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Category: Computing & data (Page 3 of 55)

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.

MariaDB and MindsDB collaborate on machine learning

MariaDB Corporation and MindsDB, a provider of in-database machine learning tools, together announced a technology collaboration that makes machine learning predictions easy and accessible to cloud database users.  By using MindsDB in SkySQL, MariaDB’s fully managed cloud database service, data science and data engineering teams can increase their organization’s predictive capabilities to plan for and address business issues. MariaDB database users will now be able to add machine learning based predictions directly into their datasets stored in SkySQL. This simplifies the task of analyzing and predicting future trends, putting machine learning capabilities into the hands of MariaDB users, no matter their role. The use cases for business predictions cut across every business function such as finance, sales, risk analysis, logistics, operations, and marketing.

https://mindsdb.comhttps://mariadb.com/products/skysql/

SimInsights launches no-code XR authoring platform

SimInsights announced general availability (GA) of HyperSkill, a no-code 3D simulation software for Virtual and Augmented Reality and Artificial Intelligence powered training. HyperSkill was created to enable instructional designers and subject matter experts to author immersive, interactive and intelligent training content without having to learn programming or technical skills in machine learning and artificial intelligence. HyperSkill enables non-programmers to author VR/AR/AI-powered content, automatically optimize it and publish it across many devices and audiences and collect and visualize experience data for assessment and evaluation. HyperSkill has been used by customers in healthcare, manufacturing and education and has been developed with their close collaboration and feedback. HyperSkill includes:

  • No-code authoring: Faster and cheaper to author compared to programming with 3D game engines
  • Reusable repository: Growing public repository of XR-ready 3D assets, including virtual environments, virtual persons and virtual objects
  • AI-Powered: Natural Language Processing (NLP) and Computer Vision to simplify authoring, enhance experiences and unlock new use cases
  • Cross Platform: Author once, deliver everywhere, including emerging AR/VR headsets as well as web, desktop and mobile platforms.
  • Multiplayer: Enables synchronous learning scenarios, including collaborative and instructor-led training.

HyperSkill is a SaaS (Software as a Service) product available for a free trial.

https://siminsights.com/hyperskill/

Deepnote data notebook comes out of beta

Deepnote, an early-stage startup backed by Accel and Index Ventures, launched version 1.0, opening up to the general availability of collaborative data science notebooks to data teams. Data team efficacy relies on the process of access to, exploration of, and collaboration around data—for example, when an organization needs to make a data-informed decision, it will rely on data teams to explore datasets and share insights that lead to action. This process is siloed within a single department, findings are inconsistent, and insights quickly become out of date. Deepnote makes data collaboration a reality improving three pain points of traditional data science notebooks:

  1. Collaboration: Sharing analysis and collaboration is as easy as sending a link because everything is hosted in a fully-managed cloud environment. Analysis is done in real-time with multiplayer mode if needed. And everything is organized and hosted in a single place.
  2. Connectivity: With dozens of native integrations to tools in the modern data stack—Snowflake, BigQuery, Postgres, S3, GitHub—data teams can seamlessly connect to the tools they’re already using.
  3. Productivity: Underserved data analysts and scientists are now equipped with productivity features—reproducibility, autocomplete, scheduling, version control—to do better work in less time.

https://deepnote.com/

Ontotext gets growth funding to meet demand for graph technology

Ontotext, an enterprise knowledge graph technology and semantic database engine provider, announced that Integral Venture Partners (Integral), a capital investment firm, announced this week that an Integral–led investment consortium has entered into a definitive agreement with our mother company Sirma Group Holding, to acquire Ontotext as a global supplier of a deep-tech enterprise software, operating in the graph databases space and the Artificial Intelligence market. The Integral-led international investment consortium also includes PortfoLion Capital Partners, the venture capital and private equity arm of OTP Bank, and Carpathian Partners, a specialized technology-focused investment platform based in London. The Consortium’s investment in excess of €30 million will be structured as a combination of a capital increase and a secondary share purchase. The transaction is not subject to any regulatory approvals and is expected to close by August 2022.

