The metaepistemology of knowing-how

Knowing-how is currently a hot topic in epistemology. But what is the proper subject matter of a study of knowing-how and in what sense can such a study be regarded as epistemological? The aim of this paper is to answer such metaepistemological questions. This paper offers a metaepistemology of knowing-how, including considerations of the subject matter, task, and nature of the epistemology of knowing-how. I will achieve this aim, first, by distinguishing varieties of knowing-how and, second, by introducing and elaborating the concept of hybrid knowing-how, which entails a combination of a ground-level ability and a meta-level perspective on that ability. The stance I wish to advocate is that the epistemology of knowing-how is a normative discipline whose main task is to study the nature and value of human practical intelligence required to do things in a particular manner.     

Episodes of experience and generative intelligence

How do humans, including toddlers, take knowledge from past experiences and apply this knowledge in new ways? Current approaches to human and artificial intelligence (AI) fail to offer satisfactory explanations. We suggest the explanation will be found in the coherence statistics of the individual time-extended episodes of human experience and the cognitive processes those statistics engage.     

Evolution and learning: An epistemological perspective

The deep formal and conceptual link existing between artificial life and artificial intelligence can be highlighted using conceptual tools derived by Karl Popper's evolutionary epistemology. Starting from the observation that the structure itself of an organism embodies knowledge about the environment which it is adapted to, it is possible to regard evolution as a learning process. This process is subject to the same rules indicated by Popper for the growth of scientific knowledge: causal conjectures (mutations) and successive refutations (extinction). In the field of machine learning such a paradigm is represented by genetic algorithms that, simulating biological processes, emulate cognitive processes. From a practical viewpoint, that perspective allows to identify the two different kinds of learning considered by artificial intelligence, knowledge acquisition and skill improvement, and to get a different view of the problem of heuristic knowledge in learning systems. From a theoretical point of view, these considerations can shade a new light on an old epistemological problem: why do we live in a learnable world?     

THE ARTIFICIALIZATION OF MIND AND WORLD

The rapid advancement of artificial intelligence (AI) has led to renewed ambitions of developing artificial general intelligence. Alongside this has been a resurgence in the development of virtual and augmented reality (V/AR) technologies, which are viewed as “disruptive” technologies and the computing platforms of the future. V/AR effectively bring the digital world of machines, robots, and artificial agents to our senses while entailing the transposition of human activity and presence into the digital world of artificial agents and machine forms of intelligence. The intersection of humans and machines in this shared space brings humans and machines into ontological continuity as informational entities in a totalizing informational environment, which subsumes both cyber and physical space in an artificially constructed virtual world. The reconstruction of mind (through AI) and world (through V/AR) thus has significant epistemological, ontological, and anthropological implications, which constitute the underlying features in the artificialization of mind and world.     

Against the iDoctor: why artificial intelligence should not replace physician judgment

Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence (AI) research advances, such technologies raise the possibility of an “iDoctor,” a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger’s critique of technology to show how an algorithmic approach to medicine distorts the physician–patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient’s needs. In response to the objection that AI could develop this capacity, I use Hubert Dreyfus’s analysis of AI to argue that attention to the needs of each patient requires the physician to attune his or her perception to the patient’s history and physical exam, an ability that seems uniquely human. Human physician judgment will remain better suited to the practice of primary care despite anticipated advances in AI technology.     

What Can Artificial Intelligence Learn from Wittgenstein＇s On Certainty？.

Meta-philosophically speaking, the philosophy of artificial intelligence （AI） is intended not only to explore the theoretical possibility of building ＂thinking machines,＂ but also to reveal philosophical implications of specific AI approaches. Wittgenstein＇s comments on the analytic/empirical dichotomy may offer inspirations for AI in the second sense. According to his ＂river metaphor＂ in On Certainty, the analytic/empirical boundary should be delimited in a way sensitive to specific contexts of practical reasoning. His proposal seems to suggest that any cognitive modeling project needs to render the system context-sensitive by avoiding representing large amounts of truisms in its cognitive processes, otherwise neither representational compactness nor computational efficiency can be achieved. In this article, different AI approaches （like the Common Sense Law of Inertia approach, the Bayesian approach and the connectionist approach） will be critically evaluated under the afore-mentioned Wittgensteinian criteria, followed by the author＇s own constructive suggestion on what AI needs to try to do in the near future.     

