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This open access textbook introduces and defines digital humanism from a diverse range of disciplines. Following the 2019 Vienna Manifesto, the book calls for a digital humanism that describes, analyzes, and, most importantly, influences the complex interplay of technology and humankind, for a better society and life, fully respecting universal human rights. The book is organized in three parts: Part I “Background” provides the multidisciplinary background needed to understand digital humanism in its philosophical, cultural, technological, historical, social, and economic dimensions. The goal is to present the necessary knowledge upon which an effective interdisciplinary discourse on digital humanism can be founded. Part II “Digital Humanism – a System's View” focuses on an in-depth presentation and discussion of the main digital humanism concerns arising in current digital systems. The goal of this part is to make readers aware and sensitive to these issues, including e.g. thecontrol and autonomy of AI systems, privacy and security, and the role of governance. Part III “Critical and Societal Issues of Digital Systems” delves into critical societal issues raised by advances of digital technologies. While the public debate in the past has often focused on them separately, especially when they became visible through sensational events the aim here is to shed light on the entire landscape and show their interconnected relationships. This includes issues such as AI and ethics, fairness and bias, privacy and surveillance, platform power and democracy. This textbook is intended for students, teachers, and policy makers interested in digital humanism. It is designed for stand-alone and for complementary courses in computer science, or curricula in science, engineering, humanities and social sciences. Each chapter includes questions for students and an annotated reading list to dive deeper into the associated chapter material. The book aims to provide readers with as wide an exposure as possible to digital advances and their consequences for humanity. It includes constructive ideas and approaches that seek to ensure that our collective digital future is determined through human agency.

The term'4th Industrial Revolution'is often mentioned in the media, but public understanding of its technologies lags behind their rapid development. This book bridges the gap, explaining essential technologies like IoT, blockchain, AI, cloud computing, and big data. It aims to enhance comprehension by minimizing technical content. The book introduces key technologies and their applications, emphasizing their importance in contemporary business models. Readers will find accessible descriptions and practical examples to aid understanding. Covering topics and trends vital for modern business, this book ensures readers grasp the technological landscape shaping the future. Throughout the book, you'll explore how these technologies are revolutionizing industries and their integration into business strategies. This journey provides a comprehensive understanding of the 4th Industrial Revolution, equipping readers with the knowledge needed to navigate and leverage these advancements effectively.

This book offers an in-depth exploration of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Beginning with foundational concepts like the attention mechanism and tokenization techniques, it delves into the intricacies of Transformer and BERT architectures. Advanced topics cover the latest developments in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the nuances of Transformer architecture, a detailed exploration of the BERT family, and hands-on guidance on working with GPT-3. The journey continues with a comprehensive overview of ChatGPT, GPT-4, and visualization using generative AI. The book also discusses influential AI organizations such as DeepMind, OpenAI, Cohere, and Hugging Face. Readers will gain a thorough understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Companion files with numerous code samples and figures from the book enhance the learning experience, providing practical tools and resources. This book is an essential guide for those seeking to master the latest advancements in natural language processing and generative AI.

Take your data preparation, machine learning, and GenAI skills to the next level by learning a range of Python algorithms and tools for data labelingKey FeaturesGenerate labels for regression in scenarios with limited training dataApply generative AI and large language models (LLMs) to explore and label text dataLeverage Python libraries for image, video, and audio data analysis and data labelingPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData labeling is the invisible hand that guides the power of artificial intelligence and machine learning. In today's data-driven world, mastering data labeling is not just an advantage, it's a necessity. Data Labeling in Machine Learning with Python empowers you to unearth value from raw data, create intelligent systems, and influence the course of technological evolution. With this book, you'll discover the art of employing summary statistics, weak supervision, programmatic rules, and heuristics to assign labels to unlabeled training data programmatically. As you progress, you'll be able to enhance your datasets by mastering the intricacies of semi-supervised learning and data augmentation. Venturing further into the data landscape, you'll immerse yourself in the annotation of image, video, and audio data, harnessing the power of Python libraries such as seaborn, matplotlib, cv2, librosa, openai, and langchain. With hands-on guidance and practical examples, you'll gain proficiency in annotating diverse data types effectively. By the end of this book, you'll have the practical expertise to programmatically label diverse data types and enhance datasets, unlocking the full potential of your data.What you will learnExcel in exploratory data analysis (EDA) for tabular, text, audio, video, and image dataUnderstand how to use Python libraries to apply rules to label raw dataDiscover data augmentation techniques for adding classification labelsLeverage K-means clustering to classify unsupervised dataExplore how hybrid supervised learning is applied to add labels for classificationMaster text data classification with generative AIDetect objects and classify images with OpenCV and YOLOUncover a range of techniques and resources for data annotationWho this book is forThis book is for machine learning engineers, data scientists, and data engineers who want to learn data labeling methods and algorithms for model training. Data enthusiasts and Python developers will be able to use this book to learn data exploration and annotation using Python libraries. Basic Python knowledge is beneficial but not necessary to get started.

