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  • Undetermined  (18)
  • Polish
  • Spanish
  • Cham : Springer Nature  (18)
  • Machine learning  (18)
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  • Undetermined  (18)
  • Polish
  • Spanish
  • English  (3)
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  • 1
    Online Resource
    Online Resource
    New Mechanism : Explanation, Emergence and Reduction (2024)
    Cham : Springer Nature
    Details
    ISBN: 9783031469176 , 9783031469169
    Language: Undetermined
    Pages: 1 Online-Ressource (272 p.)
    Series Statement: History, Philosophy and Theory of the Life Sciences
    Keywords: Philosophy: epistemology & theory of knowledge ; Philosophy of science ; Machine learning ; Evo-devo ; Structural realism ; New mechanism ; Mechanistic explanation ; Mechanistic models ; Organisms need mechanisms ; Mechanisms of emergence ; Mechanisms in chemistry
    Abstract: This open access book addresses the epistemological and ontological significance as well as the scope of new mechanism. In particular, this book addresses the issues of what is "new" about new mechanism, the epistemological and ontological reasons underlying the adoption of mechanistic instead of other modelling strategies as well as the possibility of mechanistic explanation to accommodate a non-trivial notion of emergence. Arguably, new mechanism has been particularly successful in making sense of scientific practice in the molecular life sciences. But what about other sciences? This book enlarges the context of analysis, addressing the issue of the putative compatibility between the current ways of conceiving new mechanism and actual scientific practices in quantum physics, chemistry, biochemistry, developmental biology and the cognitive sciences
    Note: English
    URL: kostenfrei
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  • 2
    Online Resource
    Online Resource
    Artificial Intelligence and Machine Learning in Health Care and Medical Sciences : Best Practices and Pitfalls (2024)
    Cham : Springer Nature
    Details
    ISBN: 9783031393556 , 9783031393549
    Language: Undetermined
    Pages: 1 Online-Ressource (810 p.)
    Series Statement: Health Informatics
    Keywords: Medical equipment and techniques ; Information technology: general topics ; Nursing and ancillary services ; Computer science ; Biology, life sciences ; Public health and preventive medicine ; Predictive analytics ; Artificial intelligence ; Medicine ; Machine learning ; Causal discovery ; Causal inference ; Genomics ; Medical knowledge discovery ; Clinical risk models ; Clinical risk stratification
    Abstract: This open access book provides a detailed review of the latest methods and applications of artificial intelligence (AI) and machine learning (ML) in medicine. With chapters focusing on enabling the reader to develop a thorough understanding of the key concepts in these subject areas along with a range of methods and resulting models that can be utilized to solve healthcare problems, the use of causal and predictive models are comprehensively discussed. Care is taken to systematically describe the concepts to facilitate the reader in developing a thorough conceptual understanding of how different methods and resulting models function and how these relate to their applicability to various issues in health care and medical sciences. Guidance is also given on how to avoid pitfalls that can be encountered on a day-to-day basis and stratify potential clinical risks. Artificial Intelligence and Machine Learning in Health Care and Medical Sciences: Best Practices and Pitfallsis a comprehensive guide to how AI and ML techniques can best be applied in health care. The emphasis placed on how to avoid a variety of pitfalls that can be encountered makes it an indispensable guide for all medical informatics professionals and physicians who utilize these methodologies on a day-to-day basis. Furthermore, this work will be of significant interest to health data scientists, administrators and to students in the health sciences seeking an up-to-date resource on the topic
    Note: English
    URL: kostenfrei
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  • 3
    Online Resource
    Online Resource
    Digital Twin : Architectures, Networks, and Applications (2024)
    Cham : Springer Nature
    Details
    ISBN: 9783031518195 , 9783031518188
    Language: Undetermined
    Pages: 1 Online-Ressource (126 p.)