What is the future for tool-specific generalized motor programs?

A key issue in cognitive sciences is to understand the cognitive bases of human tool use. Answers have been provided by two competing approaches. The manipulation-based approach assumes that humans can use tools because of the ability to store sensorimotor knowledge about how to manipulate tools. By contrast, for the reasoning-based approach, human tool use is based on the ability to reason about physical object properties. Recently, Caruana and Cuccio proposed a kind of reconciliation, based on the distinction between three types of abductive inference, involving a different contribution of motor and cognitive elements: Automatic abduction (motor + and cognitive-), abduction by selection (motor ± and cognitive±) and creative abduction (motor- and cognitive+). This perspective offers new interesting avenues. Nevertheless, it is also subject to several theoretical and epistemological limitations, which make it in its present form inappropriate for the study of the cognitive bases of human tool use. This article aims to discuss these limitations.     

Artificial Intelligence &; Games: Should Computational Psychology be Revalued?

The aims of this paper are threefold: To show that game-playing (GP), the discipline of Artificial Intelligence (AI) concerned with the development of automated game players, has a strong epistemological relevance within both AI and the vast area of cognitive sciences. In this context games can be seen as a way of securely reducing (segmenting) real-world complexity, thus creating the laboratory environment necessary for testing the diverse types and facets of intelligence produced by computer models. This paper aims to promote the belief that games represent an excellent tool for the project of computational psychology (CP).  To underline how, despite this, GP has mainly adopted an engineering-inspired methodology and in doing so has distorted the framework of cognitive functionalism. Many successes (i.e. chess, checkers) have been achieved refusing human-like reasoning. The AI has appeared to work well despite ignoring an intrinsic motivation, that of creating an explanatory link between machines and mind.  To assert that substantial improvements in GP may be obtained in the future only by renewed interest in human-inspired models of reasoning and in other cognitive studies. In fact, if we increase the complexity of games (from NP-Completeness to AI-Completeness) in order to reproduce real-life problems, computer science techniques enter an impasse. Many of AI’s recent GP experiences can be shown to validate this.  The lack of consistent philosophical foundations for cognitive AI and the minimal philosophical commitment of AI investigation are two of the major reasons that play an important role in explaining why CP has been overlooked.     

An epistemological problem for evolutionary psychology

This article draws out an epistemological tension implicit in Cosmides and Tooby's conception of evolutionary psychology. Cosmides and Tooby think of the mind as a collection of functionally individuated, domain‐specific modules. Although they do not explicitly deny the existence of domain‐general processes, it will be shown that their methodology commits them to the assumption that only domain‐specific cognitive processes are capable of producing useful outputs. The resultant view limits the scope of biologically possible cognitive accomplishments and these limitations, it will be argued, are such as to deny us epistemic capacities that evolutionary psychology presupposes in its pursuit of an objective, comprehensive account of human nature.     

(E)-Trust and Its Function: Why We Shouldn't Apply Trust and Trustworthiness to Human–AI Relations

With an increasing use of artificial intelligence (AI) systems, theorists have analyzed and argued for the promotion of trust in AI and trustworthy AI. Critics have objected that AI does not have the characteristics to be an appropriate subject for trust. However, this argumentation is open to counterarguments. Firstly, rejecting trust in AI denies the trust attitudes that some people experience. Secondly, we can trust other non-human entities, such as animals and institutions, so why can we not trust AI systems? Finally, human–AI trust is criticized based on a conception of human–human trust, which does not recognize the distinctiveness of the human–AI relationship. This article aims to refute these counterarguments based on the genealogical analyses of ‘trust’ and ‘trustworthiness’ of Karen Jones and Thomas Simpson, who show that trust and trustworthiness help to overcome vulnerabilities. This function of trust gives reason to use human–human trust as a standard. For this function, it is important that trustees are responsive to trust. While animals and institutions could be responsive, narrow AI systems are unable to be responsive to trust. Therefore, we should not apply trust to AI and instead direct our trust to those who can be responsive to and held responsible for our trust.     

