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Category: Computing & data (Page 69 of 93)

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.

Dstl releases free Baleen 3 data processing update

The Defence Science and Technology Laboratory (Dstl) has released a new free version of its popular data processing tool. Baleen 3 is a tool for building data processing pipelines using the open source Annot8 framework and succeeds Baleen 2, one of the first open source projects by Dstl, the science inside UK defence and security. It offers users the ability to search, process and collate data, and is suitable for personal and commercial applications. It has been used across government, and by industry and academia, and also internationally as well as in the UK.

The tool enables the creation of a bespoke chain of “processors” to extract information from unstructured data (e.g. text documents, images). For example, Baleen 3 could process a folder with thousands of Word Documents and PDFs in it to extract all e-mail addresses and phone numbers in those documents and store them in a database. As well as text, Baleen 3 can also find and extract images within those documents, perform OCR to find text within those images, translate that text into English, and then run machine learning models to find mentions of People within those images. Baleen 3 supports components developed within the Annot8 framework, and as a result it is easy to extend and develop further to cover new use cases and provide additional functionality. There are already a large number of components available for use within the Annot8 framework, including some previously developed by Dstl.

Following the release of Baleen 3, support for the existing Baleen 2 project will be withdrawn. Dstl is encouraging all users to move to using Baleen 3 where possible. Baleen 3 is built on top of newer technologies, and will be easier to maintain and deploy as a result of the upgrade. It also extends Baleen 2’s focus on text to support other forms of unstructured data, such as images. Baleen 3 is available to download now.

https://github.com/dstl/baleen

Expert System announces rebrand to Expert.ai

With more than 20 years’ industry experience in the Artificial Intelligence market, Expert System has rebranded to become expert.ai. This effort highlights the company’s vision to redefine what is possible in extracting value from language to make the most of information. As the AI market evolves, there is a growing demand from organizations to easily transform their information into knowledge and insight for better decision making with speed and accuracy. As part of this go to market transition, expert.ai is introducing a new logo, new corporate image and new website. The rebrand follows the rollout of the company’s 2020-2024 strategic plan which aims to accelerate global growth and capitalize on the flourishing artificial intelligence market and a newly released cloud-based Natural Language API.

https://expert.ai

Exasol and Pyramid Analytics partner

Exasol, the high-performance analytics database, announced a new strategic partnership with Pyramid Analytics, provider of an analytics platform for the enterprise. Together, Exasol and Pyramid will bring an enhanced analytics experience to their customers, allowing business users and IT teams to become more agile and make the most of their data. Both technologies can be deployed together anywhere—on-premises, in the cloud, and in hybrid architectures. Joint customers will be able to execute complex business calculations quickly and at scale, regardless of data volumes, environment of choice, or number of users.

Pyramid’s calculation engine works natively on Exasol via a direct connection, so no data is ever extracted or duplicated. This offers security and performance advantages, while simultaneously enabling governed self-service analytics through Pyramid’s business user friendly interface. In addition, Pyramid allows business analysts to easily load external data sets into Exasol where they can be combined with existing Exasol tables to fuel machine learning models.

https://www.exasol.com/, https://www.pyramidanalytics.com

SmartBear announces integration of ReadyAPI and SwaggerHub

SmartBear, provider of software development and quality tools, has integrated its API quality platform, ReadyAPI, with SwaggerHub, its API design and documentation platform. ReadyAPI users are now able to setup and manage a connection in their SwaggerHub account within ReadyAPI, search and filter through definitions in SwaggerHub, and more. Together, ReadyAPI and SwaggerHub provide the a comprehensive API tooling for all stages of the API lifecycle to help ensure high quality software. The integration streamlines the process of logging in to SwaggerHub from ReadyAPI as well as storing information for re-use when performing actions against SwaggerHub.

