Curated for content, computing, and digital experience professionals

Year: 2023 (Page 1 of 11)

Acquia updates Acquia DAM

Digital experience company Acquia announced enhancements to its digital asset management platform, Acquia DAM (Widen), including an artificial intelligence (AI) chatbot to assist in creative workflows. The capability enhances creative collaboration across content and creative teams with an always-ready sounding board and idea generator.

AI Assistant is integrated into the comments functionality of the Acquia DAM review and proofing tool, Workflow. Using it, anyone reviewing a content proof can ask the AI assistant a question in a conversational way and get a response in seconds to help spur creativity. Examples include getting copy suggestions to improve the written aspect of a project, requesting design suggestions, getting suggestions for visuals such as images or videos, receiving suggestions based on audience segmentation such as interests or behavior, or analyzing competitors’ content to help ensure differentiation.

Acquia also released new integrations for Acquia DAM to streamline collaboration across content and marketing teams and extend the value of their content across their martech stacks. These include: Canva, Jira, Dropbox, Marq, and Salesforce.

Gilbane Advisor 5-24-23 — Fine-tuning LLMs, implementing Live Activities

This week we feature articles from Skanda Vivek, and Alexander Savard.

Additional reading comes from Dean Allemang, Neil Clarke, Sridhahr Ramaswamy & Vivek Raghunathan, and Bogdan Arsintescu.

News comes from Ontotext, Expert[.]ai, Sinequa, and TransPerfect & Contentstack.

All previous issues are available at

Opinion / Analysis

When should you fine-tune LLMs?

Last week we led with an article looking at combining knowledge graphs with large language models to create high-value domain specific applications. This post from Skanda Vivek looks at another decision to make when building a domain specific application: whether to use a proprietary LLM, such as ChatGPT, fine-tune an open-source LLM, or train an LLM from scratch. Vivek includes cost considerations, but also includes a link to a post with detailed hosting costs. (7 min)

That little island changes everything

A really interesting case study from Lyft on implementing Apple’s Dynamic Island and Live Activities for a better user experience. Alexander Savard takes you through the design decisions. (10 min)

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Content technology news

Sinequa enhances platform for scientific search and clinical trial data

Combines deep learning and large language models for natural language understanding (NLU) with ChatGPT models through Azure OpenAI Service.Tridion unites web content, structured content and headless delivery

Expert[.]ai launches AI platform for Life Sciences

The Platform combines industry language models and AI-based natural language capabilities transforming health and scientific data into insights.

TransPerfect launches GlobalLink Connect app for Contentstack’s DXP

The GlobalLink Connect app is now available on the Contentstack Marketplace, part of Contentstack’s Composable Digital Experience Platform (DXP).

Ontotext releases Target Discovery​

Platform provides pharma & biotech companies more efficient insight discovery, faster information retrieval, and advanced visual analytics.

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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Elastic unveils the Elasticsearch Relevance Engine

Elastic announced the launch of the Elasticsearch Relevance Engine (ESRE), with built-in vector search and transformer models, which is designed to bring AI innovation to proprietary enterprise data. ESRE enables companies create secure deployments to take advantage of all their proprietary structured and unstructured data.

Elastic has made investments in foundational AI capabilities to democratize AI and machine learning for developers with a Unified APIs for vector search, BM25f search and hybrid search, plus a transformer model small enough to fit on a laptop’s memory.

Using a relevance engine, like ESRE, allows companies to take advantage of all of their structured and unstructured data to build custom generative AI (GAI) apps, without having to worry about the size and cost of running large language models. The ability to “bring your own” transformer model and integrate with third-party transformer models allows organizations to create secure deployments that leverage GAI on their specific business data. With ESRE, the companies and community of users that have invested in Elastic solutions can advance AI initiatives right now without a lot of additional resources.

Adobe unveils Generative Fill for Photoshop

Adobe unveiled Generative Fill in Photoshop, bringing Adobe Firefly generative AI capabilities directly into design workflows. The new Firefly-powered Generative Fill giving users a new way to work by easily adding, extending or removing content from images non-destructively using simple text prompts. This beta release of Photoshop is Adobe’s first Creative Cloud application to deeply integrate Firefly. Adobe plans to incorporate Firefly across Creative Cloud, Document Cloud, Experience Cloud and Adobe Express.

Generative Fill automatically matches perspective, lighting and style of images to enable users achieve results while reducing tedious tasks. Generative Fill expands creative expression and productivity and enhances creative confidence of creators with the use of natural language and concepts to generate digital content.

Photoshop’s Generative Fill feature is available in the desktop beta app today and will be generally available in the second half of 2023. Generative Fill is also available today as a module within the Firefly beta app for users interested in testing the new capabilities on the web.

Docugami announces integration with LlamaIndex

Docugami, a document engineering company that transforms how businesses create and execute critical business documents, announced an initial integration of LlamaIndex with Docugami, via the Llama Hub.