تعتبر أداة قوية يمكن استخدامها في مجال مكافحة الإرهاب والحد من التهديدات الأمنية المرتبطة به. يمتلك الذكاء الاصطناعي القدرة على تحليل كميات ضخمة من البيانات واستخلاص أنماط ومعلومات هامة، مما يمكنه من تحديد الأنشطة المشبوهة والتنبؤ بالهجمات المحتملة. تتضمن استخدامات الذكاء الاصطناعي في مجال مكافحة الإرهاب ما يلي: تحليل البيانات المتنوعة من مصادر مختلفة، مثل وسائل التواصل الاجتماعي والتسجيلات الصوتية، لاكتشاف الأنشطة الإرهابية المحتملة. يمكن أن يستخدم الذكاء الاصطناعي تقنيات تعلم الآلة وتعلم العمق للكشف عن أنماط غير عادية أو تغييرات مشتبه فيها في السلوك الإرهابي. يمكن للذكاء الاصطناعي استخدام نماذج تنبؤية لتحليل البيانات التاريخية والمعلومات الجغرافية والاستخباراتية لتحديد المناطق أو الأهداف المحتملة للهجمات الإرهابية

Artificial Intelligence (AI) plays a key role in crime and prevention, armed conflicts, and in the criminal and civil justice system development. This edited volume provides a cross-disciplinary approach, with contributions from legal and IT specialists, on a range of issues relating to uses, restrictions, and regulation of AI in law enforcement, warfare, and justice. Through a series of examples, international experts discuss the effect of AI on preventing crimes, and in the development of weapons and military strategies. This work also explains how AI can benefit the enforcement and justice system and the creation of a fairest judicial system that respects human rights without bias. Contributions include: The use of Artificial Intelligence (AI) by criminals; Law enforcement trade-offs between Privacy and Safety; The use of Unmanned Vehicles; The first AI war; Regulation of Autonomous Weapon Systems in warfare; Automated Justice; AI and Discrimination; and the use of AI in Judicial Appeals.

L'intelligence artificielle derrière nos écrans est devenue omniprésente. Ses capacités progressent à un rythme exponentiel dans des domaines que l'on croyait autrefois réservés aux humains, comme l'art, le langage, les relations interpersonnelles ou la science. Les innombrables IA, telles que ChatGPT, DALL-E 2 ou Midjourney, qui sont accessibles au grand public, mettent en lumière les défis d'une cohabitation incessante avec des algorithmes. En sortons-nous indemnes? L'homme, qui rêvait de se voir « augmenté » grâce à la technique et de s'affranchir des limites liées aux contraintes biologiques, découvre qu'il risque en réalité d'être assujetti à la condition de cybcog (cybernetic cognition), un être dépendant et piloté par l'IA. Les études sont unanimes : notre exposition constante aux IA diminue nos capacités à créer, à apprendre et à penser, altère nos liens sociaux et notre autonomie. Cet essai, qui tient compte des innovations et des recherches les plus récentes, nous informe sur les potentialités extraordinaires comme sur les dangers de l'IA, et plaide pour un encadrement strict de ses usages.

