Generative AI
The Generative AI workshop introduces the fundamentals of generative AI, distinguishing it from traditional AI and exploring its applications and historical evolution. Participants learn the basics of machine learning, focusing on neural networks and different types of generative models like GANs and VAEs. Practical applications in art, text, and video generation are discussed, alongside ethical considerations and future trends in the field. The workshop also covers key contributions from major vendors such as Microsoft, Google, Amazon, and OpenAI, highlighting their significant products and services. This comprehensive curriculum aims to equip beginners with a solid understanding of generative AI and its real-world impact.
Module 1: Introduction to Generative AI
This foundational module defines generative AI, differentiates it from other AI types, and explores its key characteristics and diverse applications. Students learn about various generative AI models (LLMs, Diffusion Models, GANs, VAEs), their underlying techniques and algorithms, and the crucial role of data quality and bias mitigation. The module culminates in an overview of available generative AI tools and platforms.
Module 2: Generative AI Applications
This module explores the practical applications of Generative AI across various industries. Lessons cover text and language processing, image and video generation, music and audio generation, code generation, drug discovery and healthcare, and applications within the creative industries and the future of work. Each lesson emphasizes both the potential benefits and associated challenges, including ethical considerations.
Module 3: Generative AI Models and Architectures
This module delves deeper into the technical aspects of generative AI models, providing a high-level understanding of their architectures and functionality without requiring extensive mathematical expertise. It covers LLMs, diffusion models, GANs, and VAEs, examining their strengths and weaknesses, training processes, evaluation metrics, and fine-tuning techniques.
Module 4: Generative AI and Ethics
This module addresses the critical ethical considerations surrounding generative AI. Lessons cover bias, copyright and intellectual property, transparency and explainability, misinformation and malicious use, job displacement, and responsible innovation. Students actively engage in analyzing ethical dilemmas and developing mitigation strategies.