Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI's ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research…mehr
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI's ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
Sudha Jamthe is a technology futurist, author of 7 books, educator, and independent researcher with 25+ years of entrepreneurial and operational experience in the technology industry. She is a LinkedIn Learning instructor for courses on AI, GenAI, and AI for the Internet of Things and autonomous vehicles. Her specialty is mentoring technology innovators, designers and business leaders using No-Code AI. She builds online learning communities at the Business school of AI with the WeeklyWed global speaker series. She is passionate about Gender Equality and hosts IoTWoman annually, showcasing 24 global women leaders in technology over 24 hours. She has produced courses about AI, AI ethics, and autonomous vehicles at Stanford Continuing Studies, Barcelona Technology School, Business School of AI, and EMBA Consortium at Lucas School of Business. She upskills researchers on language inclusion at Global South in AI and brings them to present at the NeurIPS conference. Her aspiration is a limitless world. In her spare time, Sudha enjoys chasing self-driving cars and hugging robots. She can be reached at sudhajamthe.com.
Inhaltsangabe
1. What is Generative AI and Why Does it Matter? 2. Technology of Foundation Models General Adversarial Networks (GAN) Synthetic Data Attention Diffusion Models 3. Generative AI Models Models Explained in English Research in GenAI Model Modality Semi-Supervised AI 4. Large Language Models LLM How LLM Works State of research of LLMs Ethical Implications of Building LLMs 5. LLM for Global Languages Country-specific LLMs Mistral Aya 6. What is Hallucination Causes and the State of Research of Hallucination in LLMs 7. Data Fine-tuning and Retrieval Augmented Generation (RAG) Difference Between RAG and Fine-tuning State of Research of Fine-tuning and RAG 8. LLM Capabilities Reasoning Models Q&A Models State of Research in Reasoning 9. The API Economy GenAI Market Trends GenAI Applications 10. Industries impacted by GenAi: Software Engineering Education e-commerce Customer Support 11. Ethical Product Design with GenAI Prompt Engineering and GenAI Middleware 12. Enterprise GenAI Data Modeling Automated Buildings 13. Ethical Responsible GenAI which Industries are Impacted More by GenAI Automation and Jobs Gender Bias and Lack of Explainability 14. AI and Social Change Anthropomorphism Voice Clones and Deep Fakes Competencies Needed for Success with GenAI 15. Appendix: Models for Reference Research References and Ethical Governance Frameworks
1. What is Generative AI and Why Does it Matter? 2. Technology of Foundation Models General Adversarial Networks (GAN) Synthetic Data Attention Diffusion Models 3. Generative AI Models Models Explained in English Research in GenAI Model Modality Semi-Supervised AI 4. Large Language Models LLM How LLM Works State of research of LLMs Ethical Implications of Building LLMs 5. LLM for Global Languages Country-specific LLMs Mistral Aya 6. What is Hallucination Causes and the State of Research of Hallucination in LLMs 7. Data Fine-tuning and Retrieval Augmented Generation (RAG) Difference Between RAG and Fine-tuning State of Research of Fine-tuning and RAG 8. LLM Capabilities Reasoning Models Q&A Models State of Research in Reasoning 9. The API Economy GenAI Market Trends GenAI Applications 10. Industries impacted by GenAi: Software Engineering Education e-commerce Customer Support 11. Ethical Product Design with GenAI Prompt Engineering and GenAI Middleware 12. Enterprise GenAI Data Modeling Automated Buildings 13. Ethical Responsible GenAI which Industries are Impacted More by GenAI Automation and Jobs Gender Bias and Lack of Explainability 14. AI and Social Change Anthropomorphism Voice Clones and Deep Fakes Competencies Needed for Success with GenAI 15. Appendix: Models for Reference Research References and Ethical Governance Frameworks
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