Generative AI: A Non-Technical Introduction

Description

OpenAI's release of ChatGPT unleashed the generative AI revolution. Within a few months, the platform attracted more than 100 million users.  This was the fastest ever for an online application. While traditional AI focuses on predictions and insights, generative AI is about creativity. ChatGPT has already shown how this technology can seem almost human, even magical.  But this is just the start.  Generative AI will have a seismic impact across every industry.  It will help to discover breakthrough drugs.  It will create hit songs and movies.  There seems to be no limits. In this book, Tom Taulli will show how generative AI works.  This will include easy-to-understand explanations of critical innovations like transformers, diffusion models and generative adversarial networks.  He will also highlight the incredible entrepreneurial stories of how companies like Jasper, StabilityAI and Hugging Face have leveraged this technology to grow at staggering rates.  The book includes strategies for using generative AI.  Taulli shows how to evaluate the tools as well as how to successfully implement them. Finally, he will explore the future of the technology, such as what it might mean for employment and the impact on economic growth. Contents About the Author Foreword Chapter 1: Introduction to Generative AI Definition Sentient? The Opportunity Using Generative AI The ChatGPT Effect The Drivers Skeptics Dangers of Hype Conclusion Chapter 2: Data Value of Data The Amazing Growth of Data Big Data Databases Cloud Models Data Strategy Data Collection Common Data Sources Data Evaluation Data Wrangling Data Labeling Quantity Privacy Laws and Regulations Generative AI for Data Wrangling Generative AI for Data Preparation Chief Data Officer Conclusion Chapter 3: AI Fundamentals Early AI Programs AI Struggle Then What Is AI? Machine Learning Supervised Learning Regression Analysis Support Vector Machines Random Forest K-Nearest Neighbor Naïve Bayes Classifiers Unsupervised Learning Clustering Anomaly Detection Association Autoencoders Reinforcement Learning Terms and Setup for Deep Learning How Deep Learning Works Types of Deep Learning The Brain and AI Drawbacks with Deep Learning Overfitting and Underfitting Python AI Tools AI Systems for Beginners Conclusion Chapter 4: Core Generative AI Technology Generative vs. Discriminative Models Probability Theory Types of Generative AI Models Generative Adversarial Networks (GANs) Variational Autoencoder (VAE) Diffusion Models DALL-E 2 Stability AI and Midjourney Speech Trilemma of Generative AI Models Conclusion Chapter 5: Large Language Models Language and Intelligence Natural Language Processing (NLP) How NLP Works Word2Vec Model Transformers Dials BERT (Bidirectional Encoder Representations from Transformers) GPT Systems and ChatGPT Dolly Gopher and Gato Cohere AI21 Labs BLOOM Megatron-Turing Natural Language Generation Model GPT-Sw3 Issues New Startup Model Prompt Engineering Character AI Empathy Conclusion Chapter 6: Auto Code Generation The Developer Shortage How AI Code Generation Systems Work Copilot AlphaCode Tabnine Magic PolyCoder Blaze Debugging Code Data Labeling Prompt Engineering for Coding Atera Large-Scale Projects Drawbacks and Risks Conclusion Chapter 7: The Transformation of Business Legal Customer Experience Sales and Marketing Anyword Wope INK Regie.ai Lavender and SellScale Grammarly Writer Cresta Forethought Intercom Product Development Spoke AI Presentations Buy vs. Build Implementing Generative AI Identify the Problem to Be Solved Form a Team Data Preparation and AI Modeling Deploy and Monitor the AI System The Generative AI Process Conclusion Chapter 8: The Impact on Major Industries Music WaveAI Education GPTZero Duolingo Journalism Gaming Roblox Healthcare Creating X-Rays Finance Conclusion Chapter 9: The Future Challenges Misuse Regulation New Approaches to AI AGI Jobs Conclusion Index