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Gesamtkatalog

Catalogue complet

Synthesis Lectures on Games and Computational Intelligence

Guzdial M: Procedural Content Generation
via Machine Learning

Procedural Content Generation via Machine Learning

An Overview

Distributed and Parallel Computing

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

A comprehensive, up-to-date introduction to active inference and the free energy principle for an engineering-focused audience. Active inference, which uses machine learning to model brain function and behavior, emerged from decades of cross-disciplinary research in computational neuroscience, resulting in a vast literature but no unifying treatment. Filling this gap, Sanjeev Namjoshi provides comprehensive coverage of the foundational material needed to understand and navigate this fast-moving field from first principles. Using a simple, conversational style free of proofs, lemmas, and theorems, Namjoshi brings together theory and technical material in one self-contained text. The book begins with an explanation of the general statistical framework used in active inference models that describes the relationship between artificial agents and their environments. It then introduces fundamental concepts in machine learning and statistics and connects them to the active inference perspective. Featuring worked examples, simulations, and detailed walkthroughs of concepts, this user-friendly text aims to expand the readership of active inference to an engineering-focused audience.&nbsp; <ul><li>Provides a one-stop-shop for understanding the foundations and applications of active inference and the free energy principle </li><li>Makes a complex, interdisciplinary subject accessible to students and professionals beyond the neurosciences</li><li>Covers discrete and continuous state-space formulations of active inference as well as state-of-the-art extensions to base active inference methods</li><li>Features extensive appendices and supplemental resources</li></ul>

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Discover how to use AI confidently, responsibly and ethically as an HR practitioner with this practical jargon-free guide.

Understand How to Use Artificial Intelligence in Your HR Role

AI for People Professionals

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Generative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;&nbsp;

This book is a comprehensive guide that immerses you into the world of building and deploying AI-powered solutions. You are introduced to the core architecture of LLMs and equipped with essential Best Data Practices (BDPs) for utilizing Generative AI responsibly, ensuring ethical and efficient AI deployment.The book starts with the foundational aspects of Gehatnerative AI application development. You will learn the nuances of data handling in Generative AI apps, from working with embeddings to managing unstructured and structured data, and handling Personally Identifiable Information (PII) data. The exploration extends to understanding the differences between deterministic and LLM-based data synthesis and integrating Generative AI apps with enterprise data, providing you with practical insights into leveraging data effectively for intelligent applications. A chapter on prompt engineering explains the importance of prompts in AI interactions, covering a spectrum of techniques and pitfalls while offering exercises to enhance prompt engineering skills. As you progress through the book, you take a journey from conceptualization to production and deployment of Generative AI applications. You discover the essentials of Generative AI application development, gain insights into the pathway from ideation to production, and explore the intricacies of LLM selection and fine-tuning.The book equips you with the knowledge and tools necessary to navigate the complex terrain of AI development and deployment, making it an indispensable resource for AI enthusiasts, developers, and business leaders alike.&nbsp;What You Will Learn<ul><li>Know the core architecture of LLMs and how these models have revolutionized AI applications</li><li>Handle various data types, including unstructured, structured, and personally identifiable information (PII) data in Generative AI applications</li><li>Understand prompt engineering and its importance in AI interactions and applications</li><li>Understand modular design of Generative AI apps, essential backend and frontend components, and the unique principles guiding Generative AI app design</li></ul>&nbsp;Who This Book Is ForFrom AI enthusiasts exploring the field to software developers seeking practical insights, and business leaders looking to harness the power of AI for organizational growth and innovation&nbsp;&nbsp;

Building and Deploying Ethical AI-Powered Solutions

Practical Generative AI: From Concept to Deployment

Radiologie und Betriebswirtschaft – Steuern, Optimieren, TransformierenAlle&nbsp;betriebswirtschaftlich&nbsp;relevanten Aspekte der Radiologie werden in diesem Buch anschaulich dargestellt.Der Radiologe übernimmt im Krankenhaus und in der Niederlassung immer mehr die Rolle eines Prozessmanagers. Im Krankenhaus und in Praxisverbünden wird er dabei von betriebswirtschaftlichen Experten aus dem Controlling unterstützt. Das Verständnis von radiologischen Prozessen und das Wissen um moderne Methoden des Controllings ist dabei für beide Berufsgruppen besonders wichtig. 

Das Buch beschäftigt sich mit den Akteuren und Schnittstellen der Radiologie, der Strategieplanung und dem Einsatz der technischen Geräte aus betriebswirtschaftlicher Perspektive. Themen sind: <ul><li>Kennzahlen für die Prozesseffizienz der Radiologie im Krankenhaus und der Niederlassung,</li><li>das Leistungsgeschehen, </li><li>Personalbedarfsrechnung und Prozesskostenrechnungen, </li><li>das Abrechnungsmanagement. </li></ul>Über das Prozess- und Qualitätsmanagement führt das Buch mit modernen Methoden (z.B. Balanced Scorecard, Service Blueprinting) hin zu ausführlich dargelegten und aktuell hochrelevanten Themen im Bereich des digitalen Dosismanagements der Digitalisierung von Controlling Prozessen und Aufgaben der medizinischen Leistungserbringung.Das Buch spricht die Berufsgruppen leitender Radiologen im Krankenhaus genauso an wie niedergelassene Radiologen und Krankenhausmanager und -administratoren im Bereich der Verwaltung sowie Experten im Bereich des Controllings.