تبرز أهمية الموضوع في قصور قواعد المسئولية الجنائية التقليدية على مواجهة جرائم تقنيات الذكاء الاصطناعي وبالتالي لا يمكن معاقبة الجاني ، وذلك يرجع إلى تعدد الأشخاص المساهمين في انتاج أجهزة وبرمجيات الذكاء الاصطناعي ومن ثم صعوبة معرفة الشخص المسئول عن وقوع الجريمة وبالتالي عدم إمكانية إسناد المسئولية الجنائية إليه. ويتعبر أحد التحديات المهمة لأنظمة الذكاء الاصطناعي هو الوضع القانوني لتلك الأنظمة، إذ مع تطوير نظم الذكاء الاصطناعي وانتشار تطبيقاتها في المجالات الطبية والمالية والعسكرية والقانونية ووسائل الانتقالات ، ومع قدرة هذه الأنظمة على اتخاذ قرارات مصيرية غير قانونية أو ارتكاب أفعال تشكل جريمة ، ظهرت الحاجة إلى ضرورة وجود أطر قانونية جديدة لتحديد المسئولية القانونية عن الأفعال غير القانونية التي ترتكبها أنظمة الذكاء الاصطناعي. وبمعنى أخر أما أن يتواجد شخص ما لكي يتحمل المسئولية أو قد تحتاج أنظمة أو برامج الروبوتات الأخرى إلى شخصية قانونية حتى يمكن لتلك الانظمة الدخول في العقود أو يتم مقاضاتها واسناد المسئولية الجنائية إلى الشخص الطبيعي المسئول عنها. وصعوبة إسناد المسئولية الجنائية إلي الجاني ليس تقصيراً من المشرع فقواعد المسئولية الجنائية التقليدية وضعت في عصر كانت فيه تقنية الذكاء الاصطناعي مجرد خيال علمي ، ومن ثم فإن الأمر الذي يتطلب تدخل المشرع لوضع نظام قانوني يحدد بشكل دقيق كيفية التعامل مع هذه التكنولوجيا الجديدة ومواجهة الجرائم التي تنشأ عنها.

The Handbook on Socially Interactive Agents provides a comprehensive overview of the research fields of Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics. Socially Interactive Agents (SIAs), whether virtually or physically embodied, are autonomous agents that are able to perceive an environment including people or other agents, reason, decide how to interact, and express attitudes such as emotions, engagement, or empathy. They are capable of interacting with people and one another in a socially intelligent manner using multimodal communicative behaviors, with the goal to support humans in various domains.Written by international experts in their respective fields, the book summarizes research in the many important research communities pertinent for SIAs, while discussing current challenges and future directions. The handbook provides easy access to modeling and studying SIAs for researchers and students, and aims at further bridging the gap between the research communities involved.In two volumes, the book clearly structures the vast body of research. The first volume starts by introducing what is involved in SIAs research, in particular research methodologies and ethical implications of developing SIAs. It further examines research on appearance and behavior, focusing on multimodality. Finally, social cognition for SIAs is investigated using different theoretical models and phenomena such as theory of mind or pro-sociality. The second volume starts with perspectives on interaction, examined from different angles such as interaction in social space, group interaction, or long-term interaction. It also includes an extensive overview summarizing research and systems of human–agent platforms and of some of the major application areas of SIAs such as education, aging support, autism, and games.

Thelong-anticipated revision of ArtificialIntelligence: A Modern Approach explores the full breadth and depth of the field of artificialintelligence (AI). The 4th Edition brings readers up to date on the latest technologies,presents concepts in a more unified manner, and offers new or expanded coverageof machine learning, deep learning, transfer learning, multi agent systems,robotics, natural language processing, causality, probabilistic programming,privacy, fairness, and safe AI.

Bruk av kunstig intelligens og big data i helsesektoren og i medisinen må kunne kalles et paradigmeskifte. Den nye teknologien kan for eksempel føre til at truende epidemier eller pandemier blir oppdaget langt tidligere, eller at riktig diagnose kan stilles raskere, slik at pasienter kan få mer presis og effektiv medisinsk behandling. Samtidig kan den ha uheldige konsekvenser - pasienter kan bli utsatt for skader eller krenkelser eller miste kontroll over spredningen av sine personopplysninger. Innenfor helseretten oppstår spørsmål om hvordan denne teknologien skal kategoriseres og vurderes ut fra gjeldende rett, og om dagens rettstilstand er tilpasset den nye teknologien. Hvis ikke, hvilke endringer bør gjøres for at kunstig intelligens og big data kan bli bedre regulert?

