A few years ago, I proposed an Iterative Search architecture that when used in conjunction with a Virtual Assistant would help feed the conversational dialogs when the Virtual Assistance was at a loss for an answer. See white paper. In a different post on this blog, I commented on how the newest releases of Solr and ElasticSearch can supplement a Virtual Assistant in these scenarios. In fact, Search has now been adopted by Virtual Assistants for that same reason.
Digital Assistant, like Siri and Google Assistant, usually provide a specific answer to a question, e.g., “What is the oldest city in the world,” it will give you the answer, or ask to do something for you, e.g., “Turn the lights on in the living room.” The digital assistant, when confident of the answer, reads back aloud over the home speaker or phone the answer or confirms the action taken. What they do, if they have no knowledge about the question or do not understand the intent? They do a web search to find the best possible matches to the query. Siri and Google Assistant have improved this feature recently. You may have noticed that if they do not have a specific answer to your question they show a list of web links containing the information you're looking for. Both have a filtering algorithm that ranks them and shows only the top three or four. Siri used to use Bing for these search suggestions; it recently switched to Google Search. As expected, Google Assistant uses Google Search as well. Why are Siri and Google Assistant not iterating further the search step to find among the search suggestions the most likely answer to the question and read it back aloud? In a white paper a few years back, I proposed the Iterative Search architecture that used in conjunction with a Virtual Assistant would help feed the conversational dialogs when the Virtual Assistance was at a loss for an answer. (link to article)
It advocated three steps:
What are Solr and ElasticSearch? In 2010 Solr merged with the Lucene project, and ElasticSearch first release came out. In the years that followed, Solr became the preferred open source distributed search, mostly for unstructured text, while ElasticSearch team continued their parallel development. In recent years, ElasticSearch has surpassed Solr in new distributed search deployments for its ease of use and integration, and grouping and filtering capabilities. Both are active open source projects. Elastic is the company behind ElasticSearch, not to be confused with Amazon ElasticSearch Service. Why Solr / ElasticSearch? If you want to provide your Virtual Assistant platform customers the option to enable the Iterative Search step to the data flow, before returning an answer to the user, Solr or Elastic Search are two equally valid open source choices to implement the Iterative Search. Presentation or slideshow software allows anyone to illustrate concepts and topic overviews very quickly and simply. Microsoft® PowerPoint, Apple® Keynote, and Google® Slides are some of these popular software apps, with PowerPoint being the most common data format supported by all.
What if you want to engage your audience in virtual whiteboard sessions or want to poll them on particular topics or take their pulse on how well they are grasping your presentation? For example, trainers, instructors, and teachers need just that, and while they use presentation software to deliver learning content, its effectiveness is limited for its inability to engage the audience with interactive canvas slides or polls. These users have to resort to separate applications causing a non-ideal user experience for both trainers and trainees. The good news is that Microsoft and Google have opened up their presentation software, online version, to developers to create integrated systems that improve enormously that user experience. We created such a system at LiberCloud, where users could not just use Microsoft PowerPoint Online to author their presentation, we allowed them to integrate polls, surveys, and tests, as well as virtual whiteboards, in their online presentation. Please see this page for an overview of what and how we did it. LiberCloud offers content management and collaboration services for content authors, educators, instructors as well as active learners. The SaaS platform allows individuals to sign-up as well as it gives the ability to educational institutions and businesses to create dedicated instances and private communities for sharing or collaborating on content.
The multi-tenant SaaS platform initially developed on the LAMP stack, was being expanded with a MongoDB NoSQL cluster, Node.js component and Socket.io for asynchronous notifications, and an Ejabberd based real-time XMPP server for group discussion rooms and live-chat. The content management services support multi-media assets as well as interactive assets such as virtual whiteboards, real-time polls and surveys, and assessments. LiberCloud launched its beta services to the public in 2014 and announced the general availability at the international Bett trade-show in London in January 2015. LiberCloud service was targeting international businesses or startups with a distributed workforce, clients, and user base. Therefore, it required designing and deploying the services in a distributed fashion, such that content originated in one region is available in others, and participants in real-time virtual discussions, polls, and group chats could be in any region. LiberCloud intent to offer its services near or in the proximity of the organizations and users we serve required a distributed architecture spread across multiple geographical regions. In the age where many talent managers, educators, philosophers, and trainers have been advocating the adoption of flipped classroom training, blended learning techniques to improve assimilation of content. The main obstacles in these efforts are two-fold:
After an overview on how assessments policies have evolved, the white paper describes the facets of a new cohesive learning and assessment system to help educators implement differentiated instruction with assessments for learning, not just for grading, ultimately to achieve the goal of making sure trainees actually learn what is being taught. I introduced these concepts as guest speaker at the Assessment Tomorrow conference in London City in March 2016. A few years back I wrote a white paper documenting the effort and thoughts that went into building a first generation SaaS platform and subsequent upgrade. While new technologies have been introduced, SaaS is now widely accepted by most companies to deliver their services, and building a SaaS-based application has become increasingly easier, there are some aspects of our original effort that were unique. We developed our first SaaS platform because the traditional web application architecture was inadequate to meet our business objectives. We were in uncharted territory, and customers demanded that we would not mingle their data with other customers' data. So, a common thread in our releases was the isolation of customer data. Many customers today may require the same, for regulatory or privacy concerns, and I thought that by sharing the thoughts that went into designing the first generation SaaS platform and subsequent upgrade would benefit your efforts as well. Our first generation SaaS platform needed some basic components to improve the provisioning and configurability of the application. We needed:
After we launched the first generation platform, over the course of several months we had other important issues to tackle to ensure scalability of platform and scalability of the business processes revolving the operation of the entire SaaS platform. The most significant improvement being the decentralization of the database and ability to use Machine Learning:
Collaboration platforms have existed for years in two primary areas: to collaborate on content and to share presentations. The first is primarily used in content management systems, general purpose ones and those specific to learning, while the second is a function of web conferencing services. The two services serve the same audiences but rarely have intersected from a functional standpoint.
Here we want to make the case that the two could and should intersect, in particular in a modern working and learning environment where content is created by brainstorming, discussions, and assessments. The proposed integrated environment is a lightweight, cloud platform, based on the concepts of real-time collaboration on content and the “Virtual Whiteboard” that provides more flexibility, unleashes the attachment to any particular physical device, and is easy for anyone to use. This white paper provides an overview of its features. |
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