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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

Studien und Beiträge zum Öffentlichen Recht

The law of public property has long been regarded as a field that lacks a sufficiently developed doctrinal foundation. Since the well-known Hamburg City Seal case in the 1990s, the prevailing view that public property under state administration constitutes a modified form of private ownership has lost considerable support, as critics argue that it violates the principle that administrative action requires a statutory basis. Marvin Gülker demonstrates that such an approach fails to do justice to this complex body of law, whose roots extend back to the 
 ius commune
 . He refines the traditional doctrine by eliminating its rule-of-law deficiencies and identifies the pre- and post-constitutional legal norms from which public control over property may be derived. To this end, he develops a conceptual framework for the notion of "public property" and distinguishes the law of public property, which primarily serves a protective function, from the law governing public institutions, which focuses on use and access. He situates this field of law within the allocation of competences between the Federation, the Länder, and the European Union, and demonstrates why the central absolute public-law right and its constitution through dedication (
 Widmung
 ) do not violate Article 14 of the German Basic Law.
 

 The analysis is not confined to the objects traditionally associated with this area of law. Rather, it ranges from the Imperial Regalia, public roads, and 
 res sacrae
 to virtual spaces and internet domains. Drawing on numerous examples, the author explores manifestations of public control over property in areas that have so far received little attention, including civil protection law under the Federal Performance Act (
 Bundesleistungsgesetz
 , BLG) and the Civil Defence Act (
 Schutzbereichsgesetz
 , SchBG), archival law, and the law of classified information. In the digital sphere, he shows that the systematized doctrine of public property presented in this study can also provide the foundation for a public law of intangible assets.

Zur Systematik öffentlicher Sachherrschaft von der Widmung bis zur Durchsetzung im analogen und im digitalen Raum

Öffentliche Sachen im digitalen Zeitalter

VDV-Schriftenreihe

Die vollständig überarbeitete und erweiterte 2. Auflage von "KI in Geodäsie und Geoinformatik" bietet einen fundierten Überblick über aktuelle Verfahren, Konzepte und Anwendungsfelder der künstlichen Intelligenz im Kontext von Geodäsie und Geoinformatik. Ein interdisziplinäres Autorenteam aus 70 Expertinnen und Experten stellt bewährte und neu entwickelte Methoden vor und zeigt, wie KI die Erfassung, Verarbeitung und Analyse von Geodaten nachhaltig verändert.

Die einleitenden Kapitel erläutern grundlegende Prinzipien des maschinellen Lernens, neuronale Netze für die Bildanalyse sowie Ansätze einer verifizierbaren und sicheren KI. Darauf aufbauend werden zentrale Softwareplattformen und industrielle Ökosysteme wie Esri und Hexagon vorgestellt und durch praxisorientierte Beispiele ergänzt. Ein Schwerpunkt liegt auf anwendungsnahen Forschungs- und Entwicklungsprojekten. Zahlreiche reale Projekte demonstrieren, wie KI eingesetzt wird: Gebäudeerkennung und Georeferenzierung hochauflösender Satellitenbilder über Infrastrukturmonitoring, Oberflächen- und Verkehrsanalysen und intelligente Parkraumkartierung bis hin zu multimodalen KI-Systemen, die räumliche und zeitliche Daten gemeinsam auswerten. Weitere Beiträge behandeln spezialisierte Verfahren wie die Detektion von Flughindernissen aus 3D-Punktwolken, Schwingungsmessungen zur Verkehrserfassung, Machine Learning in der Wärmeleitplanung oder die Optimierung spektraler und räumlicher Trainingsdaten für Deep-Learning-Modelle. Ein weiterer Themenblock zeigt, wie KI deutschlandweite Landbedeckungskartierungen, die Land- und Forstwirtschaft sowie das Monitoring von Grünflächen unterstützt. Mit UrbanAI wird zudem ein zukunftsorientierter Ansatz zur Entwicklung klimaangepasster, nachhaltiger Städte vorgestellt.

Dieses Werk bietet eine wissenschaftlich präzise und praxisnahe Gesamtdarstellung der aktuellen Entwicklungen der Geospatial AI und richtet sich gleichermaßen an Entscheider, Fachleute, Forschende und Studierende in Geodäsie, Geoinformatik, Geomarketing, Geowissenschaften und verwandten Disziplinen.