The LlamaIndex framework provides a flexible interface between a user’s information and Large Language Models (LLMs). Coupling LlamaIndex with Docugami’s ability to generate a Document XML Knowledge Graph representation of long-form Business Documents opens opportunities for LlamaIndex developers to build LLM applications that connect users to their own Business Documents, without being limited by document size or context window restrictions.

General purpose LLMs alone cannot deliver the accuracy needed for business, financial, legal, and scientific settings because they are trained on the public internet, which introduces a wide range of irrelevant and low-quality source materials. By contrast, Docugami is trained exclusively for business scenarios, for greater accuracy and reliability.

Systems aiming to understand the content of documents, such as retrieval and question-answering, will benefit from Docugami’s semantic Document XML Knowledge Graph Representation. Our unique approach to document chunking allows for better understanding and processing of your documents

Gilbane Advisor 5-17-23 — LLMs KGs & DBs, chatbot UIs

This week we feature articles from Dean Allemang, and Tim Neusesser & Evan Sunwall.

Additional reading comes from Amelia Wattenburger, Google I/O presenters, and Allen Institute staff.

News comes from Tridion, Adobe & Google, MindsDB & Nixtla, and Algolia.

All previous issues are available at

Opinion / Analysis

AI’s Woolf at the door — LLMs and knowledge graphs

The combination of Large Language Models with other sources of data and knowledge has enormous potential for new, and more powerful, applications across domains. Most of the examples we’ve looked at in the last few weeks involve knowledge graphs and LLMs, which seem like particularly promising partners. Dean Allemang reports from last week’s Knowledge Graph Conference that there is a lot of enthusiasm for such a marriage. But Allemang wants more evidence that knowledge graphs are in fact a better match for LLMs than other types of databases. His article is a good place to start for anyone who has the same question. (8 min)

Error-message guidelines

Who among us hasn’t been occasionally baffled by error messages that aren’t the least bit helpful? This is one of the most frustrating user experience failures. Tim Neusesser and Evan Sunwall describe what designers should watch out for, and review good and bad error-message examples (including one they like from ChatGPT – see the first item in the list just below for more on chatbot UIs.) (7 min)

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Content technology news

Tridion unites web content, structured content and headless delivery

Includes Tridion Docs 15, Tridion Sites 10, updated Tridion Dynamic Experience Delivery, and embedded Fonto for structured content authoring.

Adobe and Google integrating Firefly and Bard

Adobe will use the Content Authenticity Initiative’s (CAI) open-source Content Credentials technology for transparency to images generated.

MindsDB and Nixtla enhance time-series forecasting

The integration allows developers using MindsDB to build AI forecasting capabilities and anomaly detection in the database without extensive code. ■‍

Algolia launches AI-powered Algolia NeuralSearch

NeuralSearch uses Large Language Models (LLM) and Algolia’s Neural Hashing for hyper-scale, and constantly learns from user interactions.

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.

Subscribe | View online | Editorial policy | Privacy policy | Contact launches AI platform for Life Sciences announced availability of the Platform for Life Sciences. With the Platform for Life Sciences, teams can access advanced natural language understanding capabilities, learning methodologies, 3rd-party large language models like BioBert and Bio-GPT as well as customizable pre-built knowledge models to build custom solutions.

Through a hybrid AI approach combining natural language tools, enterprise language models and machine learning, the Platform for Life Sciences shifts the way unstructured medical and scientific data is monitored, understood, analyzed and collated. Teams can access knowledge and insights trapped in medical articles, reports, press releases, clinical research, customer/patient interactions, consent forms, etc. as well as up-to-date knowledge available based on standards like MeSH, UMLS Conditions & Interventions and IUPAR. Pharmaceutical and Life Sciences teams can:

  • Confirm scientific claims against trusted public and private knowledge sources;
  • Extract connections between biomedical entities in literature for in-depth causality analysis to support researchers; 
  • Monitor clinical trials and social media sources filtered by any combination of indication, drug, mechanism of action, sponsor, or geography to gain insight for clinical trials; 
  • Accelerate the quality control process of clinical and preclinical reports analysis using sensitive and proprietary data sources prior to their submission to regulatory bodies.

Ontotext releases Target Discovery

Ontotext announced the release of Target Discovery, an AI-powered platform that speeds the process of discovering new safe and efficient drug candidates. Target Discovery combines knowledge from public and proprietary data, AI-derived data from scientific publications, patents and clinical trials. It also features analytics for target identification and selection that medical or scientific experts without technical skills. Knowledge graph technology can lower the cost and shorten the time for semantic data integration. It also brings in a new level of insights on top of highly connected data and provides normalized quality data for supporting AI analysis. Benefits include:

  • Target Discovery stays up-to-date with the newest discoveries by automatically extracting knowledge from more than 80 million documents, including patents and clinical trials.
  • Target Discovery combines all required data, whether public or proprietary, AI-derived or structured in one place. Information is updated regularly and includes a selections from AlphaFold, Open Targets, EMBL.
  • One can quickly gain an overview of a disease or target with customizable visual analytics and dashboards over any type of data and source.
  • Hidden relationships can be easily uncovered in a network of over 5 billion facts with advanced graph algorithms.
  • Transparent insight provenance and evidence.

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