Objective: Mindfulness-based intervention has been proposed to alleviate loneliness and improve social connectedness. Several randomized controlled trials have been conducted to evaluate the effectiveness of MBI. This study aimed to critically evaluate and determine the effectiveness and safety of MBI in alleviating the feeling of loneliness.Methods: We searched Medline, Embase, PsycInfo, Cochrane CENTRAL, and AMED for publications from inception to May 2020. We included RCTs with human subjects who were enrolled in MBI with loneliness as an outcome. The quality of (...) evidence was assessed using Cochrane's Risk of Bias tool and Grading of Recommendations Assessment, Development, and Evaluation. A random-effects model was used for meta-analysis.Results: Out of 92 articles identified, eight studies involving 815 participants were included in this study. Most trials conducted a minimum of 8 weeks of MBI. Most of the trials used UCLA-Loneliness Scale. A pooled analysis combining three trials and compared with wait-list showed significant improvement in loneliness score reduction using the UCLA-R scale with MD of −6.33 [95% confidence interval : −9.39, −3.26]. Subgroup analysis with only two Cognitively-Based Compassion Training trials also showed similar MD of −6.05. The overall quality of evidence was low.Conclusions: Mindfulness intervention with an average length of 8-week duration significantly improved the population's loneliness level with no mental health issue. However, this evidence had a low GRADE level. (shrink)

This Special Issue addresses a range of educational issues linked to main themes from our 2019 book The Emergence of Complexity: Rethinking Education as a Social Science. This book elaborated two major theses that raise fundamental questions for philosophy of education. First, that learning by groups is typically a distinctive kind of learning that is not reducible to learning by individuals. Second, that a degree of holism, as against a focus on individuals, is essential for achieving a (...) convincing understanding of this distinctive type of group learning. These two theses are of direct interest to philosophy of education since they challenges the resources of received theories of learning. Yet this kind of group learning characterises the vast bulk of human learning situations that occur outside of formal education systems. In this Special Issue introductory article we, firstly, introduce some basic principles of complexity theory, and demonstrate that, together with the concept of a ‘co-present group’ (between 2 and about 12 individuals), these ideas offer develop novel understandings of the distinctive learning that occurs within such groups. Secondly, we outline and illustrate with further examples the main features of co-present groups. Thirdly, we consolidate our conceptual work by showing five ways in which our account of group learning serves to refurbish the concept of ‘learning’. (shrink)

Philosophers have recently focused on critical, epistemological challenges that arise from the opacity of deep neural networks. One might conclude from this literature that doing good science with opaque models is exceptionally challenging, if not impossible. Yet, this is hard to square with the recent boom in optimism for AI in science alongside a flood of recent scientific breakthroughs driven by AI methods. In this paper, I argue that the disconnect between philosophical pessimism and scientific optimism is driven by a (...) failure to examine how AI is actually used in science. I show that, in order to understand the epistemic justification for AI-powered breakthroughs, philosophers must examine the role played by deep learning as part of a wider process of discovery. The philosophical distinction between the 'context of discovery' and the 'context of justification' is helpful in this regard. I demonstrate the importance of attending to this distinction with two cases drawn from the scientific literature, and show that epistemic opacity need not diminish AI's capacity to lead scientists to significant and justifiable breakthroughs. (shrink)

What can we learn from cases of knowledge from falsehood? Critics of knowledge-first epistemology have argued that these cases provide us with good reason for rejecting the knowledge accounts of evidence, justification, and the norm of belief. I shall offer a limited defense of the knowledge-first approach to these matters. Knowledge from falsehood cases should undermine our confidence in like-from-like reasoning in epistemology. Just as we should be open to the idea that knowledge can come from non-knowledge, we should be (...) open to the idea that justified beliefs can come from unjustified beliefs. (shrink)

Perception provides us with access to the external world, but that access is shaped by our own experiential histories. Through perceptual learning, we can enhance our capacities for perceptual discrimination, categorization, and attention to salient properties. We can also encode harmful biases and stereotypes. This article reviews interdisciplinary research on perceptual learning, with an emphasis on the implications for our rational and normative theorizing. Perceptual learning raises the possibility that our inquiries into topics such as epistemic justification, (...) aesthetic criticism, and moral knowledge should include not only an examination of cognition but also of perception. (shrink)

We propose a method of learning indicative conditional information. An agent learns conditional information by Jeffrey imaging on the minimally informative proposition expressed by a Stalnaker conditional. We show that the predictions of the proposed method align with the intuitions in Douven, 239–263 2012)’s benchmark examples. Jeffrey imaging on Stalnaker conditionals can also capture the learning of uncertain conditional information, which we illustrate by generating predictions for the Judy Benjamin Problem.