To highlight the interconnectedness of SmartBear API solutions, the three modules of the ReadyAPI platform have been renamed under the ReadyAPI brand. SoapUI Pro, the API testing tool, is now ReadyAPI Test, reflecting that ReadyAPI supports RESTful, GraphQL, and other API standards in addition to SOAP. LoadUI Pro, the way to API load test, is now ReadyAPI Performance. ServiceV Pro, which allows anyone in the delivery pipeline to create mocking and service virtualization, is now ReadyAPI Virtualization. The name SoapUI, the open source solution that SmartBear supports, will remain unchanged. The consolidation under the ReadyAPI brand ensures these three API tools are delivered in one centralized, intuitive platform. From planning and designing APIs to developing, testing and deploying, Agile and DevOps teams can ensure ReadyAPI and SwaggerHub are meeting their needs when building APIs.

https://smartbear.com/

DataStax announces DataStax Fast 100 program

DataStax announced the DataStax Fast 100 program to quickly align Apache Cassandra experts to enterprise projects. Cassandra is an open-source, NoSQL database. The partners currently on-board, with more to come, include: Deloitte, Expero Inc, and Anant Corporation. The program enables swift enablement for partners with consultants certified and ready to deliver on Cassandra engagements within 30-days.

Practitioners cite a lack of skilled staff as the top obstacle to Cassandra adoption. When asked what it would take for practitioners to use Cassandra for more applications and features in production, they said “easier to migrate” and “easier to integrate.” The DataStax Fast 100 aligns prequalified partners to enterprises to help ensure their success. The program will help enterprises with business modernization, technical migrations, cloud-native data platforms, and various mission-critical use cases.

https://www.datastax.com/partners/datastax-fast-100

Cloudflare announces privacy-first Web Analytics for all website owners

Cloudflare, Inc. launched Cloudflare Web Analytics to provide accurate, clear, and free analytics for anyone who cares about how their site is performing and doesn’t want to sacrifice their visitors’ privacy. Cloudflare Web Analytics is built on top of Cloudflare’s existing network, giving site owners insight into key traffic metrics at the edge. Now site owners have control over their own site data, eliminating the need for third-party scripts that can track their users and help retarget them with advertising. Cloudflare Web Analytics will be available, for free, to any website owner, whether they are an existing Cloudflare customer or not.

Unlike ad-supporting analytics companies, Cloudflare’s business model has never been about tracking individual users across the web. Cloudflare does not track where visitors are going online, and can help web owners get clear and accurate information about how their sites are performing without the need to profile users. Cloudflare already processes the requests for sites on its network and can collect analytics at the edge without adding third-party analytics scripts to a website. This privacy-friendly approach measures a ‘visit’ by looking at the source of each request, rather than tracking individual user behavior. Cloudflare Web Analytics also does not conflict with ad blockers, which frequently block third-party analytics tools from measuring anything at all. When combined with Cloudflare’s Bot Management tool, automated bot traffic that could skew analytics is also filtered out for improved accuracy.

Cloudflare paid customers can enjoy Web Analytics today. In the coming months, Cloudflare Web Analytics will be available to any website owner including those not on Cloudflare’s network. If you are not an existing Cloudflare customer on a paid plan, you can add your name to the waitlist.

https://www.cloudflare.com/web-analytics/

Vectorspace AI & CERN create Natural Language Processing datasets

Vectorspace AI and CERN, the European Organization for Nuclear Research and the largest particle physics laboratory in the world, are creating datasets used to detect hidden relationships between particles which have broad implications across multiple industries. These datasets can provide a significant increase in precision, accuracy, signal or alpha and for any company in any industry. Datasets are algorithmically generated based on formal Natural Language Processing/Understanding (NLP/NLU) models including OpenAI’s GPT-3, Google’s BERT along with word2vec and other models which were built on top of vector space applications at Lawrence Berkeley National Laboratory and the US Dept. of Energy (DOE). Over 100 billion different datasets are available based on customized data sources, rows, columns or language models.

For commercial use, datasets are $0.99c per minute/update and $0.99c per data source, row, column and context with additional configurations and options available on a case by case SaaS/DaaS based monthly subscription. Over 100 billion unique and powerful datasets are available based on customized data sources, rows, columns or language models.

