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Datenrecht und neue Technologien

Marvin Jäschke

Die Künstliche Intelligenz im Urheberrecht

Systeme, Arbeitsprozesse und -ergebnisse moderner KI in einer urheberrechtlichen Gesamtbetrachtung

Progress in IS

This book presents an overview of the emerging topics in Artificial Intelligence (AI) and cybersecurity and addresses the latest AI models that could be potentially applied to a range of cybersecurity areas. Furthermore, it provides different techniques of how to make the AI algorithms secure from adversarial attacks. The book presents the cyber threat landscape and explains the various spectrums of AI and the applications and limitations of AI in cybersecurity. Moreover, it explores the applications and limitations of secure AI. The authors discuss the three categories of machine learning (ML) models and reviews cutting-edge recent Deep Learning (DL) models. Furthermore, the book provides a general AI framework in security as well as different modules of the framework; similarly, chapter four proposes a general framework for secure AI. It explains different aspects of network security including malware and attacks.The book also includes a comprehensive study of various scopes of application security; categorised into three groups of smartphone, web application, and desktop application and delves into the concepts of cloud security. The authors discuss state-of-the-art Internet of Things (IoT) security and describe various challenges of AI for cybersecurity, such as data diversity, model customising, explainability, and time complexity and includes some future work. They provide a comprehensive understanding of adversarial machine learning including the up-to-date adversarial attacks and defences. The book finishes off with a discussion of the challenges and future work in secure AI.
 Overall, this book covers applications of AI models to various fields of cybersecurity and appeals not only to an scholarly audience but also to professionals wanting to learn more about the new developments in these areas.

Challenges, Strategies and Trends

Understanding AI in Cybersecurity and Secure AI

Studien zum ausländischen und internationalen Privatrecht

The property law connection of blockchain tokens still faces several challenges. Not only does decentralized storage appear, at first glance, to make the application of the 
 lex rei sitae
 impossible, but a token-specific property statute must also accommodate the fact that it encompasses both a transaction within the purely decentralized Bitcoin network and the ICO of a blockchain-based security issued by an issuer. Ownership rights in tokens-which can have great practical importance, for instance in cases of insolvency or enforcement-cannot be determined through the application of other conflict-of-law statutes. For rights and dispositions over blockchain-based securities, existing provisions, notably § 17a of the German Custody Act (
 Depotgesetz
 , DepotG) and § 32 of the Electronic Securities Act (
 elektronisches Wertpapiergesetz
 , eWpG), can be applied. Particular challenges arise from the definition of the term "security," which must be interpreted autonomously and given a broad scope of application. An analogous application of § 17a DepotG and § 32 eWpG to tokens issued on a centrally maintained register or by an issuer is possible. Tokens traded on purely decentralized blockchains without intermediaries can be connected through Articles 43 and 46 of the Introductory Act to the Civil Code (
 Einführungsgesetz zum Bürgerlichen Gesetzbuch
 , EGBGB), whereby the often impractical connection to the place where the asset is situated must be corrected by a fallback connection under Article 46 EGBGB. The governing law should be that of the jurisdiction in which the person who last acquired ownership of the token-through possession of the private key-in the context of a transaction presumably relevant under property law can be identified. A Europe-wide codification of a dedicated token property statute, incorporating the connection points mentioned above, would be desirable in order to eliminate existing legal uncertainties.

Das Internationale Sachenrecht der Blockchain-Token

Highway Safety Analytics and Modeling, Second Edition comprehensively covers the key elements for effective transportation engineering and policy decisions based on highway safety data analysis in a single reference. It includes all aspects of the decision-making process, from collecting and assembling data to developing models and evaluating results. It discusses the challenges of working with crash and naturalistic data, identifies problems, and proposes well-researched methods to solve them. It examines the nuances associated with safety data analysis and shows how to best use the information to develop countermeasures, policies, and programs to reduce the frequency and severity of traffic crashes. This thoroughly updated second edition updates the material contained in the book based on the latest advancements in highway safety research as well as feedback from readers. It includes entirely new sections on topics such as digital twins as a source of data, model validation, extreme value models, temporal instability, joint crash frequency and severity modeling, sample size, quasi-induced exposure method, autonomous vehicle safety estimate, and more. This book serves as a valuable reference for students, researchers, and practitioners alike. It provides more examples and exercises to help in using the book for courses, and it continues to complement the Highway Safety Manual (HSM) published by the American Association of State Highway and Transportation Officials (AAHSTO), thus helping in the training of engineers and practitioners to better understand the concepts and methods outlined in the forthcoming HSM.