This paper examines the learning and performance of the air traffic control (ATC) work domain. This domain was chosen because it embodies features that represent future work for many other industries (e.g., information service provision mediated by information technologies; a high reliance on communication skills and collaborative work; increasing complexity and intensity of the work activity), within an organisational context undergoing considerable change. In ATC work learning occurs formally as part of accredited training and informally, as part of (...) everyday practice. In this way learning and experience of ATC work shapes – and is shaped by – the way work is organised, which includes the divisions of labour and technological artefacts. ATC work is experienced temporally, complexly, through affect and socially. Controllers learn to use a variety of socio-material resources which include embodiment and artefacts to undertake the work. In so doing the article demonstrates the ways in which learning and performance is not just a cognitive undertaking, but one that draws on embodiment to achieve what here is called “interdependent sentience”. This involves using all their senses to gain an awareness of problems as they emerge to collectively accomplish a seamless air traffic flow in a complex interdependent socio-technical system. (shrink)

Cognitive penetration of perception, broadly understood, is the influence that the cognitive system has on a perceptual system. The paper shows a form of cognitive penetration in the visual system which I call ‘architectural’. Architectural cognitive penetration is the process whereby the behaviour or the structure of the perceptual system is influenced by the cognitive system, which consequently may have an impact on the content of the perceptual experience. I scrutinize a study in perceptual learning that provides empirical evidence (...) that cognitive influences in the visual system produce neural reorganization in the primary visual cortex. The type of cognitive penetration can be synchronic and diachronic. (shrink)

Inoperative Learning draws upon the movement towards a weak philosophy that is currently gaining ground in educational philosophy: this weak philosophy does not offer a set of solutions or guidelines for improving educational outcomes, but rather renders assumptions about the theory-practice coupling that is so popular in contemporary education inoperative. By arguing that such logic reduces education to merely instrumental ends, which can only be assessed in terms of predefined measurement tools, this book presents a challenge to contemporary notions (...) of education as learning. There are various forms of inoperative education that temporarily suspend the goal-directedness of learning. From the perspective of learning, the neutralization of progress, growth, and maturity would usually be seen as obstacles needing to be overcome on the path toward set goals. Yet Lewis argues that a serious investigation of inoperativity opens up possibilities that would be otherwise unavailable in a world fixated on the question of learning. In dialogue with philosophers (Agamben, Benjamin, and Esposito), as well as authors (Kafka and Walser), and qualitative researchers (Lather), Lewis turns our collective attention to what remains when concepts such as learning, child development, teacher effectivity, and personal growth are left idle. Inoperative Learning presents a radical rewriting of educational possibilities. It should therefore be of great interest to educational researchers and educational philosophers concerned with the question of alternative logics of education beyond learning. The book may also be of interest to educational policy specialists who are looking for new and innovative forms of critique and analysis. (shrink)

A central point of contention in the ongoing debate between Humean and anti-Humean accounts of moral motivation concerns the theoretical credentials of the idea of mental states that are cognitive and motivational at the same time. Humeans claim that this idea is incoherent and thereby unintelligible (M. Smith, The Moral Problem, Blackwell 1994). I start by developing a linguistic argument against this claim. The semantics of certain ‘learning to’ and ‘knowing to’ ascriptions points to a dispositional state that has (...) both motivational and cognitive properties: habitual knowledge, as we may call it. But there is nothing unintelligible or incoherent about such ascriptions as they figure in the explanation and assessment of action. This suggests that the idea of a state that has both cognitive and motivational properties is not an artefact of philosophical speculation. Moreover, I suggest that action explanations that appeal to habitual knowledge, which are a variety of habit explanation, present distinctive problems for Humean accounts. The discussion bears on the relationship between habitual knowledge and knowing-how, and its possible significance for anti-Humean accounts of moral motivation. (shrink)

By classic results in social choice theory, any reasonable preferential voting method sometimes gives individuals an incentive to report an insincere preference. The extent to which different voting methods are more or less resistant to such strategic manipulation has become a key consideration for comparing voting methods. Here we measure resistance to manipulation by whether neural networks of varying sizes can learn to profitably manipulate a given voting method in expectation, given different types of limited information about how other voters (...) will vote. We trained over 70,000 neural networks of 26 sizes to manipulate against 8 different voting methods, under 6 types of limited information, in committee-sized elections with 5-21 voters and 3-6 candidates. We find that some voting methods, such as Borda, are highly manipulable by networks with limited information, while others, such as Instant Runoff, are not, despite being quite profitably manipulated by an ideal manipulator with full information. For the two probability models for elections that we use, the overall least manipulable of the 8 methods we study are Condorcet methods, namely Minimax and Split Cycle. (shrink)