    Series Statement: Simula SpringerBriefs on Computing
    Keywords: Maths for engineers ; Communications engineering / telecommunications ; WAP (wireless) technology ; Network hardware ; Artificial intelligence ; Machine learning ; Digital Twin ; Edge Computing ; Machine Learning ; 6G ; Internet of Things
    Abstract: This open access book offers comprehensive, self-contained knowledge on Digital Twin (DT), which is a very promising technology for achieving digital intelligence in the next-generation wireless communications and computing networks. DT is a key technology to connect physical systems and digital spaces in Metaverse. The objectives of this book are to provide the basic concepts of DT, to explore the promising applications of DT integrated with emerging technologies, and to give insights into the possible future directions of DT. For easy understanding, this book also presents several use cases for DT models and applications in different scenarios. The book starts with the basic concepts, models, and network architectures of DT. Then, we present the new opportunities when DT meets edge computing, Blockchain and Artificial Intelligence, and distributed machine learning (e.g., federated learning, multi-agent deep reinforcement learning). We also present a wide application of DT as an enabling technology for 6G networks, Aerial-Ground Networks, and Unmanned Aerial Vehicles (UAVs). The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of DT. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
    Note: English
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  • 4
    Online Resource
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    Manifold Learning : Model Reduction in Engineering (2024)
    Cham : Springer Nature
    Details
    ISBN: 9783031527647 , 9783031527630
    Language: Undetermined
    Pages: 1 Online-Ressource (107 p.)
    Series Statement: SpringerBriefs in Computer Science
    Keywords: Machine learning ; Mathematical & statistical software ; Probability & statistics ; Engineering thermodynamics ; Production engineering ; Mathematical physics ; Computational Mechanics ; Data Augmentation ; Deep Learning ; Digital Twining ; Dimensionality Reduction ; GenericROM Library ; High-Fidelity Model ; Hyper-reduction ; Image-based Digital Twins ; Manifold Learning ; Model Order Reduction ; Mordicus ; Multiphysics Modeling
    Abstract: This Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms. The book involves a survey of key methods of model order reduction applied to model-based engineering and digital twining, by learning linear or nonlinear latent spaces. Projection-based reduced order models are the projection of mechanical equations on a latent space that have been learnt from both synthetic data and experimental data. Various descriptions and representations of structured data for model reduction are presented in the applications and survey chapters. Image-based digital twins are developed in a reduced setting. Reduced order models of as-manufactured components predict the mechanical effects of shape variations. A similar workflow is extended to multiphysics or coupled problems, with high dimensional input fields. Practical techniques are proposed for data augmentation and also for hyper-reduction, which is a key point to speed up projection-based model order reduction of finite element models. The book gives access to python libraries available on gitlab.com, which have been developed as part of the research program [FUI-25] MORDICUS funded by the French government. Similarly to deep learning for computer vision, deep learning for model order reduction circumvents the need to design parametric problems prior reducing models. Such an approach is highly relevant for image-base modelling or multiphysics modelling
    Note: English
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  • 5
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    Online Resource
    Statistical Foundations of Actuarial Learning and its Applications (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031124099
    Language: Undetermined
    Pages: 1 Online-Ressource (605 p.)
    Series Statement: Springer Actuarial
    Keywords: Applied mathematics ; Probability & statistics ; Machine learning ; Algorithms & data structures ; Artificial intelligence
    Abstract: This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus
    Note: English
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  • 6
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    Revealing Media Bias in News Articles : NLP Techniques for Automated Frame Analysis (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031176937
    Language: Undetermined
    Pages: 1 Online-Ressource (238 p.)
