Ergebnis für "Das System" in Informatik

As artificial intelligence and automated systems increasingly shape our courts, hospitals, workplaces, and financial institutions, a fundamental question emerges: Who remains accountable when machines decide? Ethics in Autonomous Systems offers a rigorous yet accessible exploration of moral responsibility in a world governed by algorithms. Rather than treating automation as neutral or inevitable, this book challenges listeners to examine the ethical architecture beneath every line of code and every data-driven decision.

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements. Author Chip Huyen, cofounder of Claypot AI, considers each design decision in the context of how it can help your system as a whole achieve its objectives.

Welcome to "Predictive Power: Navigating the Landscape of AI Systems and Decision-Making." In this book, we embark on a journey into the fascinating world of artificial intelligence (AI) systems and their profound impact on decision-making processes across various domains. From business and healthcare to finance and beyond, AI technologies are revolutionizing and transforming how we analyze data, predict outcomes, and make informed choices.

Die Arbeitswelt verändert sich schneller als je zuvor. Künstliche Intelligenz automatisiert Aufgaben, verändert Berufe und stellt alles infrage, was bisher als sicher galt. Viele Menschen spüren diese Veränderung bereits, wissen aber nicht, wie sie darauf reagieren sollen.

In February 2026, sixteen autonomous AI agents built a complete C compiler in just 14 days. They wrote 100,000 lines of production code, compiled the Linux kernel and PostgreSQL, and managed their own Git workflows without human intervention. This wasn't a research experiment. It was proof that AI agent swarms can deliver enterprise-grade software faster than traditional development teams.

Bridges the gap between AI and neuroscience by telling the story of how the brain came to be. 'I found this book amazing' Daniel Kahneman, Winner of the Nobel Prize in Economics and bestselling author of Thinking Fast & Slow The entirety of the human brain’s 4-billion-year story can be...

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations.

Vertrauen in Künstliche Intelligenz ist nicht nur eine regulatorische Anforderung, sondern auch eine Frage der Unternehmensethik, des kulturellen Bewusstseins und verantwortungsvollen Designs. Dieses Buch verfolgt einen integrierten Ansatz und zeigt, wie vertrauenswürdige KI-Systeme über Algorithmen, Server und Datenbanken hinausgehen und auch Aussehen, Natur und Menschen berücksichtigen.

Artificial intelligence is moving fast—from experimental prototypes to real systems powering software, automation, and decision-making. Yet most AI projects never reach production. Fragile data pipelines, unstable models, and poorly designed architectures often prevent promising ideas from becoming real-world solutions. At the same time, a new generation of intelligent systems is emerging.

You open your CRM and find three versions of the same client. Your file system has a folder called "Final FINAL v3." Your spreadsheet has columns that no one on your team can explain — including the person who made them. And somewhere in the back of your mind, a voice is saying: I should probably be using AI by now. Here is what that voice isn't telling you: so should everyone else. And almost no one's business is ready for it. Not because the technology is too advanced.

Most people are using ChatGPT the wrong way. They ask random questions, generate quick answers, and move on. But the real power of AI isn’t just getting answers—it’s building systems that work for you automatically. ChatGPT Automation for Beginners shows you how to turn ChatGPT into a practical automation engine that can help you build sales systems, marketing workflows, and business processes—even if you have no technical background.

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete...

NEW YORK TIMES BESTSELLER An urgent warning of the unprecedented risks that AI and other fast-developing technologies pose to global order, and how we might contain them while we have the chance—from a co-founder of the pioneering artificial intelligence company DeepMind and current CEO of...

Your AI assistant loses all context the moment you close the chat. Your project history, codebase knowledge, and business data? Gone. You're stuck manually rebuilding context in every conversation, turning powerful AI into an expensive notepad. Model Context Protocol (MCP) changes everything. Building Memory for AI Systems with MCP shows developers, technical founders, and product builders how to integrate Claude AI with persistent memory systems to maintain context across sessions, access real data, and learn from experience over time.

When we avoid taking a decision, what happens to it? In The Unaccountability Machine, Dan Davies examines why markets, institutions and even governments systematically generate outcomes that everyone involved claims not to want. He casts new light on the writing of Stafford Beer, a legendary economist who argued in the 1950s that we should regard organisations as artificial intelligences, capable of taking decisions that are distinct from the intentions of their members.

The Complete Beginner's Guide to Building Autonomous Systems with LLMs and Agentic Agents is your step-by-step roadmap into the future of intelligent automation. Designed specifically for beginners, this guide breaks down complex AI concepts into clear, practical lessons—no advanced background required. Inside, you'll discover how Large Language Models (LLMs) work, how agentic systems make decisions, and how to connect tools, memory, and workflows into fully autonomous AI pipelines.

Unlock the secrets of AI-powered income with AI Income Generator—the essential guide for anyone ready to turn artificial intelligence into real, sustainable revenue. Whether you’re a total beginner or a side hustle veteran, this audiobook walks you step-by-step through launching profitable online businesses using today’s most accessible AI tools—no coding required. Discover how to use ChatGPT, Canva, and automation platforms to create digital products, land freelance gigs, and build passive income streams that work while you sleep.

Autonomous AI isn’t the future — it’s already changing how software, work, and decision-making happen. From intelligent assistants to fully autonomous systems, agentic AI is rapidly transforming industries. Yet most professionals are still trying to understand one key challenge: how do you design AI agents that can actually operate independently, reliably, and at scale?The Agentic AI Bible provides a clear and practical guide to the new generation of intelligent systems.

Trust in Artificial Intelligence is not just a regulatory requirement—it is a matter of business ethics, cultural awareness, and responsible design. This book takes an integrated approach, revealing how Trustworthy AI Systems extend beyond algorithms, servers, and databases to also include appearance, environment, and humans.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.