Highway Safety Analytics and Modeling

Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!Highlights include:1)Neural networks2)Gradient descent3)Classification4)Regularization5)Convolutional neural networks (CNNs)6)Functional API7)Transformer architecture8)Reinforcement learning9)Autoencoders10)Stable Diffusion

Harness the power of AI with this guide to using Keras! Start by reviewing the fundamentals of deep learning and installing the Keras API. Next, follow Python code examples to build your own models, and then train them using classification, gradient descent, and regularization. Design large-scale, multilayer models and improve their decision making with reinforcement learning. With tips for creating generative AI models, this is your cutting-edge resource for working with deep learning!
 
 Highlights include:
 
 1) Neural networks
 
 2) Gradient descent
 
 3) Classification
 
 4) Regularization
 
 5) Convolutional neural networks (CNNs)
 
 6) Functional API
 
 7) Transformer architecture
 
 8) Reinforcement learning
 
 9) Autoencoders
 
 10) Stable Diffusion

The Comprehensive Guide to Deep Learning with the Keras API and Python

Keras 3

Simula SpringerBriefs on Computing

This open access book revolves around predictive mathematical modelling and simulation of brain multiphysics with an emphasis on cerebrospinal fluid flow and solute transport in and around the human brain. The book consists of 10 self-contained and relatively short chapters, each offering a rapid introduction to a key problem or topic, supported by open source software. Readers will gain insights into state-of-the-art mathematical tools and techniques for modelling and simulation of brain multiphysics ranging from classical finite element approaches, network-based modelling techniques and deep neural networks. The target audiences are researchers in applied mathematics, scientific computing, biophysics, bioengineering or computational neuroscience interested in a compact introduction to image-based computational modeling of brain multiphysics and cutting-edge available tools.

From Glymphatics to Deep Learning

Mathematical Modelling of the Human Brain II

AI-Powered Drug Delivery to Neuro and Cardiovascular Systems: Perspectives and Future Challenges is a definitive reference exploring the transformative influence of Artificial Intelligence (AI) in neuro and cardiovascular drug delivery. This book integrates cutting-edge research to explain how AI is poised to reshape clinical protocols, therapeutic strategies, and regulatory standards. By examining advancements in robotics and intelligent systems, it demonstrates how AI enhances precision, personalization, and efficiency in drug delivery for heart and neurological conditions, providing readers with a thorough understanding of these innovations and their potential to revolutionize healthcare practices. The book delves into AI-driven drug design and testing for cardiovascular diseases and explores the application of AI in cardiac drug delivery vehicles. It discusses bioengineered heart and vessel therapeutics, redox homeostasis, and targeted drug replenishment in cardiac matrix remodeling. Additionally, it addresses mitochondrial drug retention, AI-assisted gene therapy for neurodegenerative disorders, and the role of AI in optimizing heart health. The text highlights smart systems for managing cerebrovascular accidents and neuromodulation with pharmacotherapy. Finally, it covers translational challenges, future directions, and the significance of AI-driven prediction models and robotic delivery mechanisms.

Perspectives and Future Challenges

AI Powered Drug delivery to Neuro and Cardiovascular Systems

Springer Undergraduate Texts in Mathematics and Technology

This text provides a mathematically rigorous introduction to modern methods of machine learning and data analysis at the advanced undergraduate/beginning graduate level. The book is self-contained and requires minimal mathematical prerequisites. There is a strong focus on learning how and why algorithms work, as well as developing facility with their practical applications. Apart from basic calculus, the underlying mathematics — linear algebra, optimization, elementary probability, graph theory, and statistics — is developed from scratch in a form best suited to the overall goals. In particular, the wide-ranging linear algebra components are unique in their ordering and choice of topics, emphasizing those parts of the theory and techniques that are used in contemporary machine learning and data analysis. The book will provide a firm foundation to the reader whose goal is to work on applications of machine learning and/or research into the further development of this highly active field of contemporary applied mathematics.

To introduce the reader to a broad range of machine learning algorithms and how they are used in real world applications, the programming language Python is employed and offers a platform for many of the computational exercises. Python notebooks complementing various topics in the book are available on a companion GitHub site specified in the Preface, and can be easily accessed by scanning the QR codes or clicking on the links provided within the text. Exercises appear at the end of each section, including basic ones designed to test comprehension and computational skills, while others range over proofs not supplied in the text, practical computations, additional theoretical results, and further developments in the subject. The Students’ Solutions Manual may be accessed from GitHub. Instructors may apply for access to the Instructors’ Solutions Manual from the link supplied on the text’s Springer website.

The book can be used in a junior or senior level course for students majoring in mathematics with a focus on applications as well as students from other disciplines who desire to learn the tools of modern applied linear algebra and optimization. It may also be used as an introduction to fundamental techniques in data science and machine learning for advanced undergraduate and graduate students or researchers from other areas, including statistics, computer science, engineering, biology, economics and finance, and so on.