    Keywords: Natural language & machine translation ; Machine learning ; Media studies ; Language: history & general works ; Political science & theory
    Abstract: This open access book presents an interdisciplinary approach to reveal biases in English news articles reporting on a given political event. The approach named person-oriented framing analysis identifies the coverage’s different perspectives on the event by assessing how articles portray the persons involved in the event. In contrast to prior automated approaches, the identified frames are more meaningful and substantially present in person-oriented news coverage. The book is structured in seven chapters: Chapter 1 presents a few of the severe problems caused by slanted news coverage and identifies the research gap that motivated the research described in this thesis. Chapter 2 discusses manual analysis concepts and exemplary studies from the social sciences and automated approaches, mostly from computer science and computational linguistics, to analyze and reveal media bias. This way, it identifies the strengths and weaknesses of current approaches for identifying and revealing media bias. Chapter 3 discusses the solution design space to address the identified research gap and introduces person-oriented framing analysis (PFA), a new approach to identify substantial frames and to reveal slanted news coverage. Chapters 4 and 5 detail target concept analysis and frame identification, the first and second component of PFA. Chapter 5 also introduces the first large-scale dataset and a novel model for target-dependent sentiment classification (TSC) in the news domain. Eventually, Chapter 6 introduces Newsalyze, a prototype system to reveal biases to non-expert news consumers by using the PFA approach. In the end, Chapter 7 summarizes the thesis and discusses the strengths and weaknesses of the thesis to derive ideas for future research on media bias. This book mainly targets researchers and graduate students from computer science, computational linguistics, political science, and further social sciences who want to get an overview of the relevant state of the art in the other related disciplines and understand and tackle the issue of bias from a more effective, interdisciplinary viewpoint
    Note: English
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  • 7
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    Process Mining Workshops : ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031278150 , 9783031278143
    Language: Undetermined
    Pages: 1 Online-Ressource (592 p.)
    Series Statement: Lecture Notes in Business Information Processing
    Keywords: Data mining ; Business mathematics & systems ; Machine learning ; Information technology: general issues ; Health & safety aspects of IT
    Abstract: This open access book constitutes revised selected papers from the International Workshops held at the 4th International Conference on Process Mining, ICPM 2022, which took place in Bozen-Bolzano, Italy, during October 23–28, 2022. The conference focuses on the area of process mining research and practice, including theory, algorithmic challenges, and applications. The co-located workshops provided a forum for novel research ideas. The 42 papers included in this volume were carefully reviewed and selected from 89 submissions. They stem from the following workshops: – 3rd International Workshop on Event Data and Behavioral Analytics (EDBA) – 3rd International Workshop on Leveraging Machine Learning in Process Mining (ML4PM) – 3rd International Workshop on Responsible Process Mining (RPM) (previously known as Trust, Privacy and Security Aspects in Process Analytics) – 5th International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) – 3rd International Workshop on Streaming Analytics for Process Mining (SA4PM) – 7th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI) – 1st International Workshop on Education meets Process Mining (EduPM) – 1st International Workshop on Data Quality and Transformation in Process Mining (DQT-PM)
    Note: English
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  • 8
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    Machine Learning and Its Application to Reacting Flows : ML and Combustion (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031162480
    Language: Undetermined
    Pages: 1 Online-Ressource (346 p.)
    Series Statement: Lecture Notes in Energy
    Keywords: Fossil fuel technologies ; Engineering thermodynamics ; Machine learning ; Thermodynamics & heat
    Abstract: This open access book introduces and explains machine learning (ML) algorithms and techniques developed for statistical inferences on a complex process or system and their applications to simulations of chemically reacting turbulent flows. These two fields, ML and turbulent combustion, have large body of work and knowledge on their own, and this book brings them together and explain the complexities and challenges involved in applying ML techniques to simulate and study reacting flows. This is important as to the world’s total primary energy supply (TPES), since more than 90% of this supply is through combustion technologies and the non-negligible effects of combustion on environment. Although alternative technologies based on renewable energies are coming up, their shares for the TPES is are less than 5% currently and one needs a complete paradigm shift to replace combustion sources. Whether this is practical or not is entirely a different question, and an answer to this question depends on the respondent. However, a pragmatic analysis suggests that the combustion share to TPES is likely to be more than 70% even by 2070. Hence, it will be prudent to take advantage of ML techniques to improve combustion sciences and technologies so that efficient and “greener” combustion systems that are friendlier to the environment can be designed. The book covers the current state of the art in these two topics and outlines the challenges involved, merits and drawbacks of using ML for turbulent combustion simulations including avenues which can be explored to overcome the challenges. The required mathematical equations and backgrounds are discussed with ample references for readers to find further detail if they wish. This book is unique since there is not any book with similar coverage of topics, ranging from big data analysis and machine learning algorithm to their applications for combustion science and system design for energy generation
    Note: English
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  • 9
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    Handbook of Computational Social Science for Policy (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031166242
    Language: Undetermined
    Pages: 1 Online-Ressource (490 p.)
