MongoDB, Inc. announced the general availability of MongoDB Realm Sync, a fully managed service which syncs data between the Realm mobile database and MongoDB Atlas. This new solution addresses the unique technical challenges of mobile and offline-first development, allowing organizations to rapidly build responsive applications for their customers and remote workforces. Realm Sync will enable teams to take advantage of bidirectional data sync between devices and the multi-cloud database, MongoDB Atlas, without having to write complex conflict resolution and networking code. The Realm mobile database allows end users to read and write data to their devices, enabling zero-latency data retrieval and offline application functionality. Bidirectional sync to MongoDB Atlas enables data captured on the device to be processed, analyzed, and combined with other data sets; this can then be delivered back to the user’s device to deliver a better and more robust experience.
Category: Computing & data (Page 47 of 80)
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.
BlueConic, a pure-play customer data platform (CDP), and Nagarro, provider of digital engineering and technology solutions, announced a strategic partnership to help enterprise organizations deploy BlueConic. Nagarro’s expertise in BlueConic’s technology, combined with the implementation and utilization best practices already used by BlueConic, will help ensure joint customers get value from their investment. Making first-party data a strategic asset is the future of all businesses. Yet accessing unified, actionable customer data can be challenging for innovative disruptors and established stalwarts. As these companies embrace the need for business transformation, they are confronting the pain of trying to use technologies with multiple unique ways of storing data and recognizing customers to support a fast moving, end-to-end customer experience.
Khoros, a provider of digital-first customer engagement software, announced that it has acquired Flow.ai, a conversational AI platform for designing and managing chatbots. Adding Flow.ai’s technology advances Khoros’ conversational AI and machine learning (ML) capabilities, data science expertise, and reflects the Company’s continued investment in the automation framework that powers Khoros’ customer engagement platform. Khoros currently offers its customer-facing chatbot, Khoros Bot, as a fully developed, ready-to-use service that easily integrates with its digital customer care solution, Khoros Care. With Flow.ai, Khoros will extend the AI/ML capabilities available to brands for greater self-service and operational agility.
With the open APIs available in Khoros’ automation framework, the Company remains “bot agnostic” and will continue to enable brands to integrate with any third-party bot provider. The Company also offers expert guidance on bot strategy through Khoros’ Strategic Services team, who can help brands identify the steps in the customer journey that are best suited for automation and how to maximize the satisfaction and ROI of those experiences.
https://khoros.com/platform/ai-ml, https://flow.ai
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Algolia, a Search-as-a-Service company, announced that it has acquired MorphL, a Google Digital News Initiative-funded startup, to help power Algolia’s new AI offering. This new suite of API-based Artificial Intelligence (AI) and Machine Learning (ML) models enables developers, data scientists, and marketers to predict users’ intent, personalize online experiences, and create targeted offers. Applying AI to every point in a user’s journey can be complex. Historically, developers’, data scientists’, and marketers’ options have been limited to a reliance on either “opaque” proprietary or open source offerings, or to build their own models from scratch, which could take several weeks or months. Algolia’s AI offering simplifies the ability to understand users’ intent so that it is possible to personalize experiences and offers, even from “first visit” and “first search.”
This acquisition extends Algolia’s intelligent search APIs, with recommendations and user behavior models all along the customer journey, so companies can deliver intent-based experiences and iterate quickly in response to market trends and user propensity profiles. It also allows them to leverage their market knowledge to manage and tune the entire experience.
https://www.algolia.com/products/ai-studio
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Pryon, an artificial intelligence (AI) company focused on enterprise knowledge, launched an automated natural language processing (NLP) platform that allows companies to add no-code AI capabilities to initiatives without professional services or special skills. Pryon’s ease of use allows teams to quickly prototype and deploy NLP projects without significant upfront commitments or planning cycles. This release of Pryon’s AI platform provides NLP that reads, organizes, and retrieves information. It can process repositories of documents, communications, intranet content, transcripts, and web pages in minutes. It then delivers results to natural language questions through text or voice interactions in an instant.
