Using Vector Databases to Scale Multimodal Embeddings, Retrieval and Generation

Many real-world problems are inherently multimodal, from the communicative modalities humans use such as spoken language and gestures to the force, proprioception, and visual sensors ubiquitous in robotics. In order for machine learning models to address these problems and interact more naturally and wholistically with the world around them and ultimately be more general and powerful reasoning engines we need them to understand data across all of its corresponding image, video, text, audio, and tactile representations. In this talk, I will discuss how we can use multimodal models, that can see, hear, read, and feel data(!), to perform cross-modal retrieval/search at the billion-object scale with the help of vector databases. I will also demonstrate, with live code demos and large-scale datasets, how being able to perform this cross-modal retrieval in real-time can help us guide the generative capabilities of large language models by grounding it in the relevant source material.

Zain Hasan

Weaviate

Zain Hasan is a Senior Developer Advocate at Weaviate an open-source vector database. He is an engineer and data scientist by training, who pursued his undergraduate and graduate work at the University of Toronto building artificially intelligent assistive technologies. He then founded his company developing a digital health platform that leveraged machine learning. More recently he practiced as a consultant senior data scientist in Toronto. He is passionate about open-source software, education, community, and machine learning.