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.
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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.
Progress announced the release of Progress Sitefinity 13 Digital Experience Platform (DXP). The new release includes:
- A new productivity environment to manage digital assets and classify content in a consistent and resource-efficient manner
- The ability to control the look and feel of the presentation layer while delivering content to a myriad of channels. This is possible through the new page layout service that decouples content from presentation when distributed to external channels.
- Personalization based on custom tags such as title, campaign source or other attributes as well as consistent, personalized experiences for returning visitors, regardless of the initial touch point.
- Customer journey and online touchpoint monitoring based on machine learning, enabling marketers to receive proactive touchpoint alerts in order to spot new opportunities and improve the ROI of marketing campaigns.
- Data-driven analytics to measure the performance of content with a comprehensive view of all personalization initiatives as well as the ability to export data directly to Google Data Studio for expanded data analysis options.
SDL announced it has advanced its partnership level with Veeva Systems (“Veeva”), supporting the product life cycle for pharmaceutical and Life Sciences companies. SDL Translation Management System (TMS) is integrated with the Veeva Vault RIM Suite, a cloud-based Regulatory Information Management (RIM) system. In the biopharmaceutical sector, companies must react quickly to complex regulatory updates across multiple regions. The combination of SDL’s network of in-house certified medical translators with SDL’s translation management technology provides an integrated set of translation capabilities within Veeva Vault RIM. This integration enables Veeva Vault RIM customers to automate multilingual tasks relating to regulatory document submissions, engagement with health authorities and product registration. SDL joined Veeva Systems’ Technology Partner Program in 2018. https://www.sdl.com
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.
Adobe and ServiceNow announced the availability of its partnership integration connecting data from Adobe Experience Platform and ServiceNow’s Customer Service Management workflow product to enable more seamless, connected customer experiences. Connecting Adobe Experience Platform, its Customer Experience Management (CXM) platform, and ServiceNow’s Customer Service Management product provides brands with a more complete view of the customer. Through this integration, Adobe and ServiceNow joint customers can:
- Establish Context to Drive Brand Loyalty
Enterprises are often challenged by navigating internal silos of data pertaining to interactions with their customers. This integration creates data workflows that removes those barriers and connects marketing and customer service organizations. - Gain Deeper Insights for Personalization
Great experiences are built on the understanding of a customer’s journey. Customers can streamline work between teams by aggregating data during the “evaluate” and “purchase” touchpoints, and capture service interactions to ultimately build rich, real-time customer profiles. - Improve Customer Experiences
A seamless customer experience allows for anticipating needs before they arise. With ServiceNow, organizations will understand which products or services the customer owns and uses, allowing organizations to drive towards greater personalization.
A radical solution to scale AI technology
Skip the proof of concept? This isn’t, or shouldn’t, be radical. It’s often a good idea for large scale projects, and not just for AI, or other digital experience or content technology initiatives.
Illustration: Israel G. Vargas
The example in this article is a customer experience chatbot for Nordea. Read More
How Medium became the best and worst place for coronavirus news
Medium’s pivots over the years created confusion about what they are and who they are for. The editorial challenges inherent in being both a platform and a publisher have only increased over time. Zoe Schiffer’s topical case study illustrates how difficult this balance is. Read More
It’s time to build
If you haven’t read this recent post by Marc Andreessen you should. Though prompted by frustration over our collective response to the current coronavirus pandemic, his prescription for preventing such future failures addresses a broader set of societal problems. Some he mentions; others are implicit, or follow, such as the focus on rent-seeking of wall street, VCs, and, well, too many of us. Read More
The New York Times’ morning email newsletter is getting an official “host and anchor”
Joshua Benton asks “Can any of the lessons of The Daily’s success be carried over into your inbox?” and attempts an answer, or rather asks the right questions. The new “The Morning” launched this week, and as someone who curates a newsletter I’ll be paying attention. But a podcast and an email newsletter are very different animals. Read More
Also…
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Should be important… Solution for IoT Interoperability – W3C Web of Things (WoT) via W3C
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Good… ICANN rejects transfer of control of .org to Ethos Capital. via ICANN
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Sexy Markdown… MDJS markup language adds JavaScript to Markdown for interactive documentation via InfoQ
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While we’re waiting for AR… Niantic buys 6D.AI as battle to own the AR cloud begins via Tech.Pinions
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The Gilbane Advisor curates content for content technology, computing, and digital experience professionals. We focus on strategic technologies. We publish more or less twice a month except for August and December. Subscribe | Feed | View online | Privacy policy | Editorial policy |
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Content and MarTech vendor subway maps
In 2008 Tony Byrne came up with the idea of a “subway map” as a useful and fun way to illustrate the content technology vendor landscape. He has updated the map every year to incorporate the shifting landscape, sprawling growth, adjacent technologies, and of course the renaming and repositioning by vendors and market analysts.
In this article, he shares all 12 subway maps and his thoughts on the changes each year. History is always relevant. A good read. Read More
Scroll, Firefox and ad-free news
Though their impact may be small, at least to start, the business model is interesting. Read More
RealWorld framework comparison
Handy up-to-date info for front-end-developers. Comparing performance, size, and lines of code implementing Conduit. Read More
The end of Starsky Robotics
This is a cautionary tale of what can happen when an enthusiastic founder and hungry investors crank each other up without guarding against mutually assured destructive confirmation bias, and don’t do enough serious due diligence. This scenario is unfortunately common, though often with enough funding/time/expert support a pivot or two can prevent disaster.
In this particular case, the problem was a naive expectation of what machine learning could, or would soon be able to, accomplish. Even the possibility of Artificial General Intelligence (AGI) is controversial among experts in the field. I only share this because Starsky’s founder and CEO Stefan Seltz-Axmacher had the courage to publish it. Kudos to him for sharing what happened, and providing enough detail for a valuable case study for entrepreneurship programs. Read More
Also…
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From the Working From Home department: new opportunities for oneupmanship… Videoconferencing etiquette via The Economist
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In case you’ve been procrastinating… Announcing mobile first indexing for the whole web via Google webmaster blog
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Great diversion while you’re at home “working”, as well as useful… Smithsonian Releases 2.8 Million Images Into Public Domain via Smithsonian Magazine
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Classic moral dilemma… Authors fume as online emergency library project “lends” unlimited free books via The Verge
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The Gilbane Advisor curates content for content technology, computing, and digital experience professionals. We focus on strategic technologies. We publish more or less twice a month except for August and December. We do not sell or share personal data. Subscribe | Feed | View online | Privacy policy | Editorial policy |
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