    Keywords: Algorithms & data structures ; Artificial intelligence ; Probability & statistics ; Society & social sciences ; Machine learning
    Abstract: This open access handbook describes foundational issues, methodological approaches and examples on how to analyse and model data using Computational Social Science (CSS) for policy support. Up to now, CSS studies have mostly developed on a small, proof-of concept, scale that prevented from unleashing its potential to provide systematic impact to the policy cycle, as well as from improving the understanding of societal problems to the definition, assessment, evaluation, and monitoring of policies. The aim of this handbook is to fill this gap by exploring ways to analyse and model data for policy support, and to advocate the adoption of CSS solutions for policy by raising awareness of existing implementations of CSS in policy-relevant fields. To this end, the book explores applications of computational methods and approaches like big data, machine learning, statistical learning, sentiment analysis, text mining, systems modelling, and network analysis to different problems in the social sciences. The book is structured into three Parts: the first chapters on foundational issues open with an exposition and description of key policymaking areas where CSS can provide insights and information. In detail, the chapters cover public policy, governance, data justice and other ethical issues. Part two consists of chapters on methodological aspects dealing with issues such as the modelling of complexity, natural language processing, validity and lack of data, and innovation in official statistics. Finally, Part three describes the application of computational methods, challenges and opportunities in various social science areas, including economics, sociology, demography, migration, climate change, epidemiology, geography, and disaster management. The target audience of the book spans from the scientific community engaged in CSS research to policymakers interested in evidence-informed policy interventions, but also includes private companies holding data that can be used to study social sciences and are interested in achieving a policy impact
    Note: English
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  • 10
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    Learning to Quantify (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031204678 , 9783031204661
    Language: Undetermined
    Pages: 1 Online-Ressource (137 p.)
    Series Statement: The Information Retrieval Series
    Keywords: Information retrieval ; Data mining ; Machine learning
    Abstract: This open access book provides an introduction and an overview of learning to quantify (a.k.a. “quantification”), i.e. the task of training estimators of class proportions in unlabeled data by means of supervised learning. In data science, learning to quantify is a task of its own related to classification yet different from it, since estimating class proportions by simply classifying all data and counting the labels assigned by the classifier is known to often return inaccurate (“biased”) class proportion estimates. The book introduces learning to quantify by looking at the supervised learning methods that can be used to perform it, at the evaluation measures and evaluation protocols that should be used for evaluating the quality of the returned predictions, at the numerous fields of human activity in which the use of quantification techniques may provide improved results with respect to the naive use of classification techniques, and at advanced topics in quantification research. The book is suitable to researchers, data scientists, or PhD students, who want to come up to speed with the state of the art in learning to quantify, but also to researchers wishing to apply data science technologies to fields of human activity (e.g., the social sciences, political science, epidemiology, market research) which focus on aggregate (“macro”) data rather than on individual (“micro”) data
    Note: English
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    URL: Inhaltsverzeichnis
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  • 11
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    Classification and Data Science in the Digital Age (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031090349 , 9783031090332
    Language: Undetermined
    Pages: 1 Online-Ressource (416 p.)
    Series Statement: Studies in Classification, Data Analysis, and Knowledge Organization
    Keywords: Algorithms & data structures ; Artificial intelligence ; Mathematical & statistical software ; Machine learning ; Data mining ; Probability & statistics
    Abstract: The contributions gathered in this open access book focus on modern methods for data science and classification and present a series of real-world applications. Numerous research topics are covered, ranging from statistical inference and modeling to clustering and dimension reduction, from functional data analysis to time series analysis, and network analysis. The applications reflect new analyses in a variety of fields, including medicine, marketing, genetics, engineering, and education. The book comprises selected and peer-reviewed papers presented at the 17th Conference of the International Federation of Classification Societies (IFCS 2022), held in Porto, Portugal, July 19–23, 2022. The IFCS federates the classification societies and the IFCS biennial conference brings together researchers and stakeholders in the areas of Data Science, Classification, and Machine Learning. It provides a forum for presenting high-quality theoretical and applied works, and promoting and fostering interdisciplinary research and international cooperation. The intended audience is researchers and practitioners who seek the latest developments and applications in the field of data science and classification
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  • 12
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    Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2022 (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031100710 , 9783031100703
    Language: Undetermined
    Pages: 1 Online-Ressource (380 p.)
