Curated for content, computing, and digital experience professionals

Day: January 17, 2024

DataStax launches Data API to simplify GenAI application development

DataStax, a company that powers generative AI (GenAI) applications with relevant, scalable data, announced the general availability of its Data API, a one-stop API for GenAI, that provides all the data and a complete stack for production GenAI and retrieval augmented generation (RAG) applications with high relevancy and low latency. Also debuting today is a completely updated developer experience for DataStax Astra DB, a vector database for building production-level AI applications. 

The new vector Data API and experience makes Apache Cassandra available to JavaScript, Python, or full-stack application developers in a more intuitive experience for AI development. It is specifically designed for ease of use, offering higher relevancy, throughput, and fast response times, by using the JVector search engine. It introduces an intuitive dashboard, efficient data loading and exploration tools, and seamless integration with AI and machine learning (ML) frameworks. 

Developers can use the Data API for an out-of-the-box AI ecosystem that simplifies integrations with GenAI ecosystem leaders like LangChain, LLamaIndex, OpenAI, Vercel, Google Vertex AI, Amazon Bedrock, GitHub Copilot, Azure, and all major platforms while supporting security and compliance standards. Any developer can now support advanced RAG techniques such as FLARE and ReAct that must synthesize multiple responses.

Gilbane Advisor 1-17-24 — Knowledge editing, Bayesian inference

This week we feature articles from Dhanshree Shripad Shenwai, and Stephanie Shen. 

Additional reading comes from Kyle Wiggers, Caleb Sponheim, Ben Waber & Nathanael J. Fast, and Raphaël Millière & Cameron Buckner.

News comes from Typeface, OpenAI, Argo Translation, and Digimarc.

All previous issues are available at

Opinion / Analysis

A comprehensive study of knowledge editing for large language models

We’re still learning how knowledge graphs and large language models might be combined to reap the benefits of their individual capabilities. Knowledge graph embeddings in LLMs are one approach, but are typically not easily modified (as they are on their own) without re-training. Dhanshree Shripad Shenwai summarizes an interesting new paper on knowledge editing activity in general, and specifically on a new approach to knowledge editing in language models that doesn’t require re-training. (3 min)


Bayesian Inference: A unified framework for perception, reasoning, and decision-making

Stephanie Shen provides an accessible and useful description of what Bayes’ Theorem is and how it works in English (minus the math), and explains why its applicability is much broader than appreciated. A good refresher for those of us not steeped in it, and a good intro for decision-makers. (14 min)

More Reading

All Gilbane Advisor issues

Content technology news

Typeface announces integration within Microsoft Dynamics 365

Dynamics 365 Customer Insights customers get access to Typeface’s brand and audience-personalized AI seamlessly within their environments.

OpenAI introduces ChatGPT Team

OpenAI introduces ChatGPT Team — ChatGPT Team offers access to GPT-4 and DALL·E 3, tools like Advanced Data Analysis, a dedicated collaborative workspace, and admin tools.

Argo Translation launches CMSConnect

Support for multiple CMS, PIM, DAM, and CRM platforms expands their ability to help businesses make content available in numerous languages.

Digimarc offers free digital watermark embedding and detection tools to device and chip manufacturers and content creation platforms

The Secure, Automated, Fair, and Efficient (SAFE) digital watermark tools are designed to protect digital content owners and consumers.

All content technology news

The Gilbane Advisor is authored by Frank Gilbane and is ad-free, cost-free, and curated for content, computing, web, data, and digital experience technology and information professionals. We publish recommended articles and content technology news weekly. We do not sell or share personal data.

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