This episode explore advancements in Maximum Inner Product Search (MIPS), a crucial technique for vector similarity search in machine learning and information retrieval. Several sources highlight Google's ScaNN library and its enhancements like SOAR (Spilling with Orthogonality-Amplified Residuals), which boost efficiency and accuracy in finding similar data points. The concept of Anisotropic Vector Quantization is also introduced as a key innovation in ScaNN for better inner product estimation. Furthermore, the texts discuss REALM (Retrieval-Augmented Language Model Pre-training), which integrates MIPS to enable language models to explicitly retrieve knowledge, and MUVERA (Multi-Vector Retrieval via Fixed Dimensional Encodings), presenting novel graph-based methods like PSP (Proximity Graph with Spherical Pathway) and Adaptive Early Termination (AET) to optimize MIPS, with real-world applications in e-commerce search engines. The collection collectively emphasizes the shift towards semantic understanding in search and its implications for SEO strategies.