Introduction: Generative AI

Get an overview of the topics that will be covered in this chapter.

In this chapter, we’ll dive into the various applications of generative models. Before that, we’ll take a step back and examine how exactly generative models are different from other types of machine learning. The difference lies with the basic units of any machine learning algorithm: probability and the various ways we use mathematics to quantify the shape and distribution of data we encounter in the world.

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In the rest of this chapter, we’ll cover:

  • A wide range of fields and tasks encompassed by AI applications showcasing its versatility and impact.

  • The two fundamental approaches in machine learning: discriminative models and generative models, each with its own strengths and applications.

  • The implementation of generative models, which involves creating algorithms and systems capable of generating new data instances that resemble those in the training dataset.

  • The crucial role of rules of probability in understanding and modeling uncertainty, guiding decision-making processes in various AI applications.

  • The use of generative models that offer unique advantages, such as data synthesis, augmentation, and creativity, expanding the scope of AI applications.

  • The unique challenges that arise in the development and deployment of generative models.