Ci troviamo davanti a una transizione storica senza precedenti: per la prima volta nella storia del pianeta, un animale intelligente è in procinto di creare a sua volta un'intelligenza di tipo artificiale e connettersi a essa. Connettendo cervelli e macchine intelligenti potremo espandere i confini cognitivi della nostra specie e interrompere il monopolio biologico sull'intelligenza, dando così il via a un'era dell'Intelligenza2 (naturale + artificiale). Questo libro ripercorre in pochi passi la storia dell'intelligenza sul pianeta Terra dai primi microrganismi fino ai più recenti progressi dell'intelligenza artificiale e della neuro-ingegneria. Allo stesso tempo, propone un'analisi dettagliata delle profonde trasformazioni sociali causate dalla chiusura dell'interfaccia tra uomo e macchina, i cui complessi profili etici e giuridici sono già materia di dibattito per la politica internazionale. Un vademecum indispensabile nell'era dell'Intelligenza2.

The Subject itself is the Subject of the Machine. What does it mean to be human? We live in a technological age, where rapid advances in personal tech and the science of Artificial Intelligence are challenging us in ways never before imagined. A book in two parts, The Universal Subject of Our Time begins with an exploration of 20th Century post-modernism's undermining of subjectivity with thinkers such as Foucault, Derrida, Baudrillard and Althusser and continues with a description of the science wars, where physical realists challenged the post-modernists up to the 1990s when the intellectual conflict resulted in an uncompromising stand-off after the Sokal Hoax. In Part II the subject is resurrected by taking a look at arguments for machine intelligence and AI and also, from the perspective of physics, examines what subjectivity means, particularly in relation to black holes or black stars, and look to what lies ahead in the future, in terms of space exploration, Martian habitats and even the possibility of first contact with extra-terrestrials.

Alle snakker om kunstig intelligens, eller artificial intelligence (AI), men veldig få snakker om hva som skjer - og hvem som har ansvaret - når et AI-prosjekt ikke går som planlagt. Hvem må for eksempel rydde opp dersom en selvlærende AI utfører handlinger som ingen tenkte på da leverandøren trente programmet og kunden testet resultatet? Når juss møter AI gir en innføring i hva kunstig intelligens er, og leseren får praktiske råd for innkjøp av et IT-system basert på selvlærende nevrale nettverk. Forfatterne behandler dessuten de juridiske konsekvensene av å sette slike systemer i drift og hvordan utviklingsrisiko bør fordeles. Når juss møter AI er aktuell for deg som vurderer å ta AI i bruk i din virksomhet, og for deg som selger ulike former for AI. Boken egner seg også for jurister med interesse for hvordan tradisjonelle kontraktsrettslige prinsipper skal brukes på helt nye områder.

Boken tar for seg hvordan kunstig intelligens og robotisering av juridiske oppgaver påvirker oss i dag og trolig i fremtiden. Manuset til denne boken ble funnet på Jon Bings PC etter hans død. Den oppsummerer mange av Jons interesser fra jus og teknologi, fortid og framtid. Boken beskriver hvordan historien har påvirket oss og teknologien vi omgir oss med og hva som er forløperen til dagens diskusjoner om kunstig intelligens og robotisering av juridiske oppgaver. Jon drar linjene helt fra de sumeriske lovskriftene i kileskrift 2000 år f. Kr. via utvikling av opphavsrett og personvern i det 20. århundre mot science fiction og betydningen av kunnskap i internettalderen. Gjennom fortellinger og Jons personlige anekdoter gir boken et rikere perspektiv på mye av det vi anser for gitt, men hvis historie de færreste har et bevisst forhold til. «Jon Bing var veldig tidlig ute med ting som folk ikke snakket om, som for eksempel IT. Han snakket mye om hvor viktig IT kom til å bli i samfunnet, og hvordan det kom til å påvirke alle, ikke bare næringslivet. Han var blant annet en av de første som snakket om E-bøker. Men han var et utrolig mangfoldig menneske» Eirik Newth

'Vast amounts of data present amajor challenge to all thoseworking in computer science, and its many related fields, who need to process and extract value from such data. Machine learning technology is already used to help with this task in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis and robot locomotion. As its usage becomes more widespread, no student should be without the skills taught in this book. Designed for final-year undergraduate and graduate students, this gentle introduction is ideally suited to readers without a solid background in linear algebra and calculus. It covers everything from basic reasoning to advanced techniques in machine learning, and rucially enables students to construct their own models for real-world problems by teaching them what lies behind the methods. Numerous examples and exercises are included in the text. Comprehensive resources for students and instructors are available online'--

Description based on print version record.

Description based on print version record.