Archive for June, 2014

Personal AI Helps Convert Social CRM for Recruiting

Thursday, June 26th, 2014

Given the need for more effective content marketing and better quality lead generation, why aren’t the tools better?  Certainly there are lots of applications, SaaS products and services available for all parts of the marketing and sales process.   With BrainBrowser we provide a tool that can understand the content from marketing and match it to bloggers, LinkedIn connections, Twitter followers and find candidates in places you would never look.

Since about one-third of the 7,500+ queries by our testers were using BrainBrowser to search for people, a key objective is to add features to manage the results and integrate them into your workflow.  If you find someone relevant to your work or a potential recruit, you should be able to connect with them right from the list, follow them on Twitter or share lists of candidates with collaborators.

BrainBrowser with Nimble Popup

As a recruiting professional your task is to find the candidates and conversations on the web where conversions will be maximized and get there first.  BrainBrowser does this for you, creating a list of people, companies and sites that match the content of your position and company description.

As a sales professional, you want to use content, either from your marketing department or content you find and create on your own, to engage your network and to identify the people that are talking about and responsible for buying/influencing a purchase.

In our research (using BrainBrowser) we discovered Nimble and a new category of Social CRM vendors with applications driving social selling (check out Gerry Moran’s post for background on content and social selling).  We were immediately hooked and started using Nimble as our company CRM but quickly found it worked well for managing lists of candidates.

Nimble, a new social CRM application, has made integration easy and I’m recommending it to everyone.  All you need to do is sign up for the trial (its only $15 per month if you like it) and install the plug in in your Chrome browser.  You’ll then be able to highlight the name of the person on the list in BrainBrowser, right click, select the Nimble Search and a popup will display the person’s social media pages in LinkedIn, Twitter, Google+ etc.  Click Save and you’ve added them to your Nimble Contacts where you can then view their social media messages, profile and decide whether to connect or follow.   Tag them and you’ve creating a recruiting hot list you can track in Nimble.

Here’s a video clip I tweeted to CEO Jon Ferrara demonstrating how/why we love it.  This was in response to his video clip to Larry Nipon following up on my referral.

Let me know how you like it.  They do a great job but if you have any questions on the difference between CRM and Social CRM, and how we’re using it for recruiting.  Be sure to add @ai_one or @tom_semantic if you tweet about this and sign up to request a login for BrainBrowser.

As of today, there are only 22 slots left for FREE registrations under the Alpha test program.  Participation gets you a year free on the platform.  Email or tweet @tom_semantic to sign up.

Context, Graphs and the Future of Computing

Friday, June 20th, 2014

Robert Scoble and Shel Israel’s latest book, Age of Context, is a survey of the contributions across the globe to the forces influencing technology and our lives today.  The five forces are mobile, social media, data, sensors and location.  Scoble calls these the five forces of context and harnessed, they are the future of computing.

Pete Mortensen also addressed context in his brilliant May 2013 article in Fast Company “The Future of Technology Isn’t Mobile, It’s Contextual.”   So why is context so important (and difficult)?  First, context is fundamental to our ability to understand the text we’re reading and the world we live in.  In semantics, there is the meaning of the words in the sentence, the context of the page, chapter, book and prior works or conversations, but also the context the reader’s education and experience add to the understanding.  As a computing problem, this is the domain of text analytics.

Second, if you broaden the discussion as Mortensen does to personal intelligent agents (Siri, Google Now), the bigger challenge is complexity.  Inability to understand context has always made it difficult for computers and people to work together.  People and the language we use to describe our world is complex, not mathematical, You can’t be reduced to a formula or rule set, no matter how much data is crunched. Mortensen argues (and we agree) that the five forces are finally giving computers the foundational information needed to understand “your context” and that context is expressed in four data graphs.  These data graphs are

  • Social (friends, family and colleagues),
  • Interest (likes & purchases),
  • Behavior (what you do & where) and
  • Personal (beliefs & values).

While Google Glass might be the poster child of a contextual UX, ai-one has the technology to power these experiences by extracting Mortensen’s graphs from the volumes of complex data generated by each of us through our use of digital devices and interaction with increasing numbers of sensors known as the Internet of Things (IoT).  The Nathan API is already being used to process and store unstructured text and deliver a representation of that knowledge in the form of a graph.  This approach is being used today in our BrainDocs product for eDiscovery and compliance.

Age of Context by Scoble and IsraelIn Age of Context, ai-one is pleased to be recognized as a new technology addressing the demands of these new types of data.  The data and the applications that use them are no longer stored in silos where only domain experts can access them.  With Nathan the data space learns from the content, delivering a more relevant contextual response to applications in real time with user interfaces that are multi-sensory, human and intuitive.

