AIM 101 - Introduction to Generative AI

1. Introduction to Generative AI
2. Neural Networks and Model Foundations
3. Exploration of generative AI's influence across industries
4. Prompt design and engineering
5. Generative AI tools and technologies
6. Ethics, fairness, and regulation guiding AI development
7. Model lifecycle and optimization
8. Prototyping and responsible innovation

1. Discuss generative Artificial Intelligence (AI), including its history, fundamental concepts, technological advancements, and future projections. (1, 2)
2. Describe how generative models function across various industries, focusing on real-world applications and societal impacts. (3, 6)
3. Demonstrate prompt design and application for Natural Language Processing (NLP) and image-generation tasks. (4, 5)
4. Use generative AI tools to create text, images, audio, and video content. (4, 5, 7)
5. Explain ethical considerations in generative AI, including fairness, transparency, and the role of global policy and regulation. (3, 6, 8)
6. Describe neural network fundamentals and the evolution of technologies such as Large Language Models (LLM) and diffusion models. (1, 2, 7)
7. Apply basic techniques in the lifecycle of generative AI projects, including pre-training, fine-tuning, and optimization. (5, 7, 8)
8. Prototype generative AI applications using appropriate techniques. (5, 7, 8)
9. Analyze strategies for aligning LLMs to ethical principles using case studies. (6, 7, 8)