Die vollständig überarbeitete und erweiterte 2. Auflage von „KI in Geodäsie und Geoinformatik“ bietet einen fundierten Überblick über aktuelle Verfahren, Konzepte und Anwendungsfelder der künstlichen Intelligenz im Kontext von Geodäsie und Geoinformatik. Ein interdisziplinäres Autorenteam aus 70 Expertinnen und Experten stellt bewährte und neu entwickelte Methoden vor und zeigt, wie KI die Erfassung, Verarbeitung und Analyse von Geodaten nachhaltig verändert.

Die einleitenden Kapitel erläutern grundlegende Prinzipien des maschinellen Lernens, neuronale Netze für die Bildanalyse sowie Ansätze einer verifizierbaren und sicheren KI. Darauf aufbauend werden zentrale Softwareplattformen und industrielle Ökosysteme wie Esri und Hexagon vorgestellt und durch praxisorientierte Beispiele ergänzt. Ein Schwerpunkt liegt auf anwendungsnahen Forschungs- und Entwicklungsprojekten. Zahlreiche reale Projekte demonstrieren, wie KI eingesetzt wird: Gebäudeerkennung und Georeferenzierung hochauflösender Satellitenbilder über Infrastrukturmonitoring, Oberflächen- und Verkehrsanalysen und intelligente Parkraumkartierung bis hin zu multimodalen KI-Systemen, die räumliche und zeitliche Daten gemeinsam auswerten. Weitere Beiträge behandeln spezialisierte Verfahren wie die Detektion von Flughindernissen aus 3D-Punktwolken, Schwingungsmessungen zur Verkehrserfassung, Machine Learning in der Wärmeleitplanung oder die Optimierung spektraler und räumlicher Trainingsdaten für Deep-Learning-Modelle. Ein weiterer Themenblock zeigt, wie KI deutschlandweite Landbedeckungskartierungen, die Land- und Forstwirtschaft sowie das Monitoring von Grünflächen unterstützt. Mit UrbanAI wird zudem ein zukunftsorientierter Ansatz zur Entwicklung klimaangepasster, nachhaltiger Städte vorgestellt.

Dieses Werk bietet eine wissenschaftlich präzise und praxisnahe Gesamtdarstellung der aktuellen Entwicklungen der Geospatial AI und richtet sich gleichermaßen an Entscheider, Fachleute, Forschende und Studierende in Geodäsie, Geoinformatik, Geomarketing, Geowissenschaften und verwandten Disziplinen.

Potenziale und Best-Practice-Beispiele

Künstliche Intelligenz in Geodäsie und Geoinformatik

Contributions to Economics

This book examines the revolutionary impact of artificial intelligence (AI) on communication and collaboration. In recent years, advances in large language models and deep learning have enabled seamless human communication across diverse languages and even with other species, such as dogs and humpback whales. AI is also unlocking the language of plants, including tomatoes and the 'wood wide web.'
The book explores how technologies in natural language processing, image recognition, audio analysis, and network analysis facilitate unprecedented fuzzy data analysis. This progress is crucial for promoting sustainability and addressing climate change by uncovering patterns in complex environmental data that inform sustainable practices and policies. The book further focuses on studying interspecies communication, where AI technologies decode communication patterns between humans and other species, allowing a better understanding of the natural world. It highlights how plants can capture human emotions, enhancing well-being and productivity.
Presenting empirical applications and case studies, the book analyzes societal, economic, and ecological implications, advancing theoretical insights, and introducing innovative methods. It will appeal to an interdisciplinary audience of scholars, students, and researchers in the social sciences, economics, business, sociology, psychology, environmental studies, and media and communication who are interested in a deeper understanding of how artificial intelligence and interspecies communication can contribute to building a sustainable future.&nbsp;

This book examines the revolutionary impact of artificial intelligence (AI) on communication and collaboration. In recent years, advances in large language models and deep learning have enabled seamless human communication across diverse languages and even with other species, such as dogs and humpback whales. AI is also unlocking the language of plants, including tomatoes and the 'wood wide web.'The book explores how technologies in natural language processing, image recognition, audio analysis, and network analysis facilitate unprecedented fuzzy data analysis. This progress is crucial for promoting sustainability and addressing climate change by uncovering patterns in complex environmental data that inform sustainable practices and policies. The book further focuses on studying interspecies communication, where AI technologies decode communication patterns between humans and other species, allowing a better understanding of the natural world. It highlights how plants can capture human emotions, enhancing well-being and productivity.Presenting empirical applications and case studies, the book analyzes societal, economic, and ecological implications, advancing theoretical insights, and introducing innovative methods. It will appeal to an interdisciplinary audience of scholars, students, and researchers in the social sciences, economics, business, sociology, psychology, environmental studies, and media and communication who are interested in a deeper understanding of how artificial intelligence and interspecies communication can contribute to building a sustainable future.&nbsp;

