approaches in experimental contexts. The papers broadly fall into five actively researched, contemporary domains of AI: pattern recognition, genetic algorithms, fuzzy sets, intelligent design, and agents. (shrink)

The Smart Home designed to extend older adults independence is emerging as a clinical solution to the growing ageing population. Nurses will and should play a key role in the development and application of Smart Home technology. Accordingly, conceptual frameworks are needed for nurse scientists who are collaborating with multidisciplinary research teams in developing an intelligent Smart Home that assists with managing older adults’ health. We present a conceptual framework that is grounded in critical realism and pragmatism, informing a unique (...) mixed methodological approach to generating, analyzing, and contextualizing sensor data for clinician‐based machine learning. This framework can guide nurse scientists in knowledge construction as they participate in multidisciplinary health‐assistive Smart Home and artificial intelligence research. In this paper, we review philosophical underpinnings and explicate how this framework can guide nurse scientists collaborating with engineers to develop intelligent health‐assistive Smart Homes. It is critical that clinical nursing knowledge is integrated into Smart Home and artificial intelligence features. A conceptual framework and practical method will provide needed structure for knowledge construction by nurse scientists. (shrink)

The notion of Simulative Reasoning in the study of propositional attitudes within Artificial Intelligence (AI) is strongly related to the Simulation Theory of mental ascription in Philosophy. Roughly speaking, when an AI system engages in Simulative Reasoning about a target agent, it reasons with that agent’s beliefs as temporary hypotheses of its own, thereby coming to conclusions about what the agent might conclude or might have concluded. The contrast is with non-simulative meta-reasoning, where the AI system reasons within a (...) detailed theory about the agent’s (conjectured) reasoning acts. The motive within AI for preferring Simulative Reasoning is that it is more convenient and efficient, because of a simplification of the representations and reasoning processes. The chapter discusses this advantage in detail. It also sketches the use of Simulative Reasoning in an AI natural language processing system, ATT-Meta, that is currently being implemented. This system is directed at the understanding of propositional attitude reports. In ATT-Meta, Simulative Reasoning is yoked to a somewhat independent set of ideas about how attitude reports should be treated. Central here are the claims that (a) speakers often employ commonsense (and largely metaphorical) models of mind in describing agents’ attitudes, (b) the listener accordingly needs often to reason within the terms of such models, rather than on the basis of any objectively justifiable characterization of the mind, and (c) the commonsense models filter the suggestions that Simulative Reasoning comes up with concerning target agents’ reasoning conclusions. There is a yet tighter connection between the commonsense models and the Simulative Reasoning. It turns out that Simulative Reasoning can be rationally reconstructed in terms of a more general type of reasoning about the possibly-counterfactual “world” that the target agent believes in, together with an assumption that that agent has a faithful representation of the world. In the ATT-Meta approach, the reasoner adopts that assumption when it views the target agent through a particular commonsense model (called IDEAS-AS-MODELS). (shrink)

Artificial intelligence (AI) is being tightly integrated into healthcare today. Even though AI is being utilized in healthcare, its application in clinical settings and health professions education is still controversial. The study described the perceptions of AI and its integration into health professions education and healthcare among health professions students. This descriptive phenomenological study analyzed the data from a purposive sample of 33 health professions students at a university in Kazakhstan using the thematic approach. Data collection was conducted from (...) March 30 to May 5, 2023, using an online questionnaire with four open-ended questions. Four significant themes describing the perceptions of the health professions students on AI and its integration into health profession education and healthcare was derived: enhanced interactive learning experiences, integrating artificial intelligence-powered diagnostic and decision-support tools into health professions education, AI technologies assist students in preparing for future roles in healthcare and integrating AI technologies presents several challenges. Health professions students perceived that integrating AI into health professions education and healthcare enhances their interactive learning experiences, increases knowledge and application of complicated medical topics, and prepares them for future healthcare roles. (shrink)

This is an introduction to the Symposium on Artificial Intelligence and Apocalypticism, which resulted from a conference hosted by the Centre for the Critical Study of Apocalyptic and Millenarian Movements (CenSAMM) in Bedford, UK. The introduction provides a brief history of scholarly work in the intersections of apocalypticism and artificial intelligence and of the emergence of CenSAMM from a millenarian religious community, the Panacea Society. It concludes by pointing toward the contributions of the symposium's essays.

Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the (...) classical problem of opacity in artificial neural networks (ANNs) explodes with the adoption of current Deep Learning techniques [LeCun, Bengio, Hinton, 2015]. In this paper we argue that the explanatory deficit of such techniques represents an important problem, that limits their adoption in the cognitive modelling and computational cognitive science arena. In particular we will show how the current attempts of providing explanations of the deep nets behaviour (see e.g. [Ritter et al. 2017] are not satisfactory. As a possibile way out to this problem, we present two different research strategies. The first strategy aims at dealing with the opacity problem by providing a more abstract interpretation of neural mechanisms and representations. This approach is adopted, for example, by the biologically inspired SPAUN architecture [Eliasmith et al., 2012] and by other proposals suggesting, for example, the interpretation of neural networks in terms of the Conceptual Spaces framework [Gärdenfors 2000, Lieto, Chella and Frixione, 2017]. All such proposals presuppose that the neural level of representation can be considered somehow irrelevant for attacking the problem of explanation [Lieto, Lebiere and Oltramari, 2017]. In our opinion, pursuing this research direction can still preserve the use of deep learning techniques in artificial cognitive models provided that novel and additional results in terms of “transparency” are obtained. The second strategy is somehow at odds with respect to the previous one and tries to address the explanatory issue by avoiding to directly solve the “opacity” problem. In this case, the idea is that one of resorting to pre-compiled plausible explanatory models of the word used in combination with deep-nets (see e.g. [Augello et al. 2017]). We argue that this research agenda, even if does not directly fits the explanatory needs of Computational Cognitive Science, can still be useful to provide results in the area of applied AI aiming at shedding light on the models of interaction between low level and high level tasks (e.g. between perceptual categorization and explanantion) in artificial systems. (shrink)

