In this session we introduce the concept of word embeddings as a proxy for "meaning" and explain the intuition of how they play an important role in modern AI systems. We will begin by starting with a simpler example - colors - and show how we can use a simple 3-dimensional vector to represent colors. We will then show the intuition of distance between colors and how colors that are closer to each other in distance are also closer to each other visually. We will then connect the concept of vectors to represent colors to vector in a higher dimension space that can represent the "meaning" of words and then show how words that have similar meaning will be closer to each other.