Connecting Species

Artificial Intelligence and Networks for a Sustainable Future

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

Navigate the intersection of deep learning technologies and Rust programming with Deep Learning with Rust. This groundbreaking book is predicated on the idea that the future of efficient, reliable, and secure deep learning applications lies in leveraging Rust's unparalleled features, such as its guarantee of memory safety, concurrency without data races, and abstractions that don't compromise performance.

You’ll explore basic neural concepts through the construction and deployment of sophisticated models using Rust. The language’s advantages for deep learning are emphasized, including enhanced performance, safety, and scalability. Through a unique blend of theory and application, you’ll explore how Rust can address the growing demand for technologies that can ensure faster, more secure deep learning solutions in an era where data volume and complexity are increasing exponentially.

This book meets a pressing need at a time when the integration of deep learning is critical across many diverse sectors. Programming languages can not only accelerate the development of AI models but also ensure they are built on a foundation of security and efficiency. This book is an indispensable resource for anyone looking to master the art of building next-generation deep learning with Rust’s growing ecosystem

What You Will Learn

Understand deep learning foundations and Rust programming principles.

Implement and optimize deep learning models in Rust, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs.

Develop practical deep learning applications to solve real-world problems, including natural language processing, computer vision, and speech recognition.

Explore Rust’s safety features, including its strict type of system and ownership model, and learn strategies to create reliable and secure AI software.

Gain an understanding of the broader ecosystem of tools and libraries available for deep learning in Rust.




Who This Book Is for

A broad audience with varying levels of experience and knowledge, including advanced programmers with a solid foundation in Rust or other programming languages (Python, C++, and Java) who are interested in learning how Rust can be used for deep learning apps. It may also be suitable for data scientists and AI practitioners who are looking to understand how Rust can enhance the performance and safety of deep learning models, even if they are new to the Rust programming language.

&nbsp;What You Will Learn<ul><li>Understand deep learning foundations and Rust programming principles.</li><li>Implement and optimize deep learning models in Rust, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs.</li><li>Develop practical deep learning applications to solve real-world problems, including natural language processing, computer vision, and speech recognition.</li><li>Explore Rust’s safety features, including its strict type of system and ownership model, and learn strategies to create reliable and secure AI software.</li><li>Gain an understanding of the broader ecosystem of tools and libraries available for deep learning in Rust.</li></ul>Who This Book Is forA broad audience with varying levels of experience and knowledge, including advanced programmers with a solid foundation in Rust or other programming languages (Python, C++, and Java) who are interested in learning how Rust can be used for deep learning apps. It may also be suitable for data scientists and AI practitioners who are looking to understand how Rust can enhance the performance and safety of deep learning models, even if they are new to the Rust programming language.

Mastering Efficient and Safe Neural Networks in the Rust Ecosystem

Deep Learning with Rust

Beiträge zu Grundfragen des Rechts

Die Beiträge dieses Bandes behandeln das breite Spektrum historischer und aktueller Problemstellungen eines interessengerechten Urheberrechts. Sie reichen von der Auslegung mittelalterlicher Maqame, der Aufarbeitung des NS-Urheberrechts im Zusammenhang mit der Reichskulturkammer bis hin zu verlagsrechtlichen Konflikten um Richard Strauss' Krämerspiegel. Thematisiert werden Nachdruckverfahren und Plagiatsvorwürfe des 19., 20. und 21. Jahrhunderts sowie urheberrechtliche Fragestellungen zu Softwarepatenten und KI. Darüber hinaus stellt der Band kulturrechtliche Debatten zur Restitution von Kulturgütern kolonialer Herkunft und NS-Raubkunst dar und leistet damit einen Beitrag zur Provenienzforschung. Aktuelle schutzrechtliche Grenzfragen, etwa zu digital fotografierten Bildern und zum Freihaltebedürfnis im Design- und Markenrecht, runden den Band ab.