Alle&nbsp;betriebswirtschaftlich&nbsp;relevanten Aspekte der Radiologie werden in diesem Buch anschaulich dargestellt.Der Radiologe übernimmt im Krankenhaus und in der Niederlassung immer mehr die Rolle eines Prozessmanagers. Im Krankenhaus und in Praxisverbünden wird er dabei von betriebswirtschaftlichen Experten aus dem Controlling unterstützt. Das Verständnis von radiologischen Prozessen und das Wissen um moderne Methoden des Controllings ist dabei für beide Berufsgruppen besonders wichtig. Das Buch beschäftigt sich mit den Akteuren und Schnittstellen der Radiologie, der Strategieplanung und dem Einsatz der technischen Geräte aus betriebswirtschaftlicher Perspektive.&nbsp;Themen sind: <ul><li>Kennzahlen für die Prozesseffizienz der Radiologie im Krankenhaus und der Niederlassung,</li><li>das Leistungsgeschehen, </li><li>Personalbedarfsrechnung und Prozesskostenrechnungen, </li><li>das Abrechnungsmanagement. </li></ul>Über das Prozess- und Qualitätsmanagement führt das Buch mit modernen Methoden (z.B. Balanced Scorecard, Service Blueprinting) hin zu ausführlich dargelegten und aktuell hochrelevanten Themen im Bereich des digitalen Dosismanagements der Digitalisierung von Controlling Prozessen und Aufgaben der medizinischen Leistungserbringung.Das Buch&nbsp;spricht&nbsp;die Berufsgruppen&nbsp;leitender Radiologen im Krankenhaus genauso an wie niedergelassene Radiologen und Krankenhausmanager und -administratoren im Bereich der Verwaltung sowie Experten im Bereich des Controllings.

Controlling, Qualitätsmanagement, Digitalisierung

Management in der Radiologie

An expert compilation of on-device training techniques, regulatory frameworks, and ethical considerations of TinyML design and development In Tiny Machine Learning: Design Principles and Applications, a team of distinguished researchers delivers a comprehensive discussion of the critical concepts, design principles, applications, and relevant issues in Tiny Machine Learning (TinyML). Expert contributors introduce a new low power resource, offering vast applications in IoT devices with system-algorithm co-design. Tiny Machine Learning explores TinyML paradigms and enablers, TinyML for anomaly detection, and the learning panorama under TinyML. Readers will find explanations of TinyML devices and tools, power consumption and memory in IoT microcontrollers, and lightweight frameworks for TinyML. The book also describes TinyML techniques for real-time and environmental applications. Additional topics covered in the book include: <ul> <li>A thorough introduction to security and privacy techniques for TinyML devices, including the implementation of novel security schemes</li> <li>Incisive explorations of power consumption and memory in IoT MCUs, including ultralow-power smart IoT devices with embedded TinyML</li> <li>Practical discussions of TinyML research targeting microcontrollers for data extraction and synthesis</li> </ul> Perfect for industry and academic researchers, scientists, and engineers, Tiny Machine Learning will also benefit lecturers and graduate students interested in machine learning.

Tiny Machine Learning: Design Principles and Applications

Medical Robots and Devices: New Developments and Advances

AI and Data Science in Medical Research focuses on the integration of AI and data science into medical research, highlighting their impact on drug discovery, medical imaging, diagnostics, and genomic medicine. The book addresses the acceleration of therapeutic compound discovery and optimization of drug development pipelines through AI. The volume also discusses advancements in medical imaging, including early disease detection and neuroimaging. Additionally, it covers the application of AI in genomic medicine, offering insights into personalized treatment strategies. The volume concludes with an examination of AI's role in public health surveillance, particularly in disease detection and epidemiological research.

AI and Data Science in Medical Research

Understand how to embrace and embed artificial intelligence into your public relations and communications practice and strategy with this how-to guide.

A How-To Guide for Implementation and Management

AI for Public Relations

Depending on who you ask, artificial intelligence is our salvation or our doom; an overhyped, bigoted artist or the ticket to immortality and utopian abundance; just another tech fad or the last invention we need ever make. The AI discourse is confusing, messy, and frustrating. But the technology — and our response to it — will shape the future, whether or not we’re part of the conversation.
Obsolete is&nbsp;for those who are interested in learning more about AI, but unsure of where to start and who to believe. They may feel intimidated by the technical jargon, put off by dry and abstract prose, or skeptical of the loudest voices on the issue (like Elon Musk and Sam Altman). Obsolete will introduce readers to the basics of AI, the idea that it could lead to human extinction, the roiling three-sided debate surrounding extinction fears, and the people and companies trying to build artificial general intelligence (AGI) — that which can outwit humans across the board. The book will grapple with the core arguments animating the AI debates, which are, by turns, uncritically parroted and unduly dismissed. It will also cut through industry hype while seriously entertaining the implications of AGI.
The risk that AGI could result in our extinction is being recognized by a significant and growing number of leading AI researchers, industrialists, and policymakers, along with the wider public. Existential risk from AI has been explored in other books, but Obsolete will be the first to center its analysis on how both capitalist and great power competition make AI more dangerous.
Obsolete will also tackle questions like: Can machines actually outsmart humanity? If so, when could that happen? If AGI is possible, is it inevitable? Why are people trying to build a technology they claim could end the world? Is the idea of AI-driven extinction the product of a big tech conspiracy aiming to hype the technology and control its regulation? Why has the left mostly ignored or dismissed existential risk from AI? Why do some powerful techies welcome human extinction? How could AI enable stable authoritarian regimes? How could killer robots reshape war and the balance of power? What do China and the US want from AI? And why has it become the front line of their brewing Cold War? And finally: What can and should the left do about it?

Power, Profit, And The Race For Machine Superintelligence

Obsolete

Wendepunkte in der Analogen und Digitalen Welt

Procedural Content Generation via Machine Learning

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