834 research outputs found
Researching animal research: What the humanities and social sciences can contribute to laboratory animal science and welfare
Every year around 80 million scientific procedures are carried out on animals globally. These experiments have the potential to generate new understandings of biology and clinical treatments. They also give rise to ongoing societal debate.This book demonstrates how the humanities and social sciences can contribute to understanding what is created through animal procedures - including constitutional forms of research governance, different institutional cultures of care, the professional careers of scientists and veterinarians, collaborations with patients and publics, and research animals, specially bred for experiments or surplus to requirements.Developing the idea of the animal research nexus, this book explores how connections and disconnections are made between these different elements, how these have reshaped each other historically, and how they configure the current practice and policy of UK animal research
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) 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 – 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 Católica 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
Climate Change and Critical Agrarian Studies
Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation
Trustworthy Artificial Intelligence (AI) is based on seven technical
requirements sustained over three main pillars that should be met throughout
the system's entire life cycle: it should be (1) lawful, (2) ethical, and (3)
robust, both from a technical and a social perspective. However, attaining
truly trustworthy AI concerns a wider vision that comprises the trustworthiness
of all processes and actors that are part of the system's life cycle, and
considers previous aspects from different lenses. A more holistic vision
contemplates four essential axes: the global principles for ethical use and
development of AI-based systems, a philosophical take on AI ethics, a
risk-based approach to AI regulation, and the mentioned pillars and
requirements. The seven requirements (human agency and oversight; robustness
and safety; privacy and data governance; transparency; diversity,
non-discrimination and fairness; societal and environmental wellbeing; and
accountability) are analyzed from a triple perspective: What each requirement
for trustworthy AI is, Why it is needed, and How each requirement can be
implemented in practice. On the other hand, a practical approach to implement
trustworthy AI systems allows defining the concept of responsibility of
AI-based systems facing the law, through a given auditing process. Therefore, a
responsible AI system is the resulting notion we introduce in this work, and a
concept of utmost necessity that can be realized through auditing processes,
subject to the challenges posed by the use of regulatory sandboxes. Our
multidisciplinary vision of trustworthy AI culminates in a debate on the
diverging views published lately about the future of AI. Our reflections in
this matter conclude that regulation is a key for reaching a consensus among
these views, and that trustworthy and responsible AI systems will be crucial
for the present and future of our society.Comment: 30 pages, 5 figures, under second revie
ReFACT: Updating Text-to-Image Models by Editing the Text Encoder
Text-to-image models are trained on extensive amounts of data, leading them
to implicitly encode factual knowledge within their parameters. While some
facts are useful, others may be incorrect or become outdated (e.g., the current
President of the United States). We introduce ReFACT, a novel approach for
editing factual knowledge in text-to-image generative models. ReFACT updates
the weights of a specific layer in the text encoder, only modifying a tiny
portion of the model's parameters, and leaving the rest of the model
unaffected. We empirically evaluate ReFACT on an existing benchmark, alongside
RoAD, a newly curated dataset. ReFACT achieves superior performance in terms of
generalization to related concepts while preserving unrelated concepts.
Furthermore, ReFACT maintains image generation quality, making it a valuable
tool for updating and correcting factual information in text-to-image models
Unveiling the frontiers of deep learning: innovations shaping diverse domains
Deep learning (DL) enables the development of computer models that are
capable of learning, visualizing, optimizing, refining, and predicting data. In
recent years, DL has been applied in a range of fields, including audio-visual
data processing, agriculture, transportation prediction, natural language,
biomedicine, disaster management, bioinformatics, drug design, genomics, face
recognition, and ecology. To explore the current state of deep learning, it is
necessary to investigate the latest developments and applications of deep
learning in these disciplines. However, the literature is lacking in exploring
the applications of deep learning in all potential sectors. This paper thus
extensively investigates the potential applications of deep learning across all
major fields of study as well as the associated benefits and challenges. As
evidenced in the literature, DL exhibits accuracy in prediction and analysis,
makes it a powerful computational tool, and has the ability to articulate
itself and optimize, making it effective in processing data with no prior
training. Given its independence from training data, deep learning necessitates
massive amounts of data for effective analysis and processing, much like data
volume. To handle the challenge of compiling huge amounts of medical,
scientific, healthcare, and environmental data for use in deep learning, gated
architectures like LSTMs and GRUs can be utilized. For multimodal learning,
shared neurons in the neural network for all activities and specialized neurons
for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table
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
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
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