The contributions in this volume deal with the broad spectrum of historical and current problems of copyright law. They range from the interpretation of medieval maqamas and the examination of Nazi copyright law in connection with the Reich Chamber of Culture to publishing law disputes over Richard Strauss's Krämerspiegel. The volume addresses reprinting practices and allegations of plagiarism in the 19th, 20th, and 21st centuries, as well as copyright issues related to software patents and AI. Furthermore, the volume presents debates in cultural law regarding the restitution of cultural property of colonial origin and Nazi-looted art, thereby contributing to provenance research. Current boundary issues in intellectual property law-such as those concerning digitally photographed images and the need for design and trademark law to remain open-round out the volume.

Geschichte und Zukunft des Urheberrechts V

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.
The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, Agentic AI for Engineers equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. 
What You Will Learn

<li>A practical introduction to Machine Learning and Generative AI, tailored for engineers

</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.

</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops

</li><li>Understand different Agentic AI frameworks and their applications.

</li><li>Integrate agentic systems into real-world applications using hands-on coding examples

</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments




Who This Book Is For
Primary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. 
AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.
 


</li>

As AI rapidly evolves from passive models to autonomous systems capable of setting goals, reasoning, and acting independently, engineers find themselves at the threshold of a new technological era. This book serves as a bridge—connecting the world of traditional engineering to the emerging domain of Agentic AI. It is crafted for hands-on professionals who may not have formal training in AI but are eager to build the next generation of intelligent, goal-driven systems.The journey begins with foundational concepts: what it truly means for a system to exhibit agency, how autonomy differs from automation, and why this distinction matters in practice. Early chapters lay down the necessary groundwork in machine learning and generative AI, allowing readers to appreciate the architecture that enables agentic behavior. From there, the book dives into system design patterns, prompting strategies, and the most influential tools shaping the agentic AI landscape—from LangChain to CrewAI. Practical guidance is provided on engineering agents that are not only capable but also aligned, safe, and robust in dynamic environments. The third chapter shifts into applied engineering: readers are walked step-by-step through building their first AI agent, supported by real-world examples, feedback loop design, and deployment practices that mirror how modern autonomous systems are built.
 By the final chapter, readers will not only understand agentic systems—they will be ready to build, evaluate, and evolve them. The book closes by addressing the road ahead: open challenges in ethics, unpredictability, and system alignment, along with a roadmap for engineers who want to actively contribute to the field. Whether you're building automation today or preparing for the autonomy of tomorrow, 
 Agentic AI for Engineers
 equips you with the knowledge, tools, and mindset to lead in the era of intelligent agents. &nbsp;
 What You Will Learn<ul><li>A practical introduction to Machine Learning and Generative AI, tailored for engineers</li><li>Conceptualize, design, and build autonomous AI agents from scratch—even with a minimal AI background.</li><li>The core principles of Agentic AI, including goals, environments, actions, and feedback loops</li><li>Understand different Agentic AI frameworks and their applications.</li><li>Integrate agentic systems into real-world applications using hands-on coding examples</li><li>Review strategies for ensuring safe, ethical, and auditable agent behavior in production environments</li></ul>Who This Book Is ForPrimary audience includes Software Engineers, DevOps, and Data Engineers curious about building intelligent, autonomous systems but who lack formal AI training; Technical Product Managers and Engineering Leaders looking to understand and implement Agentic AI in real-world systems; System Architects and Automation Engineers exploring the shift from traditional automation to intelligent agent-based architectures. AI/ML Enthusiasts and self-learners, Engineering and computer science students, and professionals in emerging tech domains who want to build or deploy autonomous agents will also benefit from this book.&nbsp;&nbsp;

Architecting Goal-Driven Systems

Agentic AI for Engineers

Although the German Civil Code (BGB) has contained injunctive relief for the protection of property rights in § 1004 since its inception, numerous dogmatic problems still surround this provision. Patrick Zurth examines injunctive relief from a legal-historical, legal-economic, and legal-dogmatic perspective, developing a new understanding of the "disturber." This characteristic forms the central element of § 1004 German Civil Code and also appears outside this norm in civil law, particularly in the case of so-called disturber liability in intellectual property and personality rights. The aim of this work is to deduce a uniform concept of disturbance across various sub-areas of private law. In particular, the proximity of injunctive relief to tort law is highlighted and the concept of disturbance in private law is further developed by including duties of conduct.

