Category: Computing & data (Page 78 of 91)

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.

8th Wall launches Face Effects tool for AR facial animations

May 26, 2020 / NewsShark

8th Wall is launching Face Effects, a new cloud tool that enables developers to create facial effects that wrap around someone’s face using augmented reality technology. The face filter developer tools are based on WebAR, which enables AR experiences to be accessed via a web browser instead of an app. 8th Wall Face Effects is designed to give developers and brands control to create face filters that are interactive, real-time, and that live on their own websites. Beyond WebAR, 8th Wall Face Effects can also be used across all devices (iOS/Android and desktops using a webcam) and benefit from no app required. You just click a link to experience it. Developers can choose the asset types, file sizes, and content to maximize the value for their audience.

Fans could connect multiple users together to create a shared shopping experience, and integrate with developers’ preferred analytics, customer relationship management system, and payment systems in virtual try-on products. Developers can simply scan a QR code to open up a cloud editor that adds a 3D object to your face. The edges of virtual sunglasses can stop at the edge of your face because an occluder prevents it from going right through your face. You can put virtual tattoos on your face to see what they look like before you make them permanent. With 8th Wall Face Effects, developers can anchor 3D objects to face attachment points, render face mesh with face components with textures and shaders, and design custom effects. Similar to 8th Wall’s existing World Effects and Image Target AR, Face Effects supports development with web frameworks such as A-Frame and Three.js. New developers can sign up for a 14-day free trial of the 8th Wall platform. Existing developers can log in and get started using the Face Effects project templates.

https://www.8thwall.com, h/t: VentureBeat

Franz Inc announces AllegroGraph v7

May 26, 2020 / NewsShark

Franz Inc., developer of Artificial Intelligence (AI) and supplier of Semantic Graph Database technology for Knowledge Graph Solutions, announced AllegroGraph 7, a solution that allows infinite data integration through a patented approach unifying all data and siloed knowledge into an Entity-Event Knowledge Graph solution that can support massive big data analytics. AllegroGraph 7 utilizes federated sharding capabilities that drive 360-degree insights and enable complex reasoning across a distributed Knowledge Graph. Hidden connections in data are revealed to AllegroGraph 7 users through a new browser-based version of Gruff, an advanced visualization and graphical query builder.

To support ubiquitous AI, a Knowledge Graph system will have to fuse and integrate data, not just in representation, but in context (ontologies, metadata, domain knowledge, terminology systems), and time (temporal relationships between components of data). The rich functional and contextual integration of multi-modal, predictive modeling and artificial intelligence is what distinguishes AllegroGraph 7 as a modern, scalable, enterprise analytic platform. AllegroGraph 7 is a temporal knowledge graph technology that encapsulates a novel entity-event model natively integrated with domain ontologies and metadata, and dynamic ways of setting the analytics lens on all entities in the system (patient, person, devices, transactions, events, and operations) as prime objects that can be the focus of an analytic (AI, ML, DL) process.

https://allegrograph.com, https://franz.com

Automattic invests in open decentralized comms ecosystem Matrix

May 21, 2020 / NewsShark

Automattic, the open source force behind WordPress, WooCommerce, Longreads, Simplenote and Tumblr, has made a $4.6M strategic investment into New Vector — the creators of an open, decentralized communications standard called Matrix. New Vector also developed a Slack rival (Riot) which runs on Matrix. Matrix is an open source project that publishes the Matrix open standard for secure, decentralised, real-time communication, and its Apache licensed reference implementations.

New Vector’s decentralized tech powers instant messaging for a number of government users, including France — which forked Riot to launch a messaging app last year (Tchap) — and Germany, which just announced its armed forces will be adopting Matrix as the backbone for all internal comms; as well as for KDE, Mozilla, RedHat and Wikimedia, and others.

https://vector.im, https://matrix.org, h/t: Techcrunch

Luminoso announces enhancements to open data semantic network

May 20, 2020 / NewsShark

Luminoso, who turn unstructured text data into business-critical insights, announced the newest features of ConceptNet, an open data semantic network whose development is led by Luminoso Chief Science Officer Robyn Speer. ConceptNet originated from MIT Media Lab’s Open Mind Common Sense project more than two decades ago, and the semantic network is now used in AI applications around the world. ConceptNet is cited in more than 700 AI papers in Google Scholar, and its API is queried over 500,000 times per day from more than 1,000 unique IPs. Luminoso has incorporated ConceptNet into its proprietary natural language understanding technology, QuickLearn 2.0. ConceptNet 5.8 features:

Continuous deployment: ConceptNet is now set up with continuous integration using Jenkins and deployment using AWS Terraform, which will make it faster to deploy new versions of the semantic network and easier for others to set up mirrors of the API.

