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 This process is siloed within a single department, findings are inconsistent, and insights quickly become out of date.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:
- 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.
- 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.
- Productivity: Underserved data analysts and scientists are now equipped with productivity features—reproducibility, autocomplete, scheduling, version control—to do better work in less time.
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.
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.
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.
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.com ■ https://allegrograph.com
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.
The Web-interoperable Runtimes Community Group (or “WinterCG”) is working with organizations including NearForm and Vercel to ensure that developers’ voices were heard in the creation of a new community group working within existing standards bodies. The API Standards allow developers to:
- Use the best tool or framework for the job: It will be easier to leverage tools and integrations from the community across runtimes, allowing developers to use the best tool for the job.
- Have a uniform approach to writing server side code: By removing platform specific nuances and the need to learn different platforms and focusing on functionality it’s easier for developers to ship better code.
- Move applications as technology needs change: As application needs evolve and change over time there is no need for massive re-writes and adding or switching vendors.
Apple, Google and Microsoft announced plans to expand support for a common passwordless sign-in standard created by the FIDO Alliance and the World Wide Web Consortium. The expanded capabilities will give websites and apps the ability to offer an end-to-end passwordless option. Users will sign in through the same action that they take multiple times each day to unlock their devices, such as a simple verification of their fingerprint or face, or a device PIN. This will be more secure when compared to passwords and multi-factor technologies such as one-time passcodes sent over SMS.
The platforms already support the FIDO Alliance standard, but previous implementations require users to sign in to each website or app with each device before using passwordless functionality. Today’s announcement extends these platform implementations to:
- Allow users to automatically access their FIDO sign-in credentials (referred to by some as a “passkey”) on many of their devices, even new ones, without having to re-enroll every account.
- Enable users to use FIDO authentication on their mobile device to sign in to an app or website on a nearby device, regardless of the OS platform or browser they are running.