AUGMENTED REALITY,ARTIFICIAL INTELLIGENCE,AND THE RE‐ENCHANTMENT OF THE WORLD

There has recently been a surge of development in augmented reality (AR) technologies that has led to an ecosystem of hardware and software for AR, including tools for artists and designers to accelerate the design of AR content and experiences without requiring complex programming. AR is viewed as a key “disruptive technology” and future display technologies (such as digital eyewear) will provide seamless continuity between reality and the digitally augmented. This article will argue that the technologization of human perception and experience of reality, coupled with the development of artificial intelligence (AI)–based natural language assistants, may lead to a secular re‐enchantment of the world, in the sense outlined by Charles Taylor, where human existence is shaped through AR inhabited by advanced personal and social AI agents in the form of digital avatars and daemons, and that enchantment has been persistent throughout the formation of modernity and is being rekindled by the integration of AI in the plane of AR.     

Does Wittgenstein Actually Undermine the Foundation of Artificial Intelligence?

Wittgenstein is widely viewed as a potential critic of a key philosophical assumption of the Strong Artificial Intelligence (AI) thesis,namely,that it is in principle possible to build a programmed machine which can achieve real intelligence.Stuart Shanker has provided the most systematic reconstruction of the Wittgensteinian argument against AI,building on Wittgenstein's own statements,the "rule-following" feature of language-games,and the putative alliance between AI and psychologism.This article will attempt to refute this reconstruction and its constituent arguments,thereby paving the way for a new and amicable rather than agonistic conception of the Wittgensteinian position on AI.     

Considerations Relating to the Study of Group Differences in Intelligence

ABSTRACT— There are signs that the debate over racial and gender differences in intelligence is about to begin again. In this article we will be concerned primarily with racial differences but will make remarks about gender differences where applicable. Previously there have been bitter arguments over whether or not races exist, over whether it is either important or proper to study racial and gender differences in intelligence, and over the conclusions that have been drawn about environmental and genetic causes as determinants of these differences. We argue that races do, indeed, exist and that studying differences in cognitive competence between groups is a reasonable thing to do. We also point out that past research on both racial and gender differences in intelligence has been marked by methodological errors and overgeneralizations by researchers on all sides of the issue. We propose ten principles of design, analysis, and reporting that ought to be considered carefully when doing or evaluating research in this area.     

Artificial Intelligence, Religion, and Community Concern

Future developments in artificial intelligence (AI) will likely allow for a greater degree of human-machine convergence, with machines becoming more humanlike and intelligent machinery becoming more integrated into human brain function. This will pose many ethical challenges, and the necessity for a moral framework for evaluating these challenges will grow. This paper argues that community concern constitutes a central factor in both the evolution of religion and the human brain, and as such it should be used as the organizing principle for moral evaluations of AI technologies.     

Epistemology of AI Revisited in the Light of the Philosophy of Information

Artificial intelligence has often been seen as an attempt to reduce the natural mind to informational processes and, consequently, to naturalize philosophy. The many criticisms that were addressed to the so-called “old-fashioned AI” do not concern this attempt itself, but the methods it used, especially the reduction of the mind to a symbolic level of abstraction, which has often appeared to be inadequate to capture the richness of our mental activity. As a consequence, there were many efforts to evacuate the semantical models in favor of elementary physiological mechanisms simulated by information processes. However, these views, and the subsequent criticisms against artificial intelligence that they contain, miss the very nature of artificial intelligence, which is not reducible to a “science of the nature”, but which directly impacts our culture. More precisely, they lead to evacuate the role of the semantic information. In other words, they tend to throw the baby out with the bath-water. This paper tries to revisit the epistemology of artificial intelligence in the light of the opposition between the “sciences of nature” and the “sciences of culture”, which has been introduced by German neo-Kantian philosophers. It then shows how this epistemological view opens on the many contemporary applications of artificial intelligence that have already transformed—and will continue to transform—all our cultural activities and our world. Lastly, it places those perspectives in the context of the philosophy of information and more particularly it emphasizes the role played by the notions of context and level of abstraction in artificial intelligence.     

