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After decades of false starts, Artificial Intelligence (AI) is entering the mainstream of society. That is largely due to the rapid adoption of ChatGPT, a service that responds to almost any natural language question with cogent paragraphs. ChatGPT is the leading example of generative AI -- technology that creates original text, images, video and computer code based on uncovering patterns in training data.
The book will explain how generative AI works and how much economic value it could create and will map out the industry value network. For each value network stage, the book will define the industry, estimate its size, growth rate, and profit potential, identify the most successful participants, and explain how they have achieved their success and where they will compete in the future. The book will conclude with a section on what investors and business leaders should do to make an informed decision on where to place their bets.
Generative AI answers natural language questions with clearly written paragraphs, images, videos, or computer code. Through applications such as ChatGPT for text and DALL-E and Midjourney for images, Generative AI identifies “patterns in large quantities of training data, and then creates original material that has similar characteristics,” according to the New York Times. How do chatbots – generative AI applications for text – discover these patterns? Chatbots are large language models – computer systems that have processed large volumes of text – called training data. Through training, LLMs can predict the words most likely to follow any prompt the user supplies. LLMs are a type of neural network that seeks to replicate the way the human brain processes information. ChatGPT’s neural network is a transformer, which assigns numerical weights to the relationship between short bits of text, called tokens. Transformers use a technique called self-attention, which allows the model to focus on specific words that best elucidate the meaning of the sentence in which it appears.
Introducing Brain Rush
What Is Generative AI?
Part I: Mining Generative AI’s End-User Value
Chapter 3: Generative AI Customer End Uses
Mapping the Generative AI Ecosystem
Generative AI Consulting
Generative AI Software
Generative AI Cloud Platforms
Generative AI Hardware
Part III: Panning for Generative AI Gold
Generative AI’s Implications for Consumers, Employees, Companies, Product Providers, and Investors
Generative AI’s Benefits and Risks to Society
After the Brain Rush: What Is Generative AI’s Future?
Conclusion