Generative AI

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