Supported by new capital, Ontotext will accelerate its international expansion and go-to-market operations, focusing on the US market. We will invest in further development of our vertical product stack — end-to-end solutions for specific industries starting with Life Sciences and Financial Services. Last but not least, we will further strengthen our position as global provider of knowledge graph technology.

https://www.ontotext.com

Komprise automates unstructured data discovery with Smart Data Workflows

Komprise announced Komprise Smart Data Workflows, a systematic process to discover relevant file and object data across cloud, edge and on-premises datacenters and feed data in native format to AI and machine learning (ML) tools and data lakes.

Komprise has expanded Deep Analytics Actions to include copy and confine operations based on Deep Analytics queries, added the ability to execute external functions such as running natural language processing functions via API and expanded global tagging and search to support these workflows. Komprise Smart Data Workflows allow you to define and execute a process with as many of these steps needed in any sequence, including external functions at the edge, datacenter or cloud. Komprise Global File Index and Smart Data Workflows together reduce the time it takes to find, enrich and move the right unstructured data. Komprise Smart Data Workflows are relevant across many sectors. Here’s an example from the pharmaceutical industry.

https://www.komprise.com/komprise-automates-unstructured-data-discovery-with-smart-data-workflows/

Relationship extraction with co-reference added to Finch for Text

Finch Computing, developers of real-time natural language processing solution Finch for Text, announced that it has added relationship extraction and co-references to the product. Relationship extraction gives users an ability to decipher relationships between entities, and co-reference enables words like “her” or “him” or “the leader” appearing in text to be resolved to a specific, named entity.

Finch for Text can now find important relationships between entities such as: Acquired-by, Co-Investor-with, Competitor-with, Customer-of, Director-of, Educated-at, Employer-of, Founder-of, Invested-in, Organization-Location, Owner-of, Partner-of, Person-Location, Relative-of, and Subsidiary-of. For companies and people in particular, understanding these connections helps users perform faster, richer and deeper analysis.

Entity co-reference refers to the ability to resolve otherwise obscure references to an entity – like her, him, the company, the product – to a disambiguated entity. The value of this capability is that it helps users understand all mentions of an entity even if that mention isn’t by name. It improves salience scores because the product can better gauge how much an article is about a given entity. It also improves sentiment scores with more mentions to analyze, and the same is true for relationship extraction – there are more relationships discovered because there are more mentions linked to an entity.

https://finchcomputing.com/2022/05/19/finch-computing-adds-spanish-german-language-support-to-its-finch-for-text-product-2/

Franz releases AllegroGraph 7.3

Franz Inc., an early innovator in Artificial Intelligence (AI) and supplier of Graph Database technology for Entity-Event Knowledge Graph Solutions, announced AllegroGraph 7.3, with enhanced GraphQL query capabilities for distributed Knowledge Graphs and Enterprise Data Fabrics. With AllegroGraph’s GraphQL APIs, developers can create performant and more complex data-driven applications. GraphQL’s capability to fetch the exact and specific data in a single request delivers flexibility to Knowledge Graph developers.

AllegroGraph’s GraphQL Support GraphQL is an open-source data query language for APIs and a runtime for fulfilling queries with data. It allows API clients to query data as a graph irrespective of how the data is stored, making it possible to loosely couple data sources with client applications. GraphQL provides a complete and understandable description of the data in the API, gives clients the power to ask for exactly what they need and nothing more, and makes it easier to evolve APIs over time. Using GraphQL APIs within AllegroGraph can lower integration costs and minimize redundancy in enterprise systems, while improving the value of data-driven applications. AllegroGraph 7.3 is immediately available directly from Franz Inc.

https://franz.comhttps://allegrograph.com

Google Translate learns 24 new languages

From the Google Products Blog…

… today we’ve added 24 languages to Translate, now supporting a total of 133 used around the globe.

Over 300 million people speak these newly added languages — like Mizo, used by around 800,000 people in the far northeast of India, and Lingala, used by over 45 million people across Central Africa. As part of this update, Indigenous languages of the Americas (Quechua, Guarani and Aymara) and an English dialect (Sierra Leonean Krio) have also been added to Translate for the first time.

This is also a technical milestone for Google Translate. These are the first languages we’ve added using Zero-Shot Machine Translation, where a machine learning model only sees monolingual text — meaning, it learns to translate into another language without ever seeing an example. While this technology is impressive, it isn’t perfect. And we’ll keep improving these models to deliver the same experience you’re used to with a Spanish or German translation, for example. If you want to dig into the technical details, check out our Google AI blog post and research paper.

https://blog.google/products/translate/24-new-languages/

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