We provide developers this new capability in a RESTful API. In addition to extracting graphs from user data, they can build biologically inspired intelligent agents they can train and embed in intelligent architectures.   Our new Nathan is enriched with NLP in a new Python middleware that allows us to reach more OEM developers.  Running in the cloud and integrated with big data sources and ecosystems of existing APIs and applications, developers can quickly create and test new applications or add intelligence to old ones.

For end users, the Analyst Toolbox (BrainBrowser and BrainDocs) demonstrates the value proposition of our new form of artificial intelligence and shows developers how Nathan can be used with other technologies to solve language problems.  While we will continue to roll out new features to this SaaS offering for researchers, marketers, government and compliance professionals, the APIs driving the applications will be available to developers.

Mortensen closes, “Within a decade, contextual computing will be the dominant paradigm in technology.”  But how?  That’s where ai-one delivers.  In coming posts we will discuss some of the intelligent architectures built with the Nathan API.

ai-one Contributes to ETH Publication on Knowledge Representation

Tuesday, June 3rd, 2014

We are pleased to announce the availability of the following publication from prestigious ETH University in Zurich.  This book will be a valuable resource to developers, data scientists, search and knowledge management educators and practitioners trying to deal with the massive amounts of information in both public and private data sources.  We are proud to have our contribution to the field acknowledged in this way.

Knowledge Organization and Representation with Digital Technologies

http://www.degruyter.com/view/product/205460  |  ISBN: 978-3-11-031281-2

ai-one was invited to contribute as co-author to a chapter in this technical book.

ETH Publication- Knowledge RepresentationIn the anthology readers will find very different conceptual and technological methods for modeling and digital representation of knowledge for knowledge organizations (universities, research institutes and educational institutions), and companies based on practical examples presented in a synopsis. Both basic models of the organization of knowledge and technical implementations are discussed including their limitations and difficulties in practice.  In particular the areas of knowledge representation and the semantic web are explored. Best practice examples and successful application scenarios provide the reader with a knowledge repository and a guide for the implementation of their own projects. The following topics are covered in the articles:

  •  hypertext-based knowledge management
  • digital optimization of the proven analog technology of the list box
  • innovative knowledge organization using social media
  • search process visualization for digital libraries
  • semantic events and visualization of knowledge
  • ontological mind maps and knowledge maps
  • intelligent semantic knowledge processing systems
  • fundamentals of computer-based knowledge organization and integration

The book also includes coding medical diagnoses, contributions to the automated creation of records management models, business fundamentals of computer-aided knowledge organization and integration, the concept of mega regions to support of search processes and the management of print publications in libraries.

Available in German only at this time.

Wissensorganisation und -repräsentation mit digitalen Technologien

http://www.degruyter.com/view/product/205460  |  ISBN: 978-3-11-031281-2

ai-one war eigeladen worden, als CO-Autor ein Kapitel in diesem Sachbuch beizusteuern.

Im Sammelband werden die sehr unterschiedlichen konzeptionellen und technologischen Verfahren zur Modellierung und digitalen Repräsentation von Wissen in Wissensorganisationen (Hochschulen, Forschungseinrichtungen und Bildungsinstitutionen) sowie in Unternehmen anhand von  praxisorientierten Beispielen in einer Zusammenschau vorgestellt. Dabei werden sowohl grundlegende Modelle der Organisation von Wissen als auch technische Umsetzungsmöglichkeiten sowie deren Grenzen und Schwierigkeiten in der Praxis insbesondere in den Bereichen der Wissensrepräsentation und des Semantic Web ausgelotet. Good practice Beispiele und erfolgreiche Anwendungsszenarien aus der Praxis bieten dem Leser einen Wissensspeicher sowie eine Anleitung zur Realisierung von eigenen Vorhaben. Folgende Themenfelder werden in den Beiträgen behandelt:

  • Hypertextbasiertes Wissensmanagement
  • digitale Optimierung der erprobten analogen Technologie des Zettelkastens
  • innovative Wissensorganisation mittels Social Media
  • Suchprozessvisualisierung für Digitale Bibliotheken
  • semantische Event- und Wissensvisualisierung
  • ontologische Mindmaps und Wissenslandkarten
  • intelligente semantische Wissensverarbeitungssysteme

sowie Grundlagen der computergestützten Wissensorganisation und -integration, das Konzept von Mega-Regionen zur Unterstützung von Suchprozessen und zum Management von Printpublikationen in Bibliotheken, automatisierte Kodierung medizinischer Diagnosen sowie Beiträge zum Records Management zur Modellbildung und Bearbeitung von Geschäftsprozessen.