본 연구의 목적은 최근 20여 년 동안 진행되어 온 학습이론에 대한 피아제의 명제논리학적 학습이론과 베이즈주의의 확률론적 학습이론의 융합에 근거하는 새로운 융합교수학습모형을 개발하는 것이다. 연구자는 이 새로운 교수학습모델을 “베이지안 구조구성주의 교수학습모형”(Bayesian structure-constructivist Model of Teaching-learning: 이하 약칭 BMT)이라 명명한다. 본고는 역사-비판적 관점 및 형식화적 관점에서 피아제의 명제논리학적 학습모형에서 해석된 학습이론과 베이즈주의의 확률론적 추론모형에서 해석된 학습이론을 일차적으로 분석하고, 논문의 후반부에서는 이를 근거로 교수법의 관점에서 양자의 학습이론을 통합하는 새로운 교수학습모델, 즉 BMT의 중요한 특성들을 세부적으로 제시한다. 몇 가지 핵심만 언급하면, 첫째로, BMT는 개념 (...) 학습이든 이론 학습이든 모든 학습은 표상과 샘플링의 상호작용임을 강조한다. 매우 이른 나이에서부터 아동은 학습하기 이전에 자기에게 주어진 예시들이 약한 샘플링인지 강한 샘플링인지, 특히 교육학적 샘플링인지 비교육학적 샘플링인지를 구분한다. 둘째로, 따라서 학습을 촉진시킬 수 있는 샘플을 선택하기 위해서 교사는 교육학적 추론을 위한 베이지안 모델을 활용하면서 직접적인 강의식 수업과 발견학습 수업을 병행해야 한다. 셋째로, 아동은 때때로 구체적인 규칙들보다는 추상적인 규칙들을 먼저 학습하기도 하지만, 대부분의 영역에서 추상적인 지식의 학습은 구체적인 지식의 학습에 의존한다. 따라서 성공적인 학습을 위해서는 피아제가 강조한 보편적 언어로서 논리적 문법과 그들 사이의 위계성을 고려하는 샘플을 개발하고 제시해야 한다. (shrink)

The paper adopts an inter-theoretical socio-cultural and -material perspective on the relationship between human + machine learning to propose a new way to investigate the human + machine assistive assemblages emerging in professional work (e.g. medicine, architecture, design and engineering). Its starting point is Hutchins’s (1995a) concept of ‘distributed cognition’ and his argument that his concept of ‘cultural ecosystems’ constitutes a unit of analysis to investigate collective human + machine working and learning (Hutchins, Philos Psychol 27:39–49, 2013). It (...) argues that: (i) the former offers a way to reveal the cultural constitution of and enactment of human + machine cognition and, in the process, the limitations of the computational and connectionist assumptions about learning that underpin, respectively, good old-fashioned AI and deep learning; and (2) the latter offers a way to identify, when amplified with insights from Socio-Materialism and Cultural-Historical Activity Theory, how ML is further rearranging and reorganising the distributed basis of cognition in assistive assemblages. The paper concludes by outlining a set of conjectures researchers that could use to guide their investigations into the ongoing design and deployment of HL + ML assemblages and challenges associated with the interaction between HL + ML. (shrink)

Learning Love from a Tiger explores the vibrancy and variety of humans' sacred encounters with the natural world, gathering a range of stories culled from Christian, Muslim, Hindu, Mayan, Himalayan, Buddhist, and Chinese shamanic traditions. Readers will delight in tales of house cats who teach monks how to meditate, rivers that grant salvation, shamans who shape-shift into jaguars, crickets who perform Catholic mass, and many others. More than a collection of wonderful stories, this book introduces important concepts and approaches (...) that underlie much recent work in environmental ethics, religion, and ecology. Capper's light touch prompts readers to engage their own views of humanity's place in the natural world and in particular question longstanding assumptions of human superiority"--Provided by publisher. (shrink)

This chapter contains sections titled: Introduction R. S. Peters and Analytic Philosophy of Education Revisiting First‐Order Ordinary Language‐Use Conclusion Notes References.