    Keywords: Robotics ; Automatic control engineering ; Artificial intelligence ; Machine learning
    Abstract: This Open Access proceedings presents a good overview of the current research landscape of assembly, handling and industrial robotics. The objective of MHI Colloquium is the successful networking at both academic and management level. Thereby, the colloquium focuses an academic exchange at a high level in order to distribute the obtained research results, to determine synergy effects and trends, to connect the actors in person and in conclusion, to strengthen the research field as well as the MHI community. In addition, there is the possibility to become acquatined with the organizing institute. Primary audience is formed by members of the scientific society for assembly, handling and industrial robotics (WGMHI)
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  • 13
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    Digital Interaction and Machine Intelligence : Proceedings of MIDI’2022 – 10th Machine Intelligence and Digital Interaction – Conference, December 12-15, 2022, Warsaw, Poland (Online) (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031376498 , 9783031376481
    Language: Undetermined
    Pages: 1 Online-Ressource (322 p.)
    Series Statement: Lecture Notes in Networks and Systems
    Keywords: Artificial intelligence ; Databases ; Machine learning
    Abstract: This open access book presents the proceedings of the 10th Machine Intelligence and Digital Interaction Conference. Artificial intelligence (AI) is rapidly affecting more aspects of our lives as a result of significant advancements in its research and the widespread usage of interactive technologies. This has led to the birth of several new social phenomena. Many nations have been working to comprehend these phenomena and discover solutions for moving artificial intelligence development in the proper direction to benefit individuals and communities at large. These efforts necessitate multidisciplinary approaches, encompassing not only the scientific fields involved in the creation of artificial intelligence and human–computer interaction but also strong collaboration between academics and practitioners. Because of this, the primary objective of the MIDI conference, which was conducted online on December 13–15, 2022, is to combine two up until recently distinct disciplines of research—artificial intelligence and human–technology interaction
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  • 14
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    Foundation Models for Natural Language Processing : Pre-trained Language Models Integrating Media (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031231902 , 9783031231896
    Language: Undetermined
    Pages: 1 Online-Ressource (436 p.)
    Series Statement: Artificial Intelligence: Foundations, Theory, and Algorithms
    Keywords: Natural language & machine translation ; Computational linguistics ; Artificial intelligence ; Expert systems / knowledge-based systems ; Machine learning
    Abstract: This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP. These models are first pre-trained on large collections of text documents to acquire general syntactic knowledge and semantic information. Then, they are fine-tuned for specific tasks, which they can often solve with superhuman accuracy. When the models are large enough, they can be instructed by prompts to solve new tasks without any fine-tuning. Moreover, they can be applied to a wide range of different media and problem domains, ranging from image and video processing to robot control learning. Because they provide a blueprint for solving many tasks in artificial intelligence, they have been called Foundation Models. After a brief introduction to basic NLP models the main pre-trained language models BERT, GPT and sequence-to-sequence transformer are described, as well as the concepts of self-attention and context-sensitive embedding. Then, different approaches to improving these models are discussed, such as expanding the pre-training criteria, increasing the length of input texts, or including extra knowledge. An overview of the best-performing models for about twenty application areas is then presented, e.g., question answering, translation, story generation, dialog systems, generating images from text, etc. For each application area, the strengths and weaknesses of current models are discussed, and an outlook on further developments is given. In addition, links are provided to freely available program code. A concluding chapter summarizes the economic opportunities, mitigation of risks, and potential developments of AI
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  • 15
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    Safety in the Digital Age : Sociotechnical Perspectives on Algorithms and Machine Learning (2023)
    Cham : Springer Nature
    Details
    ISBN: 9783031326332 , 9783031326325
    Language: Undetermined
    Pages: 1 Online-Ressource (137 p.)
