The author further explains the concept of vector spaces in Linear Algebra, where words are evaluated in multiple dimensions based on their meanings. The challenge of processing such large amounts of data is addressed through Hierarchical Navigable Small World (HNSW), a method that simplifies the search for similar vectors in high dimensional spaces. The author concludes by comparing the efficiency of human brains to AI models like GPT-4, highlighting the remarkable efficiency of the human brain.
Key takeaways:
- The author delves into the world of Vector Databases, explaining their importance and how they've been around for longer than most people realize.
- Computers do not understand words, they operate on binary language, which is just 1s and 0s, so numbers. Computers only understand numbers.
- Contextualized word embeddings allow us to generate different and unique numbers for words, depending on their context or semantic meaning.
- Linear Algebra and Hierarchical Navigable Small World (HNSW) are used to manage large amounts of data in high dimensional spaces, making it possible to process the vast amount of data associated with human speech or thinking processes.