The Most Important Generative AI Piece? (Vol. 1)
Are vector databases the most important element of the generative AI architecture?
Each week, Vector Database Central shares three big ideas about generative AI and how to harness its data. Here’s volume 1!
THE MOST IMPORTANT PIECE OF GENERATIVE AI ARCHITECTURE?
Vector databases have been declared "the most important piece of generative AI architecture from a systems standpoint." by Andreessen Horowitz because vector embeddings encode images, audio, video, and unstructured data so they’re searchable. Without them, developers must rely on tags, metadata, and keyword searching.
Read Vector Embeddings 101: The New Building Blocks for Generative AI to learn more.
A VISUAL GUIDE TO VECTOR EMBEDDINGS
‘A Visual Guide to Vector Embeddings’ takes on four types of vector embeddings and shows you six applications for AI.
For example, embeddings capture the semantic meaning of words and their relationships within a language. For example, they could encode semantic similarities between words, such as "king" being closer to "queen" than to "car."
Download the “A Visual Guide to Vector Embeddings” ebook to learn more.
THREE WAVES OF GENERATIVE AI
Three waves of generative AI innovation are rolling in. They form an epic swell that’s reshaping the landscape of every business.
1️⃣ Paddling - dipping a toe with non-core business use of AI 🧠
2️⃣ Riding the Wave - integrating AI into existing products/services 🌊
3️⃣ Surf the big one – reimagining entire business models with AI 🏄♂️
For more, read Surf’s Up: Three Waves of Generative AI Innovation on
Surf’s Up! See you next week.
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