Curated for content, computing, and digital experience professionals

Category: Computing & data (Page 2 of 79)

Computing and data is a broad category. Our coverage of computing is largely limited to software, and we are mostly focused on unstructured data, semi-structured data, or mixed data that includes structured data.

Topics include computing platforms, analytics, data science, data modeling, database technologies, machine learning / AI, Internet of Things (IoT), blockchain, augmented reality, bots, programming languages, natural language processing applications such as machine translation, and knowledge graphs.

Related categories: Semantic technologies, Web technologies & information standards, and Internet and platforms.

Adobe updates Acrobat with GenAI and chat across document types

Adobe announced Acrobat customers can now create and edit images in PDFs using text prompts, powered by Adobe Firefly, along with new features in Acrobat AI Assistant integrated into Reader and Acrobat workflows that allow customers to ask questions, get insights, and create content from information across groups of PDFs as well as other document types.

The new Acrobat features enable customers to easily enhance existing images and generate new images in their PDFs with generative AI without leaving the app. Features include: 

  • Edit Image in Acrobat offers easy-to-use tools like the Firefly-powered Generative Fill, Remove Background, Erase and Crop to add, remove and revitalize content in document images in a snap. From erasing unwanted objects to removing backgrounds or adding new images, customers have full, easy control over the visuals in their PDFs.
  • With Generate Image in Acrobat, customers can add new images to their PDFs, powered by Adobe’s Firefly Image 3 Model. Customers can quickly adjust the size and style and add images to any part of a document.

Adobe is offering free, full access to all Acrobat AI Assistant features June 18 – June 28. Early access pricing for the AI Assistant add-on subscription starts at US$4.99 per month.

Contentstack updates personalization tools

Contentstack, a creator of the Headless CMS and the Composable DXP category, announced a new solution to address personalization challenges for digital marketers: an overreliance on IT, generic AI content, and manual processes that impede scale. Contentstack’s solution includes new features that are integrated within the headless CMS as well as “no-fail promise” customer support services:

  • Personalize – An A/B/n testing and segmentation engine that removes obstacles tied to the implementation and operationalization of personalized content.
  • Brand Kit– A writing assistant that produces brand-relevant, AI-generated content at scale to align with the brand’s style, messaging, and defined Voice Profiles.
  • New extensions for Contentstack Automate – Teams can now develop fully automated sequences to address the complete lifecycle of personalized content and experiences.
  • Expanded Academy and new AI Accelerator program– Additional tools, training, and support to help achieve success on practical AI use cases within weeks.

Brand Kit sits on top of AI Assistant, the native LLM launched last year. It allows marketers to create high-quality, AI-generated content specific to the brand across all channels and platforms. The process is also fully automated via new AI connectors within Contentstack Automate.

Snowflake announces enhancements to Snowflake Cortex AI, Snowflake ML, and more

Snowflake announced new innovations and enhancements to Snowflake Cortex AI to unlock the next wave of enterprise AI for customers to create AI-powered applications. This includes new chat experiences, which help organizations develop chatbots so they can talk directly to their enterprise data and get the answers they need faster. In addition, Snowflake is democratizing how any user can customize AI for specific industry use cases through a new no-code interactive interface, access to large language models (LLMs), and serverless fine-tunings. Snowflake is also accelerating the path for operationalizing models with an integrated experience for machine learning (ML) through Snowflake ML, enabling developers to build, discover, and govern models and features across the ML lifecycle. Snowflake’s unified platform for generative AI and ML allows every part of the business to extract value from their data.

Snowflake is unveiling two new chat capabilities, Snowflake Cortex Analyst and Snowflake Cortex Search, allowing users to develop these chatbots in a matter of minutes against structured and unstructured data, without operational complexity. Cortex Analyst, built with Meta’s Llama 3 and Mistral Large models, allows businesses to build applications on top of their analytical data in Snowflake. Other announced enhancements include Snowflake Copilot, Cortex Guard, Document AI, and Hybrid Tables.

Databricks to acquire Tabular

Databricks, a Data and AI company, announced it has agreed to acquire Tabular, a data management company founded by Ryan Blue, Daniel Weeks, and Jason Reid. By bringing together the original creators of Apache Iceberg and Linux Foundation Delta Lake, the two leading open source lakehouse formats, organizations are no longer limited by which of these formats their data is in. Databricks intends to work closely with the Delta Lake and Iceberg communities to bring format compatibility to the lakehouse; in the short term, inside Delta Lake UniForm and in the long term, by evolving toward a single, open, and common standard of interoperability. Databricks and Tabular will work together towards a joint vision of the open lakehouse.

Databricks will work with the Delta Lake and Iceberg communities to bring data interoperability to the formats over time. This is a long journey, one that will likely take several years to achieve in those communities. That is why last year, Databricks introduced Delta Lake UniForm. UniForm tables provide interoperability across Delta Lake, Iceberg, and Hudi, and support the Iceberg restful catalog interface so companies can use the analytics engines and tools they are already familiar with.

