Generative AI Fundamentals

This session will introduce the fundamentals of Generative AI, explaining how it differs from, as well as how it complements traditional discriminative models in machine learning.

I will start by defining key concepts in generative modeling, showing how these systems learn underlying patterns in data to create new outputs such as text, images, or music. After contrasting generative and discriminative approaches, I will explore the history and evolution of Generative AI, highlighting groundbreaking moments and milestones that shaped the field.

Next, I will talk about the key technologies powering modern Generative AI. This segment will delve into model architectures like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as well as discuss how advancements in hardware and computing power have accelerated research. We will then look at Generative AI in practice, showcasing real-world applications ranging from content creation and design to healthcare and beyond.

Finally, we will identify current trends and predictions—including ethical considerations and the potential impact on various industries—offering a glimpse of where Generative AI may lead us in the near future. The session will conclude with an open Q&A, providing an opportunity for further discussion and clarification.

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