I develop an account of how mathematized theories in physics represent physical systems, in response to the frequent claim that any such account must presuppose a non-mathematized, and usually linguistic, description of the system represented. The account I develop contains a circularity, in that representation is a mathematical relation between the models of a theory and the system as represented by some other model --- but I argue that this circularity is not vicious, in any case refers in linguistic accounts (...) of meaning and representation, and is simply a consequence of the fact that we have no unmediated, representation-independent access to the world. (shrink)

The work of creating the future is being done now ─ and much of it is unsustainable in terms of natural and cultural resources. How will the next generation of leadership for environmental sustainability be raised up? Can we imagine sustainable futures, and can we enable transformative leadership to help us realize them? How can we best ensure that the several generations share their particular knowledge? What are the ethical frameworks, methodologies, curricula, and tools necessary for advancing and strengthening education (...) for intergenerational sustainability learning and leadership? In this book, 82 authors from 26 countries across 6 continents seek answers in 32 essays to the many questions related to the intergenerational collaboration that holds promise for creating sustainable futures. The authors themselves represent a diversity of geography, gender, and generation ─ and include the institutions comprising the emerging International Intergenerational Net-work of Centers. They speak to key principles, perspectives, and praxes at the intersection of intergenerational learning and transformative leadership in the context of education for sustainability. A visionary tour de force, this book explores the challenges and complexities of future learning models beyond the UN Decade of Education for Sustainable Development. It provides a timely discourse encompassing intergenerational and cultural dimensions, including ethics. Contributors articulate a deeper understanding of leadership in the post-Enlightenment era. Chapters in the book offer examples cutting across a diverse range of experiences worldwide, making this volume not only refreshing for practitioners, but also invaluable to policy-makers. ─ Dzulkifli Abdul Razak President, International Association of Universities Former Vice-Chancellor, Universiti Sains Malaysia These are critical times for the global community. Big issues are on the table: security, climate change, environmental destabilization, disease, hunger, and others. Actions we take today must embrace lessons from the past, present realities, and the rights and interests of future generations. Indeed, intergenerational learning is vital to education for sustainable development. This book makes a very timely and urgent call, challenging our traditional learning approaches and inviting us to dig deeper, stretch our minds wider, and see farther. ─ Akpezi Ogbuigwe Former Head of Environmental Education and Training, United Nations Environment Programme Executive Chairperson, Anpez Center for Environment and Development, Port Harcourt, Nigeria Achieving sustainable development requires generations, young and old, to engage with and learn from each other, and for leadership to embrace change. That is one of the key messages of this remarkable book. I wish I had this book when I took up the leadership role of my university two decades ago. I wish I could have given this book to my sons as they started their university education. ─ Goolam Mohamedbhai Former Secretary-General, Association of African Universities Honorary President, International Association of Universities. (shrink)

We are living in an era of development and innovation through research and learning. The nation that has achieved its development goals, has done through education reforms and a keen focus on strengthening its National Innovation System. In Pakistan, this role has fallen to the Higher Education Commission, commonly known as HEC. The Higher Education Commission has attempted various activities for ceaseless improvement of the nature of advanced education as per the worldwide norms and to patch up post-auxiliary instruction (...) to improve the instructive guidelines at the alumni level. Public Academy for Higher Education has been built up with the mean to plan and convey nonexclusive and need-based limit building and preparing programs in instructing and exploration for personnel just as a spotlight on scholastic administration and authority for the workforce and the board. Education Testing Council has additionally been modified to normalize passage tests for the universities of Pakistan. The study focuses on the different programs run by HEC to develop the country’s formal learning program and education sector. Since its commencement, we investigate various projects created by HEC to encourage Higher Education Institutions and build up a superior staff for this reason. (shrink)

I propose that metaphysical study is initially indifferent to the truth of Metaphysical Realism about Metaphysics (MRM) and Metaphysical Realism and does not presuppose them. Metaphysical Realism is a metaphysical doctrine the truth of which cannot be settled logically prior to metaphysical investigation. MRM presupposes Metaphysical Realism and therefore, one should not hold MRM uncritically. An epistemological consequence of this is that arguments against the possibility of cognition about metaphysically real entities (by e.g., Hume) are not arguments against the epistemic (...) legitimacy of metaphysics without further arguments for MRM. This paper is also a case study of the philosophical significance of having rich enough acquaintance with the history of philosophy, the recent history included. (shrink)

