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.
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.
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.
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.
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.
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.
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.
An edited version of the announcement from the Microsoft Blog:
Microsoft is teaming up with OpenAI to exclusively license GPT-3 – an autoregressive language model that outputs human-like text. GPT-3 is the largest and most advanced language model in the world, clocking in at 175 billion parameters, and is trained on Azure’s AI supercomputer. This allows us to leverage its technical innovations to develop and deliver advanced AI solutions for our customers, as well as create new solutions that harness the power of advanced natural language generation.
We see this as an opportunity to expand our Azure-powered AI platform in a way that democratizes AI technology, enables new products, services and experiences, and increases the positive impact of AI at Scale. We want to make sure that this AI platform is available to everyone – researchers, entrepreneurs, hobbyists, businesses – to empower their ambitions to create something new and interesting. The scope of commercial and creative potential that can be unlocked through the GPT-3 model is profound, with genuinely novel capabilities – most of which we haven’t even imagined yet. Directly aiding human creativity and ingenuity in areas like writing and composition, describing and summarizing large blocks of long-form data (including code), converting natural language to another language.
Realizing these benefits at true scale – responsibly, affordably and equitably – is going to require more human input and effort than any one large technology company can bring to bear. OpenAI will continue to offer GPT-3 and other powerful models via its own Azure-hosted API, launched in June. While we’ll be hard at work utilizing the capabilities of GPT-3 in our own products, services and experiences to benefit our customers, we’ll also continue to work with OpenAI to keep looking forward: leveraging and democratizing the power of their cutting-edge AI research as they continue on their mission to build safe artificial general intelligence.
ThoughtSpot announced the release of ThoughtSpot Cloud, a fully-managed SaaS offering providing business users with the flexibility to glean instant insights from data in the cloud with search & AI-driven analytics. With ThoughtSpot Cloud, employees can access data across all of their cloud data in a matter of minutes, helping these organizations maximize their investments in cloud data warehouses like Amazon Redshift and Snowflake. Additional features include:
Personalized onboarding: Specific onboarding flows by role tailor the experience for users, accelerating their time to value.
Search assist: Digital assistant that provides a step by step guide for first time users to aid in their initial search.
Prebuilt SpotApps: Reusable low-code templates to make getting insights from a particular application, like Salesforce, simple and scalable.
In-database benefits: Run queries directly in both high-performance, zero-management, built-for-the-cloud data warehouses like Amazon Redshift and Snowflake.
Pricing: Pay only for the data consumed and analyzed, not for the number of users.
