Chapters outlining the history, benefits, challenges, and future of Generative Artificial Intelligence
An exploration into the realm of Generative AI, its tools, major companies, and applications across various industries.
Chapter 1: Introduction
Generative AI is a type of artificial intelligence that can create new content, from text to images, based on patterns it has learned from existing data.
Chapter 2: History of Generative AI
Markov chains were used in modeling natural languages, Harold Cohen’s AARON program created generative AI artworks, and generative AI planning systems were applied for military, manufacturing, and decision making.
The shift towards deep learning led to the evolution of machine learning, and variational autoencoder and generative adversarial network enabled practical deep neural networks for complex data.
Chapter 3: Benefits of GPU AI
GPUs help process large scale data required for Generative AI, with high energy consumption and water cooling requirements.
Chapter 4: NVIDIA vs AMD GPUs
Details comparing the features, performance, and advantages of NVIDIA and AMD GPUs for Generative AI applications will be discussed here.
Chapter 5: Ethical Concerns and Governance Challenges
This chapter discusses the potential use of Generative AI for cybercrime and deception, its impact on the job market, ethical considerations, and intellectual property laws violations.
Chapter 6: Environmental Impact of AI
This chapter analyzes the material and energy intensity of AI systems, particularly the environmental concerns raised by their high energy consumption and cooling requirements.
Chapter 7: Future Perspectives of Generative AI
This chapter predicts developments and advancements in Generative AI technology, potential applications across various industries, ethical guidelines, regulations, and governance needed for responsible development and use of Generative AI.