In this new volume in the Oxford Psychology Series, the author presents a highly readable account of the cognitive unconscious, focusing in particular on the problem of implicit learning. Implicit learning is defined as the acquisition of knowledge that takes place independently of the conscious attempts to learn and largely in the absence of explicit knowledge about what was acquired. One of the core assumptions of this argument is that implicit learning is a fundamental, "root" process, one (...) that lies at the very heart of the adaptive behavioral repertoire of every complex organism. The author's goals are to outline the essential features of implicit learning that have emerged from the many studies that have been carried out in a variety of experimental laboratories over the past several decades; to present the various alternative perspectives on this issue that have been proposed by other researchers and to try to accommodate these views with his own; to structure the literature so that it can be seen in the context of standard heuristics of evolutionary biology; to present the material within a functionalist approach and to try to show why the experimental data should be seen as entailing particular epistemological perspectives; and to present implicit processing as encompassing a general and ubiquitous set of operations that have wide currency and several possible applications. Chapter 1 begins with the core problem under consideration in this book, a characterization of "implicit learning" as it has come to be used in the literature. Reber puts this seemingly specialized topic into a general framework and suggests a theoretical model based on standard heuristics of evolutionary biology. In his account, Reber weaves a capsule history of interest in and work on the cognitive unconscious. Chapter 2 turns to a detailed overview of the experimental work on the acquisition of implicit knowledge, which currently is of great interest. Chapter 3 develops the evolutionary model within which one can see learning and cognition as richly intertwining issues and not as two distinct fields with one dominating the other. Finally, Chapter 4 explores a variety of entailments and speculations concerning implicit cognitive processes and their general role in the larger scope of human performance. (shrink)

Testimony is an invaluable source of knowledge. We rely on the reports of those around us for everything from the ingredients in our food and medicine to the identity of our family members. Recent years have seen an explosion of interest in the epistemology of testimony. Despite the multitude of views offered, a single thesis is nearly universally accepted: testimonial knowledge is acquired through the process of transmission from speaker to hearer. In this book, Jennifer Lackey shows that this thesis (...) is false and, hence, that the literature on testimony has been shaped at its core by a view that is fundamentally misguided. She then defends a detailed alternative to this conception of testimony: whereas the views currently dominant focus on the epistemic status of what speakers believe, Lackey advances a theory that instead centers on what speakers say. The upshot is that, strictly speaking, we do not learn from one another's beliefs - we learn from one another's words. Once this shift in focus is in place, Lackey goes on to argue that, though positive reasons are necessary for testimonial knowledge, testimony itself is an irreducible epistemic source. This leads to the development of a theory that gives proper credence to testimony's epistemologically dual nature: both the speaker and the hearer must make a positive epistemic contribution to testimonial knowledge. The resulting view not only reveals that testimony has the capacity to generate knowledge, but it also gives appropriate weight to our nature as both socially indebted and individually rational creatures. The approach found in this book will, then, represent a radical departure from the views currently dominating the epistemology of testimony, and thus is intended to reshape our understanding of the deep and ubiquitous reliance we have on the testimony of those around us. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning---as far as they are concerned with reliability.

This chapter contains sections titled: Education as Transforming Ways of Being Our Ambiguous Relation to Our World Ambiguity of Becoming Reconfiguring Professional Education as a Process of Becoming Conclusion Acknowledgements Notes References.

This chapter contains sections titled: Two‐Wheeled Sensations The “Bicycling Method” Lessons from the Saddle Finding the Words Notes.

We live in turbulent times, our world is changing at accelerating speed. Information is everywhere, but wisdom appears in short supply when trying to address key inter-related challenges of our time such as; runaway climate change, the loss of biodiversity, the depletion of natural resources, the on-going homogenization of culture, and rising inequity. Living in such times has implications for education and learning. This book explores the possibilities of designing and facilitating learning-based change and transitions towards sustainability. In (...) 31 chapters contributors from across the world discuss (re)emerging forms of learning that not only assist in breaking down unsustainable routines, forms of governance, production and consumption, but also can help create ones that are more sustainable. The book has been divided into three parts: re-orienting science and society, re-connecting people and planet and re-imagining education and learning. This is essential reading for educators, educational designers, change agents, researchers, students, policymakers and entrepreneurs alike, who are concerned about the well-being of the planet and convinced of our ability to do better. The content and related issues can be discussed on the blog by editor Arjen Wals: Transformative learning. 'We are living in times of incertitude, complexity, and contestation, but also of connectivity, responsibility, and new opportunities. This book analyses the consequences of these times for learning in formal, non-formal, and informal education. It explores the possibilities offered by the concept of sustainability as a central category of a holistic paradigm which harmonizes human beings with Earth. To change people and to change the world are interdependent processes - this book contributes to both.' Moacir Gadotti, Director of Paulo Freire Institute, São Paulo, Brazil 'I hope you share my excitement about the innovations for sustainability that this book catalogues and analyses. While the ecological news is grim, the human news is not. Even in a time of accelerating change, people are showing their enormous capacities to learn, adapt, restore and protect.' From the Foreword by Juliet Schor, author of ‘True Wealth: how and why millions of Americans are creating a time-rich, ecologically-light, small-scale high-satisfaction economy’ 'This book implies a ‘culture of critical commitment’ in educational thinking and practice - engaged enough to make a real difference to social-ecological resilience and sustainability but reflexively critical enough to learn constantly from experience and to keep options open in working for a sustainability transformation.' From the Afterword by Stephen Sterling, Professor of Sustainability Education, Centre for Sustainable Futures, Plymouth University, United Kingdom. (shrink)