While data can be viewed as unrefined crude oil, Vectorspace AI produces datasets which are the refined ‘gasoline’ powering all Artificial Intelligence (AI) and Machine Learning (ML) systems. Datasets are real-time and designed to augment or append to existing proprietary datasets such as gene expression datasets in life sciences or time-series datasets in the financial markets. Example customer and industry use cases include:

Particle Physics: Rows are particles. Columns are properties. Used to predict hidden relationships between particles.

Life Sciences: Rows are infectious diseases. Columns are approved drug compounds. Used to predict which approved drug compounds might be repurposed to fight an infectious disease such as COVID19. Applications include processing 1500 peer reviewed scientific papers every 24hrs for real-time dataset production.

Financial Markets: Rows are equities. Columns are themes or global events. Used to predict hidden relationships between equities and global events. Applications include thematic investing and smart basket generation and visualization.

Data provenance, governance and security are addressed via the Dataset Pipeline Processing (DPP) hash blockchain and VXV utility token integration. Datasets are accessed via the VXV wallet-enable API where VXV is acquired and used as a utility token credit which trades on a cryptocurrency exchange.

https://vectorspace.ai

Microsoft announces SharePoint Syntex

From the Microsoft Project Cortex blog:

Microsoft announced SharePoint Syntex, the first product from Project Cortex. SharePoint Syntex uses advanced AI and machine teaching to amplify human expertise, automate content processing, and transform content into knowledge, and will be available to purchase for all Microsoft 365 commercial customers on October 1, 2020.

Machine teaching accelerates the creation of AI models by acquiring knowledge from people rather than from large datasets alone. Any information processing skill, that an expert can teach a human, should be easily teachable to a machine. SharePoint Syntex mainstreams machine teaching, enabling your experts to capture their knowledge about content in AI models they can build with no code. Your experts train SharePoint Syntex to understand content like they do, to recognize key information, and to tag content automatically. For example, a contract processing expert can teach SharePoint Syntex to extract the contract’s value, along with the expiration date and key terms and conditions.

SharePoint Syntex then uses your models to automate the capture, ingestion, and categorization of content, extracting valuable information as metadata. Metadata is critical to managing content, and seamless integration with Microsoft Search, Power Automate, and Microsoft Information Protection enable you to improve knowledge discovery and reuse, accelerate processes, and dynamically apply information protection and compliance policies.

SharePoint Syntex content center
Syntex introduces a new experience for managing content at scale, integrating metadata and workflow, and delivering compliance automation – the content center. Content centers supply capabilities to teach the cloud how to read and process documents the same way you would manually. SharePoint Syntex uses those insights to automatically recognize content, extract important information, and apply metadata tags. SharePoint Syntex uses advanced AI to automate the capture, ingestion, and categorization of content, to accelerate processes, improve compliance, and facilitate knowledge discovery and reuse. SharePoint Syntex mainstreams AI to process three major types of content: digital images, structured or semi-structured forms, and unstructured documents.

Digital image processing
SharePoint Syntex can automatically tag images using a new visual dictionary with thousands of commonly recognized objects. In addition, SharePoint Syntex can recognize convert extracted handwritten text into tags for search and further processing.

Document understanding
Most organizations generate vast amounts of unstructured documents such as manuals, contracts, or resumes. You can teach SharePoint Syntex to read your content the way you would using machine teaching to build AI models with no code. SharePoint Syntex can automatically suggest or create metadata, invoke custom Power Automate workflows, and attach compliance labels to enforce retention or record management policies. Document understanding models are based on Language Understanding models in Azure Cognitive Services.

Form processing
SharePoint Syntex includes a powerful form processing engine, based on AI Builder, that lets you automatically recognize and extract common values from semi structured or structured documents, such as dates, figures, names, or addresses. These models are built with no code and only require a small number of documents for reliable results.

https://techcommunity.microsoft.com/t5/project-cortex-blog/announcing-sharepoint-syntex/ba-p/1681139

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