Linear Algebra, Data Science, and Machine Learning

Explore the convergence of deep learning and GPU technology. This book is a complete guide for those wishing to use GPUs to accelerate AI workflows.


The book is meant to make complex concepts understandable, with step-by-step instructions on how to set up and use GPUs in deep learning applications. Starting with an introduction to the fundamentals, you'll dive into progressive topics like Convolutional Neural Networks (CNNs) and sequence models, exploring how GPU optimization boosts performance. Further, you will learn the power of generative models, and take your skills by deploying AI models on edge devices. Finally, you will master the art of scaling and distributed training to handle large datasets and complex tasks efficiently.


This book is your roadmap to becoming proficient in deep learning and harnessing the full potential of GPUs.


What You Will Learn:


 How to apply deep learning techniques on GPUs to solve challenging AI problems.

 Optimizing neural networks for faster training and inference on GPUs

 Integration of GPUs with Microsoft Copilots

 Implementing VAEs (Variational Autoencoders) with TensorFlow and PyTorch





Who This Book Is For:


Industry IT professionals in AI. Students pursuing undergraduate and postgraduate degrees in Engineering, Computer Science, Data Science.

Essential GPU ideas, Deep Learning Frameworks, and optimization Approaches

GPU-Accelerated Deep Learning

Konzepte und VerfahrenDeep Learning ist nicht gleich Deep Learning. Lernen Sie, einschlägige Verfahren nicht nur zu implementieren, sondern fundiert auszuwählen und zielorientiert einzusetzen - auch in Kombinationen.Daten und ModelleEntscheidend für das Training der Deep-Learning-Modelle sind Daten. Worauf es bei der Aufbereitung ankommt, hängt vom gewählten Verfahren ab. Lernen Sie das richtige Vorgehen für Aufgaben wie Klassifizierungen, zeitreihenbasierte Vorhersagen, Computer Vision u. v. m.Unüberwachtes LernenMit Autoencodern und VAEs lernen Sie zwei leistungsstarke Architekturen für unüberwachtes Lernen kennen. Nach einer grundlegenden Einführung sehen Sie die praktische Umsetzung mit vollständigen, einsatzbereiten Codebeispielen.Große SprachmodellePassen Sie mit HuggingFace vortrainierte LLMs an spezifische Aufgaben an und optimieren Sie dabei Speicherbedarf und Rechenaufwand.Deployment und EvaluierungSetzen Sie für die Evaluierung und das Deployment Ihrer Modelle modernste Tools ein: von FlowML über TensorBoard und WandB bis zu FastAPI für den lokalen Betrieb und Heroku für die Cloud.

PyTorch-Programmierung professionell! Das meistbenutzte Deep-Learning-Framework in der Praxis: Lernen Sie für spezifische Aufgaben aus realen Projektszenarien, wie Sie mit PyTorch KI-Modelle trainieren, optimieren und produktiv einsetzen. Bert Gollnick zeigt Ihnen in diesem Buch alle einschlägigen Verfahren inklusive Python-Implementierung, von linearer Regression über große Sprachmodelle bis zur Kombination mehrerer Verfahren. Die Anwendungen beinhalten Computer Vision, RAG-Systeme, Zeitreihenanalysen und vieles mehr. Sie evaluieren und deployen Ihre Modelle mit modernsten Methoden und lernen dafür Tools wie FlowML, TensorBoard und FastAPI kennen. Profitieren Sie von vollständigen Codebeispielen, die auf handelsüblicher Hardware lauffähig sind, und entdecken Sie das Zusammenspiel mit PyTorch Lightning, HuggingFace und weiteren Tools.Deep Learning von der Datenaufbereitung über Training und Fineturing bis zum DeploymentVielfältige Architekturen wie Autoencoder, RNNs, LLMs, RAG-Syteme etc.Inkl. PyTorch Lightning, TensorBoard, LangChain, FastAPI u.v.m.Aus dem Inhalt:Installation des FrameworksDeep-Learning-GrundkonzepteVortrainierte Modelle verwendenDaten vorbereitenKlassifizierungsaufgabenComputer VisionEmpfehlungssystemeGraph Neural Networks Große Sprachmodelle (LLMs)Zeitreihen-VorhersagenPyTorch LightningLangChainCloud-Deployment mit HerokuFlowML, TensorBoard und WandBLokaler Einsatz mit FastAPI

KI-Modelle trainieren, tunen und einsetzen

PyTorch

This book responds to the need to distinguish human creations from those produced by AI. It does so by tracing human attributes of authorship and inventorship in statutory requirements for protection and ownership in European copyright and patent laws. Its main contribution lies in exposing shortcomings in how the laws are applied in the UK, Germany, and France. It shows that the human origin of creations is traditionally inferred from their expressive form or technical character. Given the advancements in AI, such inferences are no longer legitimate. What is more, these shortcomings may eventually lead to granting copyright or patent protection where none is lawfully permitted or sufficiently justified. To remedy the situation, this book offers doctrinal and conceptual amendments and proposes law reforms to implement them. This book guides authorities, practitioners, and students through the main arguments of the debate concerning copyright and patents for objects entirely or partly generated by AI. It also makes original contributions to advance the ongoing academic and policy debates on AI and intellectual property law.

