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Beyond stateless AI-models: Exploring vector databases for stateful knowledge representation

Most machine learning models interacted with the world during training by ingesting text, audio, depth, heath (maps), IMU, and other data types to create a stateless snapshot representation of the world, expressed in space, using vector embeddings.

Vector databases are the shepherds of the time element. By using the models to generate vector embeddings to represent new knowledge and injecting them into the ML model (a technique called retrieval augmented generation), we can give generative models access to unique, personal, or proprietary data. Giving state to the new wave of AI-native applications.

In this talk we will look at the next evolution in the world of open-source vector databases, where we tighten the relationship of knowledge management in space (the models) and time (the database).

Featuring:
Bob van Luijt - Founder/CEO - Weaviate
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