Ever wondered how AI systems handle images and videos, or how they make lightning-fast recommendations? Tune in as Nicolay chats with Zain Hassan, an expert in vector databases from Weaviate. They break down complex topics like quantization, multi-vector search, and the potential of multimodal search, making them accessible for all listeners. Zain even shares a sneak peek into the future, where vector databases might connect our brains with computers!Zain Hasan:LinkedInX (Twitter)WeaviateNicolay Gerold:⁠LinkedIn⁠⁠X (Twitter)Key Insights:Vector databases can handle not just text, but also image, audio, and video dataQuantization is a powerful technique to significantly reduce costs and enable in-memory searchBinary quantization allows efficient brute force search for smaller datasetsMulti-vector search enables retrieval of heterogeneous data types within the same indexThe future lies in multimodal search and recommendations across different sensesBrain-computer interfaces and EEG foundation models are exciting areas to watchKey Quotes:"Vector databases are pretty much the commercialization and the productization of representation learning.""I think quantization, it builds on the assumption that there is still noise in the embeddings. And if I'm looking, it's pretty similar as well to the thought of Matryoshka embeddings that I can reduce the dimensionality.""Going from text to multimedia in vector databases is really simple.""Vector databases allow you to take all the advances that are happening in machine learning and now just simply turn a switch and use them for your application."Chapters00:00 - 01:24 Introduction01:24 - 03:48 Underappreciated aspects of vector databases03:48 - 06:06 Quantization trade-offs and techniquesVarious quantization techniques: binary quantization, product quantization, scalar quantization06:06 - 08:24 Binary quantizationReducing vectors from 32-bits per dimension down to 1-bitEnables efficient in-memory brute force search for smaller datasetsRequires normally distributed data between negative and positive values08:24 - 10:44 Product quantization and other techniquesAlternative to binary quantization, segments vectors and clusters each segmentScalar quantization reduces vectors to 8-bits per dimension10:44 - 13:08 Quantization as a "superpower" to reduce costs13:08 - 15:34 Comparing quantization approaches15:34 - 17:51 Placing vector databases in the database landscape17:51 - 20:12 Pruning unused vectors and nodes20:12 - 22:37 Improving precision beyond similarity thresholds22:37 - 25:03 Multi-vector search25:03 - 27:11 Impact of vector databases on data interaction27:11 - 29:35 Interesting and weird use cases29:35 - 32:00 Future of multimodal search and recommendations32:00 - 34:22 Extending recommendations to user data34:22 - 36:39 What's next for Weaviate36:39 - 38:57 Exciting technologies beyond vector databases and LLMsvector databases, quantization, hybrid search, multi-vector support, representation learning, cost reduction, memory optimization, multimodal recommender systems, brain-computer interfaces, weather prediction models, AI applications

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