"This comprehensive volume - containing 27 chapters and contributions from six continents - presents and discusses key principles, perspectives, and practices of social learning in the context of sustainability. Social learning is explored from a range of fields challenged by sustainability including: organizational learning, environmental management and corporate social responsibility; multi-stakeholder governance; education, learning and educational psychology; multiple land-use and integrated rural development; and consumerism and critical consumer education. An entire section of the book is devoted (...) to a number of reflective case studies of people, organizations and communities using forms of social learning in moving towards sustainability. 'This book brings together a range of ideas, stories, and discussions about purposeful learning in communities aimed at creating a world that is more sustainable than the one currently in prospect....The book is designed to expand the network of conversations through which our society can confront various perspectives, discover emerging patterns, and apply learning to a variety of emotional and social contexts.' From the Foreword by Fritjof Capra, co-founder of the Center of Ecoliteracy. 'Joining what is so clear and refreshing in this book with the larger movements toward a critically democratic and activist education that is worthy of its name, is but one step in the struggle for sustainability. But it is an essential step if we are to use the insights that are included in this book.' From the Afterword by Michael Apple, author of 'Educating the ""Right"" Way: Markets, Standards, God, and Inequality'.". (shrink)

In a spirit of collegial support, this paper argues that Beckett and Hager’s theoretical justification and empirical exemplifications do not do full justice to the complexity of group or team learning. We firstly reaffirm our support for the theoretical argument Becket and Hager make, though expressing some reservations about Complexity Theory, to explain the taken-for-granted assumptions that learning by an individual is the paradigm case of learning and that context plays a minimal role in this process. Drawing (...) on our joint and separate work, we demonstrate that Becket and Hager’s argument is less radical than it may initially seem because it is predicated on: (i) cognitive-bounded rather than “distributed” or “extended” conception of mind; (ii) material rather than a “immaterial” conception of activity; and (iii) co-present rather than a “fractional” or “connective” conception of ontology. Despite making this critique, we conclude by making the case that we are adding further substance to Becket and Hager’s overarching argument and, in doing so, encouraging them to be more radical about how they conceptualise the complexity of learning. (shrink)

Artificial intelligence research connected with learning theory ("deep learning," “machine learning,” analysis of the quality of learning, etc.) has existed for many years; however, there have been few investigations in that area conducted from a robust philosophical methodological basis.

In this paper we analyse the conditions for attributing to AI autonomous systems the ontological status of “artificial moral agents”, in the context of the “distributed responsibility” between humans and machines in Machine Ethics (ME). In order to address the fundamental issue in ME of the unavoidable “opacity” of their decisions with ethical/legal relevance, we start from the neuroethical evidence in cognitive science. In humans, the “transparency” and then the “ethical accountability” of their actions as responsible moral agents is not (...) in contradiction with the unavoidable “opacity” (unawareness) of the brain process by which they perform their moral judgements on the right action to execute. In fact, the moral accountability of our actions depends on what is immediately before and after our “moral judgements” on the right action to execute (formally, deontic first order logic (FOL) decisions). I.e., our moral accountability depends on the “ethical constraints” we imposed to our judgement before performing it in an opaque way. Anyway, our moral accountability depends overall on the “ethical assessment” or explicit “moral reasoning” after and over the moral judgement before executing our actions (deontic higher order logic (HOL) assessment). In this way, in the light of the AI “imitation game”, the consistent attribution of the status of ethically accountable artificial moral agents to autonomous AI systems depends on two similar conditions. Firstly, it depends on the presence of “ethical constraints” to be satisfied in their Machine Learning (ML) supervised optimization algorithm during its training phase, to give the system ethical skills (“competences”) in its decisions. Secondly – and definitely—, it depends on the presence in an AI autonomous system of a deontic HOL “ethical reasoner” to perform an automatic, and fully transparent assessment (metalogical deontic valuation) about the decisions taken by the ethically skilled ML algorithm about the right action to execute, before executing it. Finally, we show that the proper deontic FOL and HOL for this class of artificial moral agents is Kripke’s modal relational logic, in its algebraic topological formalization. This is naturally implemented in the dissipative QFT unsupervised deep learning of our brains, based on the “doubling of the degrees of freedom” (DDF), and then in the so-called “deep-belief” artificial neural networks for the statistical data pre-processing. This unsupervised learning procedure is also compliant with the usage of the “maximin fairness principle”, used as a balancing aggregation principle of the statistical variables in Sen’s formal theory of fairness. (shrink)

