To effectively use multiple types of data, AI systems first need a solid understanding of each data type on its own. This chapter addresses how different forms of information, such as text, images, audio, and video, are prepared and structured so that machines can process them. We will look at the common ways these data are represented and the initial steps taken to get them ready for more complex multimodal tasks.

You will learn about:

Data Representation: How text, images, audio, and video are transformed into numerical formats that AI models can work with. For instance, an image can be seen as a grid of pixel values, where each pixel at coordinates $(x, y)$ has an intensity $I(x,y)$ .
Basic Preprocessing: The initial methods used to clean and prepare raw data from each modality, making it suitable for AI algorithms.
Data Alignment: The importance of synchronizing or linking data from different sources, such as matching spoken words in an audio file to the corresponding visual cues in a video.
Comparing Information Across Modalities: An introduction to how we can measure similarities or differences in the content conveyed by various types of data.

Grasping these data preparations is an important step before learning how AI models integrate these diverse information streams.

Chapter 2: Data Foundations for Multimodal Systems

Sections