Authorship and Inventorship Revisited

Copyright and Patent Laws for the Age of Artificial Intelligence

Zum Werk Das Handbuch "KI im Steuerrecht - Automatisierung, Analyse, Anwendung" bietet eine praxisnahe Einführung in den Einsatz von KI und Large Language Models im Steuerrecht. Es behandelt technische und rechtliche Rahmenbedingungen zu Fragen des Datenschutzes, des Berufsrechts und der europäischen KI-Verordnung, speziell zugeschnitten auf rechts- und steuerberatende Berufe. Grundlagen des Promptings werden ebenso wie besondere Aspekte des Promptings beim Einsatz der KI im Steuerrecht und in der Rechtsberatung dargestellt. Das Werk liefert auch konkrete Anwendungsbeispiele für den KI-Einsatz im Alltag der Steuerberatung, z.B. bei der Gutachtenerstellung, bei Betriebsprüfungen, im Bereich der Verrechnungspreise, bei M&amp;A-Projekten sowie im Rahmen von allgemeinen Büroprozessen und bietet Steuerberaterinnen und Steuerberatern und verwandten Berufsgruppen einen verlässlichen Leitfaden. Vorteile auf einen Blick<ul><li>praxisnah</li><li>anschaulich</li><li>leicht verständlich</li></ul> Zielgruppe Für Steuerberaterinnen und Steuerberater, Fachanwältinnen und Fachanwälte sowie Wirtschaftsprüferinnen und Wirtschaftsprüfer.

Zum Werk 
 Das Handbuch "KI im Steuerrecht - Automatisierung, Analyse, Anwendung" bietet eine praxisnahe Einführung in den Einsatz von KI und Large Language Models im Steuerrecht. Es behandelt technische und rechtliche Rahmenbedingungen zu Fragen des Datenschutzes, des Berufsrechts und der europäischen KI-Verordnung, speziell zugeschnitten auf rechts- und steuerberatende Berufe. Grundlagen des Promptings werden ebenso wie besondere Aspekte des Promptings beim Einsatz der KI im Steuerrecht und in der Rechtsberatung dargestellt.
 
 Das Werk liefert auch konkrete Anwendungsbeispiele für den KI-Einsatz im Alltag der Steuerberatung, z.B. bei der Gutachtenerstellung, bei Betriebsprüfungen, im Bereich der Verrechnungspreise, bei M&amp;A-Projekten sowie im Rahmen von allgemeinen Büroprozessen und bietet Steuerberaterinnen und Steuerberatern und verwandten Berufsgruppen einen verlässlichen Leitfaden.
 Vorteile auf einen Blick<ul><li>praxisnah</li><li>anschaulich</li><li>leicht verständlich</li></ul> Zielgruppe 
 Für Steuerberaterinnen und Steuerberater, Fachanwältinnen und Fachanwälte sowie Wirtschaftsprüferinnen und Wirtschaftsprüfer.

Automatisierung - Analyse - Anwendung

KI im Steuerrecht

Artificial Intelligence in Precision Drug Design: Advanced Applications showcases how artificial intelligence (AI) is revolutionizing modern drug discovery and development. Building upon the foundational principles established in Volume 1, this book dives into real-world applications where AI accelerates innovation, enhances predictive accuracy, and enables breakthrough therapeutics. Featuring contributions from leading global researchers and practitioners, the book explores machine learning, deep learning, and network-based approaches applied to complex biomedical challenges. Key areas include AI driven drug repurposing, combination therapies, immunotherapy, vaccine design, quantum computing, and the integration of large language models in drug discovery. Additional chapters highlight predictive modeling using electronic health records, AI-powered medical imaging, and explainable AI for structure-based drug design. What sets this volume apart is its emphasis on practical impact, demonstrating how data, computation, and interdisciplinary collaboration converge to advance precision medicine. Designed for scientists, clinicians, educators, and students, it serves as both a comprehensive reference and a source of inspiration for leveraging AI to transform healthcare.

Foundations and Core Techniques

Artificial Intelligence in Precision Drug Design, Volume 1

Die Künstliche Intelligenz im Urheberrecht

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