I examine the phenomenon of implicit learning, the process by which knowledge about the rule-governed complexities of the stimulus environment is acquired independently of conscious attempts to do so. Our research with the two seemingly disparate experimental paradigms of synthetic grammar learning and probability learning, is reviewed and integrated with other approaches to the general problem of unconscious cognition. The conclusions reached are as follows: Implicit learning produces a tacit knowledge base that is abstract and representative (...) of the structure of the environment; such knowledge is optimally acquired independently of conscious efforts to learn; and it can be used implicitly to solve problems and make accurate decisions about novel stimulus circumstances. Various epistemological issues and related problems such as intuition, neuroclinical disorders of learning and memory, and the relationship of evolutionary processes to cognitive science are also discussed. (shrink)

Industry groups engage in venue shifting when they seek to overturn or alter restrictive regulations imposed by one political venue through another. A critical step in this process is resolving uncertainties surrounding the preference of the targeted venue and the nature of the relevant policy proposal. While existing studies emphasize a long-term trial-and-error process of policy learning, we focus on nascent industries and argue that ventures seek other information sources to resolve these uncertainties quickly. In particular, nascent industry groups (...) are likely to perceive that the targeted venue will support their policy proposal if the targeted venue is ideologically distant from the venue that has enacted the restrictive regulations, if the targeted venue has recently supported other nascent industry groups’ similar policy proposals, or if the industry groups themselves are more exposed to industry peers’ success in promoting the same policy proposal in other jurisdictions. Under these conditions, the industry groups invest more to influence the targeted venue in response to restrictive regulations enacted by other venues. We find support for our theory by examining how from 2013 to 2019 the small unmanned aircraft systems industry trade associations in the United States lobbied state governments to nullify local regulations. (shrink)

In this paper we address the problem of short-term electric energy prediction using a time series forecasting approach applied to data generated by a Paraguayan electricity distribution provider. The dataset used in this work contains data collected over a three-year period. This is the first time that these data have been used; therefore, a preprocessing phase of the data was also performed. In particular, we propose a comparative study of various machine learning and statistical strategies with the objective of (...) predicting the electric energy consumption for a given prediction horizon, in our case seven days, using historical data. In this paper we have tested the effectiveness of the techniques with different historical window sizes. Specifically, we considered two ensemble strategies, a neural network, a deep learning technique and linear regression. Moreover, in this study, we tested whether the inclusion of meteorological data can help achieve better predictions. In particular, we considered data regarding temperature, humidity, wind speed and atmospheric pressure registered during the three-year period of data collection. The results show that, in general, the deep learning approach obtains the best results and that such results are obtained when meteorological data are also considered. Moreover, when meteorological data is used, a smaller historical window size is required to obtain precise predictions. (shrink)

This target article presents a theory of human cultural learning. Cultural learning is identified with those instances of social learning in which intersubjectivity or perspective-taking plays a vital role, both in the original learning process and in the resulting cognitive product. Cultural learning manifests itself in three forms during human ontogeny: imitative learning, instructed learning, and collaborative learning – in that order. Evidence is provided that this progression arises from the developmental ordering (...) of the underlying social-cognitive concepts and processes involved. Imitative learning relies on a concept of intentional agent and involves simple perspective-taking. Instructed learning relies on a concept of mental agent and involves alternating/coordinated perspective-taking (intersubjectivity). Collaborative learning relies on a concept of reflective agent and involves integrated perspective-taking (reflective intersubjectivity). A comparison of normal children, autistic children and wild and enculturated chimpanzees provides further evidence for these correlations between social cognition and cultural learning. Cultural learning is a uniquely human form of social learning that allows for a fidelity of transmission of behaviors and information among conspecifics not possible in other forms of social learning, thereby providing the psychological basis for cultural evolution. (shrink)

John McDowell begins his essay ‘Knowledge by Hearsay’ (1993) by describing two ways language matters to epistemology. The first is that, by understanding and accepting someone else's utterance, a person can acquire knowledge. This is what philosophers call ‘knowledge by testimony’. The second is that children acquire knowledge in the course of learning their first language—in acquiring language, a child inherits a conception of the world. In The Formation of Reason (2011), and my writings on Russian socio‐historical philosophy and (...) psychology, I address issues bearing on the second of these topics, questions about the child's development through initiation into language and other forms of social being. In this chapter, I focus on the first: the epistemology of testimony. After expounding a view of testimony inspired by McDowell, and supplemented by ideas from Sebastian Rödl, I consider how such an account illuminates two issues in philosophy of education: the extent of an individual's epistemic dependence upon others, and the nature of teaching. (shrink)

