Archive for the ‘text analytics’ Category

The State of A.I. and Switzerland

Tuesday, June 28th, 2016

the-state-of-artificial-intelligence-in-15-visuals-1050x580As you know, Artificial intelligence, or AI, has been a part of our world at ai-one since our founding in 2003.  Don’t be confused by the latest buzzwords, deep learning, machine learning, artificial intelligence and biologically inspired intelligence (our Nathan) are all part of the field of A.I.    It’s a hot subject now but the languages, techniques and algorithms have been around for decades, often as part of an application (Google search and map directions for example).

In order to become it’s own industry A.I. needs a large number of companies, money and its own problems to solve.  This is the real news of the last few years and Max Wegner and our friends at have created “State of Artificial Intelligence Infographic” to tell the story.

Today A.I. is a significant and growing sector of the technology industry; billions of dollars are invested in new AI developments, and companies around the world are working on new AI applications as you read this. And while most AI companies have been in existence for less than a decade (the average age is around four or five years), the tech behind AI is evolving, and the role of AI in our lives in the coming years is all but certain to grow.

One of the surprises in the report is the ranking of Switzerland as the second largest location in the world by amount of VC funding received and third by number of companies.  With almost all of the companies less than 10 years old, ai-one was clearly early, before the cloud and big data brought in the new era. Our biologically inspired intelligence is another differentiation from all but a few of these companies.

It is a new era and with all the competition comes the demand from the business community to make significant investments in A.I. powered applications.  We see the change in the character of the inbound leads from our website.  In the past those inquiries were from PhDs, engineers and startups where today they are almost exclusively from product managers at larger enterprises.  This is the type of demand that will drive growth and we’re excited to be in this space.

If you want to put see what our AI can do for your enterprise, please connect.


ISC Consulting Powers Pytheas AI with BrainDocs

Friday, June 24th, 2016

We are pleased to publish ISC’s submission under the DIUx program.  The new  “Defense Innovation Unit Experimental (DIUx) serves as a bridge between those in the U.S. military executing on some of our nation’s toughest security challenges and companies operating at the cutting edge of technology.” Powered by ai-one’s Nathan ICE artificial intelligence core for language, ISC’s Pytheas AI will provide ISC with the technology to assist researchers and help our governments keep us safe.  Some proprietary sections have been deleted in the version below.

ISC White Paper for DIUx Technology Area of Interest: Knowledge Management

By Jeremy Toor, ISC Consulting Group

Executive Summary

ISC/ai-one develops a prototype using Pytheas artificial intelligence (Pytheas AI) which will provide automated intelligent information management, or knowledge management (KM) of multiple data sources.  Pytheas AI fingerprints the flow of data from almost any source including chat, email, message traffic, and other data.   This AI core then supports the user in a publish/subscribe architecture, with building knowledge from the fingerprinted data through queries and intuitive alerts that understand the difference and importance of contextual situations.

The abundant quantity of data that is available to users, analysts, and commanders today can make it challenging to build a concise and accurate picture from which dynamic assessments can be made. Both Command and Control (C2) and intelligence systems are largely data-centric. Users that are required to make strategic and tactical decisions will benefit from a task-centric user experience that is able to manage information as it is created and presented, and distil many sources of data into a manageable data flow.  This user experience, facilitated by Pytheas AI will deliver an KM Engine that can accelerate the decision making process.

Through Pytheas AI the user will be presented with data that has gone through automated processes to be categorized, tagged, and ranked according to its value in the current context of operations.  Pytheas AI will give the user flexibility to tailor their focus area and pull information from a wide breadth of sources as they build situational awareness and confidence to take action.


ISC/ai-one proposes a three phase project.  Phase 1 will include one-week for initial installation, configuration and user training. Phase 2 will include a five-month period to support data ingestion, intelligent agent training and dashboard customization. Phase 3 will include a three-week evaluation and close out.