Additional curation of crowd-sourced data: ConceptNet’s developers have filtered entries from Wiktionary that were introducing hateful terminology to ConceptNet without its context. This is part of their ongoing effort to prevent human biases and prejudices from being built into language models. ConceptNet 5.8 has also updated its Wiktionary parser so that it can handle updated versions of the French and German-language Wiktionary projects.

HTTPS support: Developers can now reach ConceptNet’s website and API over HTTPS, improving data transfer security for applications using ConceptNet.

http://blog.conceptnet.io/posts/2020/conceptnet-58/, https://luminoso.com/how-it-works

Microsoft open sources Fluid Framework – announces Fluid Workspaces and Fluid Components for Office 365

May 20, 2020 / NewsShark

Microsoft introduced the first way for end users to experience the Fluid Framework in Microsoft 365 with the upcoming availability in preview of Fluid Workspaces and Fluid Components. Fluid Workspaces and Components work like the web to bring the right level of context and connection as well as seamlessly capture follow-ups in-line and edit action items with an entire team. Fluid Components and Fluid Workspaces will become available in more places over time. This initial public preview includes basic text, tables, lists, agendas and action items. These Fluid components will be available for creation in Outlook for the web and Office.com. Microsoft also announced the Fluid Framework will be made open source and hosted as a repository available on GitHub in the next month, allowing developers and creators to use infrastructure from Fluid Framework in their own applications. Coupled with the release of additional developer documentation and tooling, developers can work alongside Microsoft to create and evolve Fluid Framework as it is developed. Developers can take advantage of JavaScript APIs that give them access to collaborative, shared data structures which can be used to power collaborative experiences. They also can create Fluid components — elements that can be reused within Microsoft 365 and across applications.

https://www.microsoft.com/en-us/microsoft-365/blog/2020/05/19/microsoft-teams-fluid-framework-new-microsoft-365/

Information Model

Gilbane Advisor 6-9-21 — stacks & aggregation, JavaScript for data, news

Hugging Face dives into machine translation with release of 1,000 models

May 19, 2020 / NewsShark

Hugging Face is taking its first step into machine translation this week with the release of more than 1,000 models. Researchers trained models using unsupervised learning and the Open Parallel Corpus (OPUS). OPUS is a project undertaken by the University of Helsinki and global partners to gather and open-source a wide variety of language data sets, particularly for low resource languages. Low resource languages are those with less training data than more commonly used languages like English.

Models trained with OPUS data now make up the majority of models provided by Hugging Face and the University of Helsinki’s Language Technology and Research Group the largest contributing organization. Before this week, Hugging Face was best known for enabling easy access to state-of-the-art language models and language generation models, like Google’s BERT, which can predict the next characters, words, or sentences that will appear in text. The Hugging Face Transformers library for Python includes pretrained versions of advanced and state-of-the-art NLP models like versions of Google AI’s BERT and XLNet, Facebook AI’s RoBERTa, and OpenAI’s GPT-2.

https://huggingface.co h/t: VentureBeat

Amazon releases Kendra to solve enterprise search with AI and machine learning

May 11, 2020 / NewsShark

Amazon Web Services announced the general availability of Amazon Kendra, an enterprise search service. Amazon Kendra uses machine learning to enable organizations to index all of their internal data sources, make that data searchable, and allow users to get precise answers to natural language queries. When users ask a question, Amazon Kendra uses finely tuned machine learning algorithms to understand the context and return the most relevant results, whether that be a precise answer or an entire document. For example, businesses can use Amazon Kendra to search internal documents spread across portals and wikis, research organizations can create a searchable archive of experiments and notes, and contact centers can use Amazon Kendra to find the right answer to customer questions across the complete library of support documentation. Amazon Kendra requires no machine learning expertise and can be set up completely within the AWS Management Console. Amazon Kendra provides a wide range of native cloud and on-premises connectors to popular data sources such as SharePoint, OneDrive, Salesforce, ServiceNow, Amazon Simple Storage Service, and relational databases.

https://aws.amazon.com/kendra/ ht: Techcrunch

walled-garden

In the context of the the internet “walled-garden” refers to privately controlled proprietary sections of the internet as opposed to the world wide web vision of an open web. Facebook is an obvious example.