Generative AI For The Rest Of US
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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!
About the Author
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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