Consolidating key AI and ML concepts
In this section, we will briefly revisit the core GenAI concepts discussed in previous chapters to prepare us for our exploration of emerging technologies and future trends. In each case, you can refer to the original chapter for a more in-depth discussion of the content and its theory.
In Chapter 9, we first laid the foundation for understanding the origins of GenAI models through a discussion of probabilistic models and contextual embeddings. Key concepts to remember from that chapter include:
- Bayesian inference and probabilistic models: Bayes’ Theorem guides the model to adjust its understanding by integrating prior knowledge with new evidence. This adaptive process is crucial for scenarios where the model encounters entirely new contexts—enabling it to refine its predictions without the need for extensive retraining.
- GenAI models: Foundational models such as generative adversarial networks (GANs), variational...
