Having established how individual data modalities such as text, images, and audio are represented for AI processing, this chapter addresses the methods for combining this diverse information. We will examine the core techniques that enable AI systems to integrate data from these different sources.

This chapter covers:

• Multimodal Fusion Strategies: An overview of early, intermediate, and late fusion. You'll learn how and at what stage data streams are merged. For example, early fusion might involve concatenating feature vectors $v_{\text{image}}$ and $v_{\text{text}}$: $v_{\text{fused}} = \text{concat}(v_{\text{image}}, v_{\text{text}})$
• Shared Representations: Techniques where information from various modalities is projected into a common feature space. The goal is often to find a space $S$ where representations $s_i \in S$ are comparable across modalities.
• Coordinated Representations: Methods that focus on learning mappings or correlations between distinct modal spaces, rather than necessarily creating a single shared space.
• Basic Architectures: An introduction to fundamental neural network architectures frequently employed in multimodal learning tasks.
• Attention Mechanisms: A brief look at attention, a technique that allows models to selectively weigh the importance of different parts of the input data from various modalities.

By the end of this chapter, you will have a solid understanding of the primary approaches multimodal systems use to bring together information from separate channels, forming a more comprehensive basis for interpretation and decision-making.