To Think Human out of the Machine Paradigm: <Emphasis Type="Italic">Homo Ex Machina</Emphasis>

This article attempts to show that the metaphorical conception of human being as a machine takes a very specific epistemological standpoint. To make short the complex task of considering the implication of this paradigm for psychological and behavioral sciences, three important mismatches between the machine and the living human will be considered. Experience, agency and plasticity of human being are excluded in the scientific models and research activities when they are situated in the machine paradigm. For this reason, I claim that the machine paradigm does not offer the relevant frame for integrating results from various domains or approaches within human sciences, even if it can sometimes produce relevant scientific knowledge in certain domain at the scale of detailed investigation. Due to the importance of overcoming the fragmentation of scientific knowledge to solve the crisis in psychology, an “organic paradigm” should be elaborated which provides a new epistemological framework.     

Artificial Intelligence and the New Alienation of Human Beings

Artificial intelligence (AI) is a revolutionary and overwhelming technology that is yet to immature. While profoundly changing and shaping people and society, AI also splits into its own opposites and develops into a new external alien force. As the basic technical support of the entire society, intelligent technology entails the overt or covert domination of human beings, who are becoming the “vassals” and “slaves” of this high-speed intelligent social system. Various intelligent systems are constantly replacing human work, so that the “digital poor” gradually lose the opportunities and values offered by labor and hence are excluded by the global economic and social system, rendering their existence empty and absurd. The rapid development of intelligent robots has blurred the boundary between humans and machines and had a strong impact on the nature of man and his position as a conscious agent, making “What is man?” and the human-machine relationship prominent issues for our times, challenging the commonplaces of philosophy. We must face up to the existing or imminent risk of alienation, expand our theoretical horizons, innovate theories of alienation in the era of intelligence, take constructive action in terms of the construction of an ideal society and the evolution of man himself, build an ecological system for the joint evolution and growth of human beings and intelligent machines, and achieve liberty of man and the all-round and free development.     

THE EVOLUTION OF HUMAN BIRTH AND TRANSHUMANIST PROPOSALS OF ENHANCEMENT

Some transhumanists argue that we must engage with theories and facts about our evolutionary past in order to promote future enhancements of the human body. At the same time, they call our attention to the flawed character of evolution and argue that there is a mismatch between adaptation to ancestral environments and contemporary life. One important trait of our evolutionary past which should not be ignored, and yet may hinder the continued perfection of humankind, is the peculiarly human way of bearing and raising children. The suffering associated with childbirth and a long childhood have demanded trade‐offs that have enhanced our species, leading to cooperation, creativity, intelligence and resilience. Behaviors such as mother–infant engagement, empathy, storytelling, and ritual have also helped to create what we value most in human beings. Therefore, the moral, cognitive, and emotional enhancements proposed by these transhumanists may be impaired by their partial appropriation of evolution, insofar as the bittersweet experience of parenthood is left aside.     

Introduction to the special issue on cognitive epidemiology

This is an introduction to a special issue of the journal Intelligence on cognitive epidemiology. Cognitive epidemiology is a new field of study, which examines the associations between intelligence—usually from early in life—and later morbidity (physical and mental) and mortality. In addition to exploring and establishing associations, studies within cognitive epidemiology attempt to explain them, by testing possible confounders and mediators, and complex pathways, of intelligence–health associations. Popular among mediators are health behaviours and education, and the well-known risk factors for chronic illnesses such as cardiovascular disease. In this special issue, readers will find advances in all of these matters. Thirteen new empirical studies, all involving large cohorts of humans, provide novel associations between intelligence and mortality, morbidity, and health behaviours and risk factors. New hypotheses of these associations are tested. This is the largest collection of cognitive epidemiology studies to date. Together, they will take the field forward by a quantum jump. This is a feast of cognitive epidemiology, establishing that, beside education and occupation, health outcomes contribute to the impressive predictive validity of intelligence differences.     

基于情绪认知评价理论的虚拟人情绪模型研究

情感计算是人工智能的前沿领域之一,建立具有情绪表现力的人机智能系统具有广泛的社会需求。本文分析了基于情绪认知评价理论的情绪建模方法,总结了典型的虚拟人情绪计算模型,归纳了情绪和感知的相关成果,梳理了虚拟人情绪表现方法。针对情绪建模所要深入研究的问题,分析现有研究中存在的不足,对虚拟人情绪模型研究的未来发展方向提出了建议,为进一步深入研究虚拟人情绪模型提供了参考。