It may be of interest to see what can be done by giving a robot no innate knowledge about its environment or its sensors or effectors and only a totally general learning mechanism, such as reinforcement learning, or some information-reduction algorithm, to see what it can learn in various environments. However, it is clear that that is not how biological evolution designs animals, as McCarthy states.

The field of artificial intelligence and law is remarkably diverse not just because it encompasses many areas of academic study but also because it attracts the interest of both the research and commercial worlds. While much of the research is no doubt too exploratory and tentative to be of direct relevance to practising lawyers, in other projects there is but a short step from the research laboratory to the marketplace.Given that most readers of this journal tend to be involved (...) with, or interested in, research findings in the field, it might well be asked to what extent there should also be coverage here of commercial projects in artificial intelligence and law. (shrink)

While artificial intelligence’s (AI’s) potential role in enhancing diagnostic accuracy and personalising treatment is well-recognised, its application in evaluating physicians raises critical ethical concerns as well. The paper examines the impact of AI on the ‘comparative abilities’ exception to informed consent, which currently exempts physicians from disclosing information about the performance of other providers. With AI’s ability to generate granular, accurate comparisons of physician metrics, this exception will be challenged, potentially empowering patients to make more informed decisions. However, AI’s (...) use in disclosing physician success rates may exacerbate healthcare disparities, as wealthier patients may have more access to higher-skilled providers. Policymakers and ethicists must proactively address these concerns to ensure equitable access to care as AI technologies advance. (shrink)

Artificial Intelligence and Scientific Method examines the remarkable advances made in the field of AI over the past twenty years, discussing their profound implications for philosophy. Taking a clear, non-technical approach, Donald Gillies shows how current views on scientific method are challenged by this recent research, and suggests a new framework for the study of logic. Finally, he draws on work by such seminal thinkers as Bacon, Gdel, Popper, Penrose, and Lucas, to address the hotly-contested question of whether computers (...) might become intellectually superior to human beings. (shrink)

Artificial intelligence is not and has never been a technology. It began with Turing's famous "can machine think?", a philosophical question that too many were quick to transform into a more prosaic "can Thought be mechanized?" Only in this perspective can the history and the technological success of AI be duly explained and understood, one of the tasks this book engages in. -/- It is important for philosophers to take AI seriously, and for AI researchers to see their discipline (...) through the lenses of the adventure of the Spirit of man. The conclusion is that - paraphrasing Von Clausewitz's declaration about politics - "AI is a continuation of Philosophy by other means". Only after a proper meditaion and understanding of this fact can all the ethical, social and economical debates surrounding Artificial Intelligence begin to be discussed. (shrink)

Artificial Intelligence (AI) isset to revolutionise global knowledge domains and biblical hermeneutics is no exception. At face value, in Zimbabwe, AI has been stigmatised as a humanistic and profane technological system with an immense propensity to cause general religious backsliding, degeneracy, vain philosophising and secularisation of the Gospel of Christ. This article isolated Colossians 2:8 as a lens to investigate the congruency of Artificial Intelligence to the pericope’s scope of ‘philosophy, vain deceit, tradition of men and rudiments of (...) the world’. The Zimbabwean setting was used to examine whether the Colossian Christian Church’s philosophical aspersions bear any semblances to how AI is viewed among the clergy, theologians, Christian believers and academics of religious studies. The qualitative methodological paradigms of African Biblical Hermeneutics and Exegetical Method were employed in the study. The study mainly established that AI, like a tool in the hands of a workman, can either build or destroy, enhance or adulterate biblical hermeneutics depending on how it is viewed and used.Contribution: The article reflects on how the world’s most nascent technological development, Artificial Intelligence, impacts on biblical interpretation generally, but, more particularly, in the Zimbabwean context. (shrink)

Wagman examines the emulation of human cognition by artificial intelligence systems. The book provides detailed examples of artificial intelligence programs (such as the FERMI System and KEKADA program) accomplishing highly intellectual tasks.

The author sees his work as uniting the philosophy of mind and computer research. Each of these fields can benefit the other, philosophy of mind providing conceptual analyses and computers providing models for understanding human mental processes. A case in point providing the focus of this book is the problem of the mechanical simulation of the human ability to recognize handwritten script. Present difficulties in designing machines that can read human script point to a conceptual muddle in which classification and (...) recognition are confused. The author untangles the confusion by ordinary language analysis and then speculates as to how a more successful machine for recognizing the letters of human script might be designed. The language analysis is somewhat tedious, but the speculations about perceptual acts and mechanical letter recognition are intriguing.—P. M. (shrink)