1.	The Evolution of Generative AI	2.	Key Technologies Powering Generative AI	3.	Ethical Challenges in AI Development	4.	AI’s Impact on Creativity and Innovation	5.	The Role of Data in Training AI Models	6.	Real-World Applications of Generative AI	7.	The Future of AI and Human Collaboration	8.	Limitations of Generative AI Systems	9.	The Importance of Understanding AI for the Future Workforce

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Generative AI and humans will work side by side, complementing each other’s strengths. While AI takes over repetitive tasks, humans will focus on strategy, creativity, and ethical oversight. 	“The future of work will be a partnership between humans and AI.”

The Future of AI & Human Collaboration

Generative AI systems rely on neural networks, machine learning, and deep learning technologies to create content. These core technologies enable AI to mimic human creativity and learning. 	“Deep learning forms the foundation of how generative AI creates and innovates.”

Key Technologies Powering Generative AI

With power comes responsibility, and generative AI presents significant ethical concerns, from data privacy to misinformation. Developers must carefully navigate these issues to build trust. 	“The ethics of AI will determine whether it is embraced or feared.”

Ethical Challenges in AI Development

Generative AI has come a long way from early algorithms to powerful models like GPT-3. The history of AI development reflects the rapid growth and increasing complexity of these systems. 	“Generative AI today is an amalgamation of decades of technological advancement.”

The Evolution of Generative AI

As AI continues to evolve, it’s crucial for the workforce to develop a strong understanding of these systems. Learning to work with and leverage AI will be essential for future success. 	“In a world shaped by AI, those who understand it will lead.”

The Importance of Understanding AI for the Future Workspace

Data is the fuel that powers AI models. Generative AI requires vast amounts of high-quality data to learn and generate accurate outputs, raising questions about data sourcing and integrity. 	“The quality of AI’s output is only as good as the data it’s trained on.”

The Role of Data in Training AI Models

1.	AI Evolution: Generative AI is rapidly advancing.	2.	Core Technologies: Neural networks and deep learning drive AI.	3.	Ethical Considerations: AI presents new ethical dilemmas.	4.	Creativity Enhancement: AI boosts creative industries.	5.	Data-Driven: High-quality data is essential for AI.﻿6.	Real-World Use: AI’s applications span many industries.	7.	Collaboration: AI and humans will work together.	8.	AI Limitations: AI still has significant shortcomings.	9.	AI Literacy: Understanding AI is vital for future careers.

Despite its power, generative AI has limitations, including biases in data, errors in output, and the inability to understand context in the same way humans do. 	“AI is brilliant at replication, but it lacks the nuanced understanding that defines human intelligence.”

Limitations of Generative AI Systems

Generative AI is transforming industries by automating creative processes. From art to design and content creation, AI helps humans push creative boundaries. 	“AI doesn’t replace creativity—it enhances and expands human imagination.”

AI’s Impact on Creativity & Innovation

From generating art to writing code, generative AI is already being used in various fields. Its application in healthcare, entertainment, and business is shaping the future of industries. 	“The practical applications of generative AI are as diverse as the problems it can solve.”

Real-world Applications of Generative AI

Explore how generative AI transforms creativity, ethics, and innovation in the modern world!

The last book on AI you’ll ever need. We swear! AI technology moves so fast that this book is probably already out of date! But don’t worry—The Complete Obsolete Guide to Generative AI is still an essential read for anyone who wants to make generative AI into a tool rather than a toy. It shows you how to get the best out of AI no matter what changes come in the future. You’ll be able to use common automation and scripting tools to take AI to a new level, and access raw (and powerful) GPT models via API. Inside The Complete Obsolete Guide to Generative AI you will find: • Just enough background info on AI! What an AI model is how it works • Ways to create text, code, and images for your organization's needs • Training AI models on your local data stores or on the internet • Business intelligence and analytics uses for AI • Building your own custom AI models • Looking ahead to the future of generative AI Where to get started? How about creating exciting images, video, and even audio with AI. Need more? Learn to harness AI to speed up any everyday work task, including writing boilerplate code, creating specialized documents, and analyzing your own data. Push beyond simple ChatGPT prompts! Discover ways to double your productivity and take on projects you never thought were possible! AI—and this book—are here to show you how. About the technology Everything you learn about Generative AI tools like Chat-GPT, Copilot, and Claude becomes obsolete almost immediately. So how do you decide where to spend your time—and your company’s money? This entertaining and unbelievably practical book shows you what you can (and should!) do with AI now and how to roll with the changes as they happen. About the book The Complete Obsolete Guide to Generative AI is a lighthearted introduction to Generative AI written for technology professionals and motivated AI enthusiasts. In it, you’ll get a quick-paced survey of AI techniques for creating code, text, images, and presentations, working with data, and much more. As you explore the hands-on exercises, you’ll build an intuition for how Generative AI can transform your daily work and communication—and maybe even learn how to make peace with your new robot overlords. What's inside • The big picture of Generative AI tools and tech • Creating useful text, code, and images • Writing effective prompts • AI-driven data analytics About the reader Written for developers, admins, and other IT pros. Some examples use simple Python code. About the author David Clinton is an AWS Solutions Architect, a Linux server administrator and a world-renowned expert on obsolescence. The technical editor on this book was Maris Sekar. Table of Contents 1 Understanding generative AI basics 2 Managing generative AI 3 Creating text and code 4 Creating with media resources 5 Feeding data to your generative AI models 6 Prompt engineering: Optimizing your experience 7 Outperforming legacy research and learning tools 8 Understanding stuff better 9 Building and running your own large language model 10 How I learned to stop worrying and love the chaos 11 Experts weigh in on putting AI to work A Important definitions and a brief history B Generative AI resources C Installing Python

Key Technologies Powering Generative AI

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