Der Störer im Privatrecht

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 networks2)Gradient descent3)Classification4)Regularization5)Convolutional neural networks (CNNs)6)Functional API7)Transformer architecture8)Reinforcement learning9)Autoencoders10)Stable Diffusion

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

Keras 3

Spektrum edition

Ich erinnere mich noch, wie fasziniert ich war, als ich im Sommer 2022 erstmals davon las, welche Fähigkeiten Sprachmodelle angeblich haben: Kohärente Sätze könnten sie formulieren und Fragen aus unterschiedlichsten Themenfeldern beantworten. Es war noch vor der Veröffentlichung von ChatGPT. Um auf die Sprachmodelle zuzugreifen, musste man etwas Aufwand betreiben und die Schnittstellen von Techfirmen zu nutzen wissen. Auch wenn die Fortschritte damals beeindruckend klangen, war ich vorsichtig. Für mich klang vieles nach PR-Hype. Ich ging davon aus, dass ein großer Durchbruch noch weit in der Zukunft läge.
Und dann, nur wenige Monate später, kam ChatGPT heraus. Bald darauf berichtete mir eine Freundin, sie habe sich mit dem Chatbot gestritten - die KI hatte ihr das Ende einer Serie gespoilert, obwohl meine Freundin explizit gefordert hatte, das zu unterlassen. Nach ihrer wütenden Sprachnachricht bekam sie ein schlechtes Gewissen und entschuldigte sich bei der KI. Ich kann nur sagen: Die Welt hat sich in den letzten Jahren stark verändert.
Dieser Wandel macht sich in allen Lebensbereichen bemerkbar. Besonders faszinierend finde ich, wie die neuen Technologien die Wissenschaft beeinflussen. Einige Fachleute können deutlich effizienter arbeiten, unliebsame Aufgaben an die Maschine abtreten und sich dadurch auf ihre Kernkompetenzen konzentrieren. Die KI-Systeme können sogar noch mehr leisten als bloße Routinetätigkeiten und haben teilweise erstaunliche Fähigkeiten erlangt.
Das lässt einige mit der Frage zurück, welche Rolle der Mensch in Zukunft in der Wissenschaft spielen wird. Werden überlegene KIs Forschenden aus Fleisch und Blut künftig den Rang ablaufen?
So spannend und teilweise beängstigend die Szenarien von einer KI-getriebenen Forschungswelt sind, so unklar ist hier noch vieles. Unterdessen offenbaren die Technologien bereits einige handfeste Nachteile: Die Systeme sind extrem energiehungrig, grundlegend unzuverlässig und aus Sicht des Datenschutzes ein enormes Problem. Umso besorgniserregender ist es, dass sich viele Menschen und Unternehmen immer mehr von den KI-Programmen abhängig machen, die von einigen wenigen großen Techfirmen stammen.
Eines ist klar: Die Technologie ist gekommen, um zu bleiben. Aber wir müssen uns Gedanken machen, wie wir sie nutzen möchten - und wie weit wir sie in unseren beruflichen und privaten Alltag eindringen lassen. 
Manon Bischoff, Spektrum der Wissenschaft.

Spektrum edition Nr. 9 - Künstliche Intelligenz

In bewährter Tradition dokumentiert der 21. Band des Jahrbuchs 'Wirtschaftsrecht Schweiz - EU' die aktuellen Entwicklungen in zentralen Bereichen des EU-Wirtschaftsrechts und deren Bedeutung für die Schweiz. Berücksichtigt werden diverse wirtschaftsrelevante Rechtsgebiete, u.a. Banken- und Kapitalmarktrecht, Immaterialgüterrecht, Arbeitsrecht, Steuerrecht und Wettbewerbsrecht. Das Jahrbuch richtet sich an Unternehmens¿, Wirtschafts- und VerwaltungsjuristInnen sowie an RichterInnen und RechtsanwältInnen und bietet ihnen einen kompakten Überblick über die wichtigsten Gesetzgebungsvorstösse, neue Rechtsakte und ergangene Urteile im vergangenen Jahr 2025.

Überblick und Kommentar 2025/26

Jahrbuch Wirtschaftsrecht Schweiz - EU

Die Künstliche Intelligenz im Urheberrecht

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