Generative AI For The Rest Of US
Generative AI For The Rest Of US
Your Future, Decoded
About the Book
The primary purpose of this book is to provide the average person with a comprehensive, 360-degree view of the Generative AI landscape.
It simplifies the material in a way that is suitable for both beginners and experts, enabling readers to understand everything they need to know about the topic and prepare for the future.
From foundational to cutting-edge, the book demystifies complex AI concepts in an easily understandable manner.
Even without a solid background in AI, readers will be able to grasp the most complex concepts.
The book answers many questions, including:
General knowledge
- What is Generative AI?
- How has AI evolved from the 1950s to the present day?
- What are the most common foundational Generative AI models?
- What are the differences between ChatGPT, Google Gemini, Claude, Mistral, LLaMA, Amazon Titan, Alibaba Tongyi Tingwu, and other Generative AI models?
- What are the most common applications of Generative AI?
Our Economy
- In what ways is the US leading the global AI industry, and why is China catching up very quickly?
- How are the UK, Canada, Germany, France, Japan, South Korea, and other countries investing in AI?
- What are the most common AI strategies and policies worldwide?
- What are the current trends in Generative AI globally?
- What is the relationship between AI, economy, and geopolitics?
- How is the semiconductor industry shaping the future of Generative AI?
The Workforce
- In what ways does AI automate tasks and transform jobs?
- Will AI take over your job?
- What are the predicted impacts of Generative AI on global GDP and personal income?
- How is Generative AI used in healthcare, finance, marketing, education, retail, manufacturing, and other industries?
- What are the most common myths about AI?
The Applications
- What are the leading applications of Generative AI that you can start using today?
- In what ways do AI-assisted platforms facilitate content creation, including writing, image generation, voice synthesis, and music composition, thereby enhancing creativity and productivity?
The Technology
- How are generative AI models created?
- What is the difference between "symbolic AI" and "connectionist AI"?
- How are AI, machine learning, deep learning, and generative AI related?
- What is supervised learning, unsupervised learning, and reinforcement learning?
- What is autonomous generative AI and how is it different?
Your Productivity
- How are costs for Generative AI services, like ChatGPT, Google Gemini, Claude, and similar platforms, calculated for regular users and developers?
- What is "tokenization," and how is it used in Generative AI?
- How can you increase your productivity using Prompt Engineering?
- What are the most advanced Prompt Engineering techniques?
Ethics, Regulation, and Security
- How can AI wipe out humanity?
- How is the Dead Internet Theory related to Generative AI?
- Why is the Godfather of Deep Learning worried about the future?
- How do figures like Elon Musk, Geoffrey Hinton, and Chomsky view the potential risks of Generative AI? Are their concerns valid?
- What are the limitations and criticisms of current Generative AI technologies in terms of understanding and intelligence?
- How are the United States, China, the EU, and the UK approaching AI regulation?
- How is Generative AI harming the environment?
- Should we be optimistic or pessimistic about the future of Generative AI?
Filled with interesting facts, statistics, demonstrations, criticisms, and answers to a multitude of questions, the book is a must-read for anyone interested in their future!
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Table of Contents
- Preface
- How This Book Is Organized
- Who Should Read This Book?
- About the Author
- Join the Community
- Bonus Content
- Rome, AI, and the Trillion-Dollar Market
- The Trillion-Dollar AI Market
- The Global AI Landscape: A Comparative Analysis
- The United States: The Leader in AI Investment
- China: The Battle for Global AI Dominance
- The United Kingdom: No Rush to Regulate
- Japan: A Dual Landscape
- India: High Trust and Adoption
- Germany: A Strategic Shift and Lack of AI Adoption
- Canada: The Global Leader in AI Talent Concentration
- South Korea: Highest AI Understanding and AI-Assisted Spirituality
- France: Ambitions and Fears
- Sweden: A Surge in AI Investment Despite Challenges
- Singapore: Leading the Southeast Asian AI Landscape
- Finland: Opportunities and Challenges in Adoption
- Australia: Collaboration and Innovation in the Medical Sector
- Russia: Putin’s Vision for AI Dominance Amidst Challenges
- Estonia: Escalating Investments for a Tech-Driven Future by 2030
- Israel: Strategic Investments Spark Market Growth and Military Innovations
- Netherlands: Enabling Digital Sovereignty and Semiconductor Industry
- The Global AI Landscape: A Summary
- The Complexity of the AI Landscape
- From Alan Turing to Generative AI: A Quick Timeline of Modern AI
- 1950: The Turing Test
- 1956: The Dartmouth Conference
- 1965 - 1972: ELIZA and PARRY, the First Chatbots
- 1973 and 1988: The First and Second AI winters
- 1997: Deep Blue Defeats Garry Kasparov
- 2006 - 2010: The AI Renaissance
- 2011: IBM Watson Wins Jeopardy, Apple Introduces Siri
- 2014 - 2016: The Emergence of Deep Learning
- 2017: Attention Is All You Need
- 2018 - 2019: GPT-2 and BERT
- 2020 and Beyond: The AI Boom and The Rise of Generative AI
- Moving Forward
- From AI to Generative AI: Understanding the Magic Behind Our Machines
- AI is the New Electricity
- Connectionism vs Symbolism: The Yin and Yang of AI
- Symbolism: Reasoning Through Rules
- Connectionism: Biological Inspiration
- Neuro-symbolic AI: The Best of Both Worlds
- AI, Machine Learning, and Deep Learning: How Do They Relate?