Ontotext announces Metadata Studio 3.8

Ontotext, a provider of enterprise knowledge graph and semantic database engines, announced the latest version of Ontotext Metadata Studio (OMDS), a tool designed for knowledge graph enrichment through text analytics of unstructured documents. Version 3.8 aids in the creation, evaluation, and quality improvement of text analytics services. With more intuitive and effective search solution capabilities, enhancement to OMDS removes the difficulties users face when exposing semantic search over their documents, especially when they are working with their own, custom reference domain models. Updates include:

  • Enhanced Domain Model Search Interface transforms the reference annotation schema into a user-friendly search interface, allowing exploration and retrieval of content based on the preferred domain data model.
  • Knowledge Graph Enrichment and Extension enables users to reuse their domain models so they can be leveraged for advanced analytics and quality management.
  • Advanced Search Capabilities supports all types of searches. The solution allows users to conduct simple searches such as identifying documents containing specific text as well as complex queries that filter documents based on the presence or absence of certain text and combinations of metadata objects and property values.
  • Improved Usability and Workflow Efficiency enables users to organize content effortlessly by moving documents between corpora or deleting them from the database.

Perplexity introduces Perplexity Pages

Snippets from the Perplexity blog…

You’ve used Perplexity to search for answers, explore new topics, and expand your knowledge. Now, it’s time to share what you learned. Meet Perplexity Pages, your new tool for easily transforming research into visually stunning, comprehensive content. Whether you’re crafting in-depth articles, detailed reports, or informative guides, Pages streamlines the process so you can focus on sharing your knowledge with the world.

Pages lets you effortlessly create, organize, and share information. Search any topic, and instantly receive a well-structured, beautifully formatted article. Publish your work to our growing library of user-generated content and share it directly with your audience with a single click. What sets Perplexity Pages apart?

  • Customizable: Tailor the tone of your Page to resonate with your target audience, whether you’re writing for general readers or subject matter experts.
  • Adaptable: Easily modify the structure of your article—add, rearrange, or remove sections to best suit your material and engage your readers.
  • Visual: Elevate your articles with visuals generated by Pages, uploaded from your personal collection, or sourced online.

Pages is rolling out to users now. Log in to your Perplexity account and select “Create a Page” in the library tab.

Sinequa releases new generative AI assistants

Sinequa announced the availability of Sinequa Assistants; enterprise generative AI assistants that integrate with enterprise content and applications to augment and transform knowledge work. Sinequa’s Neural Search complements GenAI and provides the foundation for Sinequa’s Assistants. Its capabilities go beyond RAG’s conventional search-and-summarize paradigm to intelligently execute complex, multi-step activities, all grounded in facts to augment the way employees work.

Sinequa’s Assistants leverage all company content and knowledge to generate contextually-relevant insights and recommendations. Optimized for scale with three custom-trained small language models (SLMs), Sinequa Assistants help ensure accurate conversational responses on any internal topic, complete with citations and traceability to the original source.

Sinequa Assistants work with any public or private generative LLM, including Cohere, OpenAI, Google Gemini, Microsoft Azure Open AI, and Mistral. The Sinequa Assistant framework includes ready-to-go Assistants along with tools to define custom Assistant workflows so that customers can use an Assistant out of the box, or tailor and manage multiple Assistants from a single platform. These Assistants can be tailored to fit the needs of specific business scenarios and deployed and updated quickly without code or additional infrastructure. Domain-specific assistants scientists, engineers, lawyers, financial asset managers and others are available. launches secure unstructured data lakehouse for LLMs launched a secure data lakehouse for LLMs, Tonic Textual, to enable AI developers to securely leverage unstructured data for retrieval-augmented generation (RAG) systems and large language model (LLM) fine-tuning. Tonic Textual is a data platform designed to eliminate integration and privacy challenges ahead of RAG ingestion or LLM training bottlenecks. Leveraging its expertise in data management and realistic synthesis, has developed a solution to tame and protect siloed, messy, and complex unstructured data into AI-ready formats ahead of embedding, fine-tuning, or vector database ingestion. With Tonic Textual: 

  1. Build, schedule, and automate unstructured data pipelines that extract and transform data into a standardized format convenient for embedding, ingesting into a vector database, or pre-training and fine-tuning LLMs. Textual supports TXT, PDF, CSV, TIFF, JPG, PNG, JSON, DOCX and XLSX out-of-the-box.
  2. Detect, classify, and redact sensitive information in unstructured data, and re-seed redactions with synthetic data to maintain the semantic meaning. Textual leverages proprietary named entity recognition (NER) models trained on a diverse data set spanning domains, formats, and contexts to ensure sensitive data is identified and protected.
  3. Enrich your vector database with document metadata and contextual entity tags to improve retrieval speed and context relevance in RAG systems.

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