    Series Statement: SpringerBriefs in Applied Sciences and Technology
    Series Statement: SpringerBriefs in Safety Management
    Keywords: Engineering: general ; Machine learning ; Sociology
    Abstract: This open access book gathers authors from a wide range of social-scientific and engineering disciplines to review challenges from their respective fields that arise from the processes of social and technological transformation taking place worldwide. The result is a much-needed collection of knowledge about the integration of social, organizational and technical challenges that need to be tackled to uphold safety in the digital age. The contributors whose work features in this book help their readers to navigate the massive increase in the capability to generate and use data in developing algorithms intended for automation of work, machine learning and next-generation artificial intelligence and the blockchain technology already in such extensive use in real-world organizations. This book deals with such issues as: · How can high-risk and safety-critical systems be affected by these developments, in terms of their activities, their organization, management and regulation? · What are the sociotechnical challenges of the proliferation of big data, algorithmic influence and cyber-security challenges in health care, transport, energy production/distribution and production of goods? Understanding the ways these systems operate in the rapidly changing digital context has become a core issue for academic researchers and other experts in safety science, security and critical-infrastructure protection. The research presented here offers a lens through which the reader can grasp the way such systems evolve and the implications for safety—an increasingly multidisciplinary challenge that this book does not shrink from addressing
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  • 16
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    Regularized System Identification : Learning Dynamic Models from Data (2022)
    Cham : Springer Nature
    Details
    ISBN: 9783030958602
    Language: Undetermined
    Pages: 1 Online-Ressource (377 p.)
    Series Statement: Communications and Control Engineering
    Keywords: Machine learning ; Automatic control engineering ; Statistical physics ; Bayesian inference ; Probability & statistics ; Cybernetics & systems theory
    Abstract: This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors' reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book
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  • 17
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    xxAI - Beyond Explainable AI : International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers (2022)
    Cham : Springer Nature
    Details
    ISBN: 9783031040832
    Language: Undetermined
    Pages: 1 Online-Ressource (397 p.)
    Series Statement: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence
    Keywords: Artificial intelligence ; Machine learning
    Abstract: This is an open access book. Statistical machine learning (ML) has triggered a renaissance of artificial intelligence (AI). While the most successful ML models, including Deep Neural Networks (DNN), have developed better predictivity, they have become increasingly complex, at the expense of human interpretability (correlation vs. causality). The field of explainable AI (xAI) has emerged with the goal of creating tools and models that are both predictive and interpretable and understandable for humans. Explainable AI is receiving huge interest in the machine learning and AI research communities, across academia, industry, and government, and there is now an excellent opportunity to push towards successful explainable AI applications. This volume will help the research community to accelerate this process, to promote a more systematic use of explainable AI to improve models in diverse applications, and ultimately to better understand how current explainable AI methods need to be improved and what kind of theory of explainable AI is needed. After overviews of current methods and challenges, the editors include chapters that describe new developments in explainable AI. The contributions are from leading researchers in the field, drawn from both academia and industry, and many of the chapters take a clear interdisciplinary approach to problem-solving. The concepts discussed include explainability, causability, and AI interfaces with humans, and the applications include image processing, natural language, law, fairness, and climate science
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  • 18
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    Digital Interaction and Machine Intelligence : Proceedings of MIDI'2021 - 9th Machine Intelligence and Digital Interaction Conference, December 9-10, 2021, Warsaw, Poland (2022)
    Cham : Springer Nature
    Details
    ISBN: 9783031114328
    Language: Undetermined
    Pages: 1 Online-Ressource (300 p.)
    Series Statement: Lecture Notes in Networks and Systems
    Keywords: Artificial intelligence ; Databases ; Machine learning
    Abstract: This book is open access, which means that you have free and unlimited access. This book presents the Proceedings of the 9th Machine Intelligence and Digital Interaction Conference. Significant progress in the development of artificial intelligence (AI) and its wider use in many interactive products are quickly transforming further areas of our life, which results in the emergence of various new social phenomena. Many countries have been making efforts to understand these phenomena and find answers on how to put the development of artificial intelligence on the right track to support the common good of people and societies. These attempts require interdisciplinary actions, covering not only science disciplines involved in the development of artificial intelligence and human-computer interaction but also close cooperation between researchers and practitioners. For this reason, the main goal of the MIDI conference held on 9-10.12.2021 as a virtual event is to integrate two, until recently, independent fields of research in computer science: broadly understood artificial intelligence and human-technology interaction
    Note: English
    URL: kostenfrei
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