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Category: Computing & data (Page 1 of 64)

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.

PingCAP unveils TiDB 7.1

PingCAP, a developer of distributed SQL database solutions, announced the release of TiDB 7.1, the newest iteration of its open source product, with enhanced features, improved performance, and greater ease of use. TiDB powers all applications with elastic scaling, real-time analytics, and continuous data. With simplified operations and enhanced MySQL compatibility, users can better meet high customer expectations and accelerate the productivity of developers and infrastructure engineers. TiDB 7.1 allows users to:

  • Stabilize business-critical workloads by giving multi-workload stability control to DB operators and significantly improving tail latencies.
  • Speed up workloads with fewer resources via architectural enhancements for higher throughput, lower storage costs, and reduced scaling time.

TiDB 7.1 also makes generally available multiple enhancements and key features added in prior non-stable releases since TiDB 6.5, including:

  • Multi-tenant UX – Allows operators of TiDB to set resource quotas and priority for different workloads.
  • Added flexibility and speed with a multi-value index – Also known as a JSON index, a multi-value index (supported in MySQL) enables an N:1 mapping of index records to data records.
  • Accelerated Time to Live (TTL) – Better performance and resource utilization by parallelizing across TiDB nodes.
  • Accelerated analytics with late materialization.

https://www.pingcap.com

Datometry partners with Databricks

Datometry, provider of a database virtualization solution, announced their partnership with Databricks to accelerate the transition of enterprises from classic data warehouse technology to the lakehouse. The partnership will help enterprise customers overcome the lock-in of legacy vendors.

Enterprises struggle to move their workloads off legacy data warehouses like Teradata and Oracle. Until now, the way companies moved applications off these legacy systems was to rewrite them and translate the embedded SQL with conversion tools. This approach is not only costly and time-consuming but also error-prone and poses significant risk to an organization.

Datometry has joined the Databricks Technology Partner Program to offer customers a validated integration with Databricks that overcomes the challenges of conventional migrations. With Datometry, enterprises can move their business as-is without having to rewrite or redefine application code.

https://datometry.com

Snowflake acquires Neeva

From the Snowflake blog…

Search is fundamental to how businesses interact with data, and the search experience is evolving rapidly with new conversational paradigms emerging in the way we ask questions and retrieve information, enabled by generative AI. The ability for teams to discover precisely the right data point, data asset, or data insight is critical to maximizing the value of data.

That’s why Snowflake is acquiring Neeva, a search company founded to make search even more intelligent at scale. Neeva created a unique and transformative search experience that leverages generative AI and other innovations to allow users to query and discover data in new ways.

We plan to infuse and leverage these innovations across the Data Cloud to the benefit of our customers, partners and developers. Neeva allows us to tap into some of the most cutting-edge search technologies available to bring search and conversation in Snowflake to a new level.

As part of the acquisition, we are joined by some of the brightest minds working in search today. Neeva’s leadership and team members have been instrumental in the creation of numerous successful products like Google’s search advertising and YouTube monetization.

https://www.snowflake.com/blog/snowflake-acquires-neeva-to-accelerate-search-in-the-data-cloud-through-generative-ai/

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.

https://acquia.com/products/acquia-dam

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.

https://www.elastic.co/enterprise-search/generative-ai

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.

https://firefly.adobe.com

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

https://www.docugami.com/blog/llamaindex

Expert.ai launches AI platform for Life Sciences

Expert.ai announced availability of the expert.ai Platform for Life Sciences. With the expert.ai 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 expert.ai 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.

https://www.expert.ai/

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