The idea that fictions may educate us is an old one, as is the view that they distort the truth and mislead us. While there is a long tradition of passionate assertion in this debate, systematic arguments are a recent development, and the idea of empirically testing is particularly novel. Our aim in this chapter is to provide clarity about what is at stake in this debate, what the options are, and how empirical work does or might bear on its (...) resolution. We distinguish between merely influencing people’s opinions and providing genuine learning, where the latter requires that the source of the change in opinion should be reliable. Other important ideas here are the extent to which authors of fiction may be considered to provide testimony, or something like it, and the possibility of recognizing an unstated purpose in the project of the fictional work. We ask whether fictions can furnish us not merely with ideas but with reasons for believing them, perhaps by constituting thought experiments. We consider whether the focus should be on understanding rather than on knowledge, and whether fictions can inform us about the qualities of another’s experiences. We briefly describe some experimental work of recent decades and suggest that the evidence for learning from fiction is currently meagre. (shrink)

In this groundbreaking book, Andrea R. English challenges common assumptions by arguing that discontinuous experiences, such as uncertainty and struggle, are essential to the learning process. To make this argument, Dr. English draws from the works of two seminal thinkers in philosophy of education - nineteenth-century German philosopher J. F. Herbart and American Pragmatist John Dewey. English's analysis considers Herbart's influence on Dewey, inverting the accepted interpretation of Dewey's thought as a dramatic break from modern European understandings of education.

In this text Hasse presents a new, inclusive, posthuman learning theory, designed to keep up with the transformations of human learning resulting from new technological experiences, as well as considering the expanding role of cyborg devices and robots in learning. This ground-breaking book draws on research from across psychology, education, and anthropology to present a truly interdisciplinary examination of the relationship between technology, learning and humanity. Posthumanism questions the self-evident status of human beings by exploring how (...) technology is changing what can be categorised as "human". In this book, the author applies a posthumanist lens to traditional learning theory, challenging conventional understanding of what a human learner is, and considering how technological advances are changing how we think about this question. Throughout the book Hasse uses vignettes of her own research and that of other prominent academics to exemplify what technology can tell us about how we learn and how this can be observed in real-life settings. Posthumanist Learning is essential reading for students and researchers of posthumanism and learning theory from a variety of backgrounds, including psychology, education, anthropology, robotics and philosophy. (shrink)

Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of (...) synthetic audiovisual media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Buber's two ways of learning -- Buber's method of inclusion -- Teaching as unteaching -- The broadest frame: dialogue as meta-methodology -- Dialogues with texts -- Dialogues with students -- Interview dialogues -- Journal dialogues.

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories (...) of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. (shrink)

I (mostly) accept Ancell’s argument that my proposal for dealing with the radical behavioral challenge entails what he calls ‘the excessive recusal problem’. I argue that this is no reason to reject my proposal, but rather an opportunity for further reflection on what behavioral and normative ethicists can learn from each other. I make some suggestions for future lines of inquiry for both fields.

Alasdair MacIntyre is one of the major philosophers of the late twentieth and early twenty-first century. After Virtue, first published in 1981, remains the book for which he is best known but, as this volume testifies, his phenomenal output extends over a period of seven decades. Not only is his output extensive, but its impact, unusually for philosophers, has been wide-ranging. As MacIntyre enters his tenth decade, this book pays tribute not just to his work, but to the way in (...) which it has been influential across disciplines outside of philosophy. Beginning with an intellectual biography, the chapters which follow, written by experts in their fields, explore MacIntyre's contributions to theology, Thomism, moral philosophy, classical philosophy, political philosophy, Marxism, the Frankfurt School, communication, business ethics, sociology, education, law, and therapeutic method. Essential reading for scholars from across these disciplines, and for anyone who wishes to understand MacIntyre's contributions, this volume not only helps readers to appreciate what we may learn from MacIntyre, but also indicates how his work will continue to be influential"-- Back cover. (shrink)

Deep learning is currently the most prominent and widely successful method in artificial intelligence. Despite having played an active role in earlier artificial intelligence and neural network research, philosophers have been largely silent on this technology so far. This is remarkable, given that deep learning neural networks have blown past predicted upper limits on artificial intelligence performance—recognizing complex objects in natural photographs and defeating world champions in strategy games as complex as Go and chess—yet there remains no universally (...) accepted explanation as to why they work so well. This article provides an introduction to these networks as well as an opinionated guidebook on the philosophical significance of their structure and achievements. It argues that deep learning neural networks differ importantly in their structure and mathematical properties from the shallower neural networks that were the subject of so much philosophical reflection in the 1980s and 1990s. The article then explores several different explanations for their success and ends by proposing three areas of inquiry that would benefit from future engagement by philosophers of mind and science. (shrink)