Today’s AI assistants and chatbots can only respond to a limited set of requests. In addition, search results require too much work by users to find relevant information. Pryon’s AI enables both a better understanding of requests as well as more accurate responses. With Pryon, companies can easily and quickly extend existing assistants and chatbots to answer millions of questions. They can also enhance search capabilities to deliver results from inside source documents along with additional context. Pryon’s fully automated NLP platform is now available as a cloud-based offering for enterprise customers.
Cortical.io announced a new release of its Cortical.io software. Utilizing a natural language understanding (NLU) approach based on semantic folding theory, the software analyzes the content of large quantities of documents. It automatically searches, extracts, classifies and compares key information from agreements, contracts, and other unstructured documents like policies and financial reports. The Cortical.io Contract Intelligence solution understands the meaning of whole sentences and concepts, instead of just keywords. The new version capabilities include: high-fidelity rendering of documents, improved extraction capabilities and advanced search (i.e. the ability to perform range queries that allow you to search for numerical and date ranges).
Cortical.io Contract Intelligence helps reduce the time and costs for any organization that needs to review and extract information from a large number of unstructured documents. It also helps reduce human errors inherent to a boring repetitive task and make better use of expensive subject matter experts. This helps markets such as insurance, that review and extract sensitive information from policies and loss run reports on a large scale. Pricing is based on annual volume of documents.
eccenca released version 20.12 of its knowledge graph software eccenca Corporate Memory. The latest releases consolidates their mission to make semantic data management technologies enterprise-ready and usable for business users. By focussing on user experience and performance eccenca Corporate Memory provides a wide array of access and integration points, a ready-to-use query catalogue, tools for data automation as well as the means for a detailed and transparent data governance. In past releases eccenca introduced enterprise-ready interactive graph visualization, automation tools for data migration and normalization, REST-API connections to query data from 3rd party applications, and mechanisms for data protection and access management. eccenca Corporate Memory 20.12 benefits:
- Simplified building process: The DataIntegration workbench unifies all relevant views.
- Powerful, easy-to-use reporting: Integrated connectors allow the creation of dashboards and data visualizations directly in Microsoft Power BI and Redash.
- Business-user friendly data exploration: The catalogue of declarative data queries allows business users to access and explore data without coding.
- Workflow automation: The updated cmemc command line tool simplifies the execution of workflows like dataset creation, update and deletion as well as updating vocabularies.
- Ready for internationalization: Localization of the user interface and metadata with i18n language integration.
- Enhanced data transparency and understanding: Statement annotation allows definition and documentation of additional metadata for a shared understanding of enterprise data across departments.
https://eccenca.com/news/article/release-corporate-memory-20-12
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Google introduced “ToTTo: A Controlled Table-To-Text Generation Dataset”, an open domain table-to-text generation dataset created using a novel annotation process (via sentence revision) along with a controlled text generation task that can be used to assess model hallucination. ToTTo (shorthand for “Table-To-Text”) consists of 121,000 training examples, along with 7,500 examples each for development and test. Due to the accuracy of annotations, this dataset is suitable as a challenging benchmark for research in high precision text generation. The dataset and code are open-sourced on our GitHub repo.
In the last few years, research in natural language generation, used for tasks like text summarization, has made tremendous progress. Yet, despite achieving high levels of fluency, neural systems can still be prone to hallucination (i.e.generating text that is understandable, but not faithful to the source), which can prohibit these systems from being used in many applications that require high degrees of accuracy.
While the process of assessing the faithfulness of generated text to the source content can be challenging, it is often easier when the source content is structured (e.g., in tabular format). Moreover, structured data can also test a model’s ability for reasoning and numerical inference. However, existing large scale structured datasets are often noisy (i.e., the reference sentence cannot be fully inferred from the tabular data), making them unreliable for the measurement of hallucination in model development.
https://ai.googleblog.com/2021/01/totto-controlled-table-to-text.html
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