Tag: embeddings

  • Cloud Blog: Arize, Vertex AI API: Evaluation workflows to accelerate generative app development and AI ROI

    Source URL: https://cloud.google.com/blog/topics/partners/benefits-of-arize-ai-in-tandem-with-vertex-ai-api-for-gemini/ Source: Cloud Blog Title: Arize, Vertex AI API: Evaluation workflows to accelerate generative app development and AI ROI Feedly Summary: In the rapidly evolving landscape of artificial intelligence, enterprise AI engineering teams must constantly seek cutting-edge solutions to drive innovation, enhance productivity, and maintain a competitive edge. In leveraging an AI observability…

  • Simon Willison’s Weblog: docs.jina.ai – the Jina meta-prompt

    Source URL: https://simonwillison.net/2024/Oct/30/jina-meta-prompt/#atom-everything Source: Simon Willison’s Weblog Title: docs.jina.ai – the Jina meta-prompt Feedly Summary: docs.jina.ai – the Jina meta-prompt From Jina AI on Twitter: curl docs.jina.ai – This is our Meta-Prompt. It allows LLMs to understand our Reader, Embeddings, Reranker, and Classifier APIs for improved codegen. Using the meta-prompt is straightforward. Just copy the…

  • Hacker News: Vector databases are the wrong abstraction

    Source URL: https://www.timescale.com/blog/vector-databases-are-the-wrong-abstraction/ Source: Hacker News Title: Vector databases are the wrong abstraction Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses the complexities and challenges faced by engineering teams when integrating vector databases into AI systems, particularly in handling embeddings sourced from diverse data. It introduces the concept of a “vectorizer”…

  • Cloud Blog: AI Hypercomputer software updates: Faster training and inference, a new resource hub, and more

    Source URL: https://cloud.google.com/blog/products/compute/updates-to-ai-hypercomputer-software-stack/ Source: Cloud Blog Title: AI Hypercomputer software updates: Faster training and inference, a new resource hub, and more Feedly Summary: The potential of AI has never been greater, and infrastructure plays a foundational role in driving it forward. AI Hypercomputer is our supercomputing architecture based on performance-optimized hardware, open software, and flexible…

  • Hacker News: Probably pay attention to tokenizers

    Source URL: https://cybernetist.com/2024/10/21/you-should-probably-pay-attention-to-tokenizers/ Source: Hacker News Title: Probably pay attention to tokenizers Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text delves into the critical role of tokenization in AI applications, particularly those utilizing Retrieval-Augmented Generation (RAG). It emphasizes how understanding tokenization can significantly affect the performance of AI models, especially in contexts…

  • Hacker News: The PlanetScale vectors public beta

    Source URL: https://planetscale.com/blog/announcing-planetscale-vectors-public-beta Source: Hacker News Title: The PlanetScale vectors public beta Feedly Summary: Comments AI Summary and Description: Yes Summary: PlanetScale has launched an open beta for its vector search and storage capabilities, which integrate with its MySQL database. The new feature allows for the simultaneous management of vector data and relational data, ensuring…

  • Cloud Blog: Beyond the basics: Build real-world gen AI skills with the latest learning paths from Google Cloud

    Source URL: https://cloud.google.com/blog/topics/training-certifications/four-new-gen-ai-learning-paths-on-offer/ Source: Cloud Blog Title: Beyond the basics: Build real-world gen AI skills with the latest learning paths from Google Cloud Feedly Summary: The majority of organizations don’t feel ready for the AI era. In fact, 62% say they don’t have the expertise they need to unlock AI’s full potential.1 As the leader…

  • Simon Willison’s Weblog: Quoting François Chollet

    Source URL: https://simonwillison.net/2024/Oct/16/francois-chollet/ Source: Simon Willison’s Weblog Title: Quoting François Chollet Feedly Summary: A common misconception about Transformers is to believe that they’re a sequence-processing architecture. They’re not. They’re a set-processing architecture. Transformers are 100% order-agnostic (which was the big innovation compared to RNNs, back in late 2016 — you compute the full matrix of…