2,384 research outputs found
Unleashing the power of artificial intelligence for climate action in industrial markets
Artificial Intelligence (AI) is a game-changing capability in industrial markets that can accelerate humanity's race against climate change. Positioned in a resource-hungry and pollution-intensive industry, this study explores AI-powered climate service innovation capabilities and their overall effects. The study develops and validates an AI model, identifying three primary dimensions and nine subdimensions. Based on a dataset in the fast fashion industry, the findings show that the AI-powered climate service innovation capabilities significantly influence both environmental and market performance, in which environmental performance acts as a partial mediator. Specifically, the results identify the key elements of an AI-informed framework for climate action and show how this can be used to develop a range of mitigation, adaptation and resilience initiatives in response to climate change
The representational systems for object and agent in new world monkeys
Representing the environment in its most basic components, namely objects and agents, is a fundamental feature of human cognition which we may share to different extents with nonhuman animals. This thesis explored some manifestations of these abilities in two new world monkey species, squirrel monkeys and capuchin monkeys.
We first investigated squirrel monkeysâ ability of individuating object by spatiotemporal and property/kind information with a âmagic boxâ paradigm using both manual search and looking time measures (chapter 2). The squirrel monkeys failed both tasks with both measures, whereas capuchin monkeys showed individuating competence with exactly the same tasks and apparatus in a previous study.
Chapter 3 tested and explored the possibility that squirrel monkeys failed the âmagic boxâ tasks that capuchin monkeys passed because they acted so fast that they didnât form or use this type of object representations to guide their actions. In fact, in a touchscreen-based object tracking/catching game (Whack-a-cricket task), the squirrel monkeys were slower to âcatchâ
a moving âcricketâ compared to capuchin monkeys.
In chapter 4, we tested squirrel monkeys with another individuation task that included two separate barriers instead of a single box. The squirrel monkeys preferred to search the last- visited-location first when either spatiotemporal or property/kind information suggested that only one object was present. This preference disappeared when either information indicated that there were two objects, one behind each barrier. We conclude that squirrel monkeys are therefore able to individuate objects using both kinds of information when tested with an appropriate task.
In the last chapter, we investigated whether capuchin monkeys can locate a causal agent based on an event that he initiated from a hidden location. Capuchin monkeys located the hidden agent when they saw an object pushed, raked, rolled, or thrown across a table seemingly by the experimenter behind one of two screens (agentive trials), but not when they saw an object roll down a ramp or fall off a block after a shake of the table (arbitrary control trials) that contained no information about the agentâ location. This result suggests capuchin monkeys can use motion events to infer the location of a causal agent, an ability also demonstrated by human infants.
Taken together, our studies show that squirrel monkeys and capuchin monkeys have some core abilities to represent some of the fundamental properties of objects and agents, comparable to those demonstrated by human infants, which supports the core knowledge view that such representational systems may have a long evolutionary history and exist widely in primates
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (âAIâ) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics â and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the CatĂłlica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
Design Knowledge for Virtual Learning Companions from a Value-centered Perspective
The increasing popularity of conversational agents such as ChatGPT has sparked interest in their potential use in educational contexts but undermines the role of companionship in learning with these tools. Our study targets the design of virtual learning companions (VLCs), focusing on bonding relationships for collaborative learning while facilitating studentsâ time management and motivation. We draw upon design science research (DSR) to derive prescriptive design knowledge for VLCs as the core of our contribution. Through three DSR cycles, we conducted interviews with working students and experts, held interdisciplinary workshops with the target group, designed and evaluated two conceptual prototypes, and fully coded a VLC instantiation, which we tested with students in class. Our approach has yielded 9 design principles, 28 meta-requirements, and 33 design features centered around the value-in-interaction. These encompass Human-likeness and Dialogue Management, Proactive and Reactive Behavior, and Relationship Building on the Relationship Layer (DP1,3,4), Adaptation (DP2) on the Matching Layer, as well as Provision of Supportive Content, Fostering Learning Competencies, Motivational Environment, and Ethical Responsibility (DP5-8) on the Service Layer
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Conversations on Empathy
In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy â be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" â others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice
Digital Innovations for a Circular Plastic Economy in Africa
Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE).
This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy.
Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license
Revising the Future: Exploring Ethnofuturism
Title from PDF of title page, viewed January 4, 2023Dissertation advisors: Anthony S. Shiu and Norma E. CantĂșVitaIncludes bibliographical references (pages 218-236)Dissertation (Ph.D.)--Department of English Language and Literature. University of Missouri--Kansas City, 2022While the desire for a postracial, colorblind society remains an emotional investment, the present reality of race and racist attitudes ingrained in the structure of American culture suggest that any such imagined future is structured based on the standards of whiteness. Representations of this future postracial society tend most often to manifest within speculative, magical realist, science fiction, and other fantastic cultural productions. These fantastic genres, whether set in an alternate present (or past) or some imagined future, give the greatest leeway for writers to navigate concepts of a society-in-the-making. It is important to note, however, that throughout their history, science fiction and futurist narratives have largely been the creation of white writers, and as such have perpetuated dominant notions of whiteness as superior through imaginary postrace worlds that negate racial identities and subsequently rely on the assumption of white as default.
Depictions of colorblind worlds suggest the possibility that we can move past racial issues, and in fact many present that possibility as close-at-hand. The majority of these representations, as the creations of white authors and filmmakers, suggest that the concept of a postracial society has been largely subsumed by white society. However, another way of conceiving alternative concepts of race and identity might be found in those works portraying a future in which racial identity is not placed under erasure but instead becomes a ground for discussion of issues at the core of United States history and culture. Though it is not possible to draw a generalized conclusion about the entirety of an ethnofuturist authorship that encompasses a broad cross-section of experiences, backgrounds, interests, and personalities, larger patterns begin to emerge. Often, writers will engage current race issues in presenting speculations on the future, addressing problems directly instead of sidestepping into a whitewashed postracial vision.
This dissertation looks at how ethnofuturist narratives navigate the cultural thrust of positive representation to counteract racist stereotyping in a multifaceted dialectical space, where an aesthetic of cultural intersection and self-contained ethnic agency starts to take shape, liberated from the perspective of a Eurocentric imperative and redefining the concept of postrace.Introduction -- Genre as a dialect -- Folklore and myth -- Framing super-bodie
Learning and Control of Dynamical Systems
Despite the remarkable success of machine learning in various domains in recent years, our understanding of its fundamental limitations remains incomplete. This knowledge gap poses a grand challenge when deploying machine learning methods in critical decision-making tasks, where incorrect decisions can have catastrophic consequences. To effectively utilize these learning-based methods in such contexts, it is crucial to explicitly characterize their performance. Over the years, significant research efforts have been dedicated to learning and control of dynamical systems where the underlying dynamics are unknown or only partially known a priori, and must be inferred from collected data. However, much of these classical results have focused on asymptotic guarantees, providing limited insights into the amount of data required to achieve desired control performance while satisfying operational constraints such as safety and stability, especially in the presence of statistical noise.
In this thesis, we study the statistical complexity of learning and control of unknown dynamical systems. By utilizing recent advances in statistical learning theory, high-dimensional statistics, and control theoretic tools, we aim to establish a fundamental understanding of the number of samples required to achieve desired (i) accuracy in learning the unknown dynamics, (ii) performance in the control of the underlying system, and (iii) satisfaction of the operational constraints such as safety and stability. We provide finite-sample guarantees for these objectives and propose efficient learning and control algorithms that achieve the desired performance at these statistical limits in various dynamical systems. Our investigation covers a broad range of dynamical systems, starting from fully observable linear dynamical systems to partially observable linear dynamical systems, and ultimately, nonlinear systems.
We deploy our learning and control algorithms in various adaptive control tasks in real-world control systems and demonstrate their strong empirical performance along with their learning, robustness, and stability guarantees. In particular, we implement one of our proposed methods, Fourier Adaptive Learning and Control (FALCON), on an experimental aerodynamic testbed under extreme turbulent flow dynamics in a wind tunnel. The results show that FALCON achieves state-of-the-art stabilization performance and consistently outperforms conventional and other learning-based methods by at least 37%, despite using 8 times less data. The superior performance of FALCON arises from its physically and theoretically accurate modeling of the underlying nonlinear turbulent dynamics, which yields rigorous finite-sample learning and performance guarantees. These findings underscore the importance of characterizing the statistical complexity of learning and control of unknown dynamical systems.</p
AI: Limits and Prospects of Artificial Intelligence
The emergence of artificial intelligence has triggered enthusiasm and promise of boundless opportunities as much as uncertainty about its limits. The contributions to this volume explore the limits of AI, describe the necessary conditions for its functionality, reveal its attendant technical and social problems, and present some existing and potential solutions. At the same time, the contributors highlight the societal and attending economic hopes and fears, utopias and dystopias that are associated with the current and future development of artificial intelligence
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