- Generative AI: The Next Big Thing
- Sam Altman’s Bet: Scaling Up
- Neural Networks and Transformers: The Foundation of Modern AI
- Convolutional Neural Networks
- Recurrent Neural Networks
- Long Short-Term Memory Networks
- Gated Recurrent Units (GRUs)
- Generative Adversarial Networks
- Transformers: Transforming the Landscape
- The Landscape of Generative AI: Foundation Models, Platforms, and Applications
- Foundation Models: The Building Blocks of Generative AI
- BERT and MUM: Powering Google Search
- GPT-1 and GPT-2: The Ancestors of ChatGPT
- ChatGPT and the Future: The Evolution Continues
- Gemini: Google’s Latest Generative AI Model
- Mistral: The Rising Star in Generative AI
- LLaMA: Facebook’s Open Foundation Language Models
- Claude: Constitutional AI and Reinforcement Learning From Human Feedback
- Amazon Titan: The E-Commerce Giant’s Foray Into Generative AI
- Midjourney: Award-Winning Image Generator
- Stable Diffusion: The Open-Source Alternative
- Tongyi Tingwu: The Chinese Competitor
- Building on the Foundations: Platforms, Agents, Autonomous Systems, and Beyond
- Autonomous Agent-Driven Solutions
- AutoGPT: A Foundation for Making AI Agents Accessible
- AgentGPT: Configure and Deploy Autonomous AI Agents
- AutoGPT vs. AgentGPT
- Multi-GPT
- BabyAGI
- Multi-Model Language Model Interfaces
- ChatGPT GPTs
- AI-Powered Writing Platforms
- Jasper
- GrowthBar
- Frase
- Copy.ai
- AI Image Generation Platforms
- PlaygroundAI
- Adobe Firefly
- Generative AI by Getty Images
- Talking AI Avatar Platforms
- Synthesia
- Elai.io
- HeyGen
- AI Text-to-Speech Platforms
- Descript
- ElevenLabs
- Lovo AI
- AI-Assisted Music Composition Platforms
- AIVA
- Soundful
- Soundraw
- AI Presentation Generators
- Beautiful.AI
- Slidebean
- SlidesAI
- Autonomous Agent-Driven Solutions
- Foundation Models: The Building Blocks of Generative AI
- Understanding Pricing Models for Generative AI Tools
- How Costs are Calculated
- OpenAI GPT Models
- Google Gemini
- Claude
- Mistral
- StableDiffusion
- Self-Hosted Membership
- API
- Image Generation Using the API
- Image Upscaling Using the API
- Editing Using the API
- Video Generation Using the API
- Midjourney
- Prompt Engineering: Efficiency in the Age of AI
- Zero-Shot Learning
- Few-Shot Learning
- Chain of Thought (CoT)
- Self-Consistency
- Tree of Thought Prompting
- Considering Perplexity
- General Knowledge Prompting
- Best Practices for Prompt Engineering
- Use Action Verbs
- Provide a Clear Context
- Use Role-Playing
- Break Down Complex or Lengthy Tasks
- Styles, Tones, Formats, Structures, Lengths
- Use Single Quotes, Double Quotes, and Triple Quotes
- Using Separators
- Providing Examples and Guiding the Model
- Refining Continuously
- Viewing the Problem from Different Angles
- Stay Curious, Experiment, and Never Stop Learning
- Generative AI and the Future of Work
- Reshaping the Economic Landscape
- Generative AI Applications Across Industries
- Generative AI in Healthcare
- Generative AI in Finance
- Generative AI in Education
- Generative AI in Marketing
- Generative AI in Retail
- Generative AI in Manufacturing
- Will AI Take Over Your Job?
- Ethics, Limitations, and Controversies of Generative AI
- The Mechanical Turk and the Artificial Artificial Intelligence
- Good Artists Copy; Great Artists Steal
- AI Could “Wipe Out Humanity”
- The Dead Internet Theory
- The Environmental Cost of AI
- Is AI a Solution or a Problem?
- AI Regulations Examples
- US’s AI Regulation: Balancing Innovation and Governance
- China’s New Generation Artificial Intelligence Development Plan: A Pioneer in AI Regulation
- EU’s AI Act: Privacy and Safety at the Forefront
- UK’s AI Regulation: A Pro-Innovation Regulatory Framework
- The Squirrel Brain Leading the Fourth Industrial Revolution
- The Journey Ahead
- Preface
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