Pytheas AI is built with an artificial intelligence core to collect, organize and analyze language to uncover key links and patterns within large volumes of unstructured text.  The application empowers analysts to find the relationships necessary to discover, manage, process and exploit data.  Key features and attributes of Pytheas include:

  • Discovery of Concepts through the use of Intelligent Agents
  • Agent collections can be built from existing plans, roadmaps and strategy documents
  • DoD analysts can use common KM collections or build and share concept agents
  • Agents provide classification for query and tagging of documents
  • Application core is language independent
  • Fast and lightweight running on PC class machines or VMs

Pytheas AI is built upon ai-one’s BrainDocs software application (with NathanICE API core) which is a commercially ready and viable technology that has been applied to several use-cases similar to the requirements in the technology area of interest, knowledge management, that DIUx is seeking.  Our prototype for KM is ready for demonstration using sample data.

Pytheas uses the ability of ai-one’s proprietary NathanICE API to discern patterns in the words and associations that are central to the meaning of all or a portion of a text document (in the same way as the brain).  Nathan extracts these keywords and associations, filtering out the noise to create a proprietary fingerprint array of the concept that can be used in many ways.

Pytheas uses the fingerprint of a trained concept to find (rank) similar concepts within a corpus of information (documents, websites, databases) and returns paragraph-level results sorted by “similarity”. These results support a variety of workflows in enterprise compliance, classification, search and knowledge management.  Agent similarity scores are exported to Excel or your database to support analytics and BI tools. This can be done by the analyst for small ad hoc studies.  Agents can also be used to code years of legacy data without additional training.

Users employ agents in Pytheas AI to organize text based on contextual ideas and metadata dimensions, improving accuracy, consistency and saving substantial amounts of time in this tedious process.

The Basic Elements of Pytheas

Documents – Pytheas is capable of analyzing any form of unstructured text. In fact, our technology works best with semantically-rich content written in your business vernacular without external taxonomies or ontologies.  Working at the paragraph level it has been used on everything from text messages to database fields to long documents always with full traceability to source.

Conceptual Fingerprints – This is the “secret sauce” of our discovery capabilities. Pytheas uses the Nathan API keywords and associations to create semantic “fingerprints” of concepts. Because one concept can be written in multiple ways, our algorithm does not rely on word counts, natural language processing (NLP) or latent semantic analysis (LSA) when identifying and fingerprinting concepts.

Intelligent Agents – Pytheas agents examine and compare the conceptual fingerprints to find traces of concepts buried within your data. Our premise is that analyst is the expert and needs to be able to train their own army of software agents to “read” documents and deliver the relevant paragraph. Used as a collection, the scores from a collection of agents set the context for a user’s query.

Paragraph Level Concept Discovery – Pytheas provides the ability to categorize and display concept results at the paragraph-level. Users do not need to hunt through documents trying to find a concept that a search engine claims to be present. Our system will return the paragraph(s) that closely match a concept, sort and group the concepts by similarity to one another. Paragraphs can be evaluated and traced back to their source document for reporting and distribution.

Topic Mapper Entity and Sentiment

Figure 1. Topic Mapper Entity and Sentiment in SEC Filings

Ease of Integration – Pytheas application can be used with conventional desktop tools for ad hoc projects.  For workflow automation a Restful API provides developers an easy method to process documents and export results to SQL or other DBs for reporting and visualizations.

Optional Entity Extraction and Sentiment (Figure 1 above) – Complementing paragraph level concept detection is the ability to extract entities and/or score for sentiment so this information can be added to visualizations and follow on workflows.  Clients can use their own technology for this purpose or add custom analytics to further refine the insight for social network analysis, tagging existing file headers or streamlining the flow of information into the analyst.

Defense Utility

The immediate benefit to DoD is increased productivity, consistent analysis and more effective information management.  The long-term benefit is an ability to perform quicker, more informed decisions.

Operational users of this prototype include any person that has to search through data.  This includes anyone using SharePoint and other common organizational databases.  Analysts who must sift through massive amounts of data in order to discover relevant information will save countless hours through the employment of our prototype.   Through the employment of a similar use case at NASA, our customer was able to complete a typical six-week project in one-week!

Company and Relevant Use Case

Lead by ISC, personnel from ISC Consulting and ai-one inc. will execute the project.

ISC Consulting Group is a Service Disabled Veteran-Owned Small Business (SDVOSB). We are headquartered in Sierra Vista, Arizona, with operational offices at Ft. Huachuca, AZ; Orlando, FL; Ft. Gordon, GA; and Northern Virginia. ISC provides a full-spectrum of services, products & solutions supporting the DOD Intelligence Community and key commercial clients with advanced capabilities in Instructional Solutions, Cyber Security, Command and Control planning and operations, Intelligence operations, Information Technology, and Data Analytics through Artificial Intelligence products and services.

ai-one inc. is the developer of a proprietary core technology that emulates the complex pattern recognition functions of the human brain that can detect the key features and contextual meaning of text, time-series and visual data.  This technology will enable DIUx to score and analyze any piece of textual content and discover information by concept, bringing the dimension of AI understanding to knowledge management. This technology automatically generates a lightweight ontology that easily detects all relationships among data elements; solving the immediate problems facing the DIUx knowledge management based process and schedule.

Existing Customers

ISC has served several clients with Pytheas technology, including NASA Marshall Space Flight Center (MSFC).   Currently, Pytheas is being used by MSFC’s Advanced Concepts Office (ACO) under a Cooperative Agreement to assist in technology roadmap development and separately by the Office of Strategic Analysis and Communication (OSAC) to manage and report on their portfolio of project investments (similar to SBIR grants).   For example, the roadmap project is described below:

Overview of the NASA Advanced Concepts TAPP Pilot Project

The Advance Concepts Office (ACO) at MSFC, NASA is developing and refining methods and processes for performing Information Based Decisions for Strategic Technology Investments.  This system is currently referred to as TAPP, Technology Alignment & Prioritization Process.   This process supports the evaluation of the technologies for investment by NASA and MSFC to insure alignment with NASA mission plans, technology area priorities and strategic knowledge gaps.

TAPP creates an interactive system for exploring the almost mind boggling complexity of planning for multiple missions using over 400 technologies (many still in basic research) and hundreds of interrelated elements/sub-elements over 30-year planning horizons.

Pytheas provides NASA the capability to have data mining agents parse and score unstructured content against the nearly 400 technologies identified in the 15 Technology Roadmaps.  This ability to score proposals with agents allows ACO to perform statistical analysis within the Information Based Decision framework for Strategic Investments.

The immediate benefit to ACO is increased productivity and consistent analysis. The long-term benefit is an ability to perform quicker, more informed technology assessments, feasibility analysis, and concept studies that align with NASA evolving strategic goals and multiple mission objectives.


Given a six-month prototype build period, ISC/ai-one will demonstrate to DIUx that ISC/ai-one’s Pytheas AI application will enable the organization to save critical time and human capital in the implementation and operation of knowledge management systems.  Pytheas will empower the IC to rapidly and effectively sort through vast volumes of text data in order to gain knowledge and position decision makers with the right information to achieve stated organizational analytical research outcomes.

ai-one Powers Competitive Intelligence Analytics as a Service

Tuesday, June 7th, 2016

ai-one inc and KDD Analytics put their artificial intelligence and business intelligence expertise to work for competitive analysts.  After collaboration on projects from aerospace research to marketing surveys, the companies are pleased to announce a new service for C-Suite executives and analysts.

This blog was posted earlier on our Analyst Toolbox website.

Day in the Life of a Financial Competitive Analyst

“It’s 10:00 PM, the night before the quarterly board meeting, and we are still pulling together financial data on our company and competitors into presentation worthy graphics.

Procrastination?  No, the tools and processes just make it a recurring battle. One key section of our report is hampered by a lack of standardization in SEC filings.  Our auditors routinely deliver the internal financials at the last minute.  Reformatting the financials in a way consistent with comparatives, let alone making them visual, interactive and providing scenario analysis capability is a time consuming hassle.

We always run out of time to actually “analyze” the results…there has to be a better way”.

– Earl Harvey, Senior Financial Competitive Analyst

Sound familiar?  Earl’s problem led him to KDD Analytics and ai-one, and ultimately to a collaboration developing CIaaS (Competitive Intelligence as a Service).

The State of Financial Competitive Intelligence

Financial data on your competitors comes from SEC filings (10q/10k) via companies such as Dow Jones FactSet, Edgar Online, ThomsonOne and Bloomberg who provide aggregated financial data (typically) in Excel worksheets or through an API “firehose” that requires programming resources to navigate.

But then what?

The data still needs to be standardized across companies and reporting periods and presented in a visually digestible manner; often for people using different devices (desktop, tablet, mobile).  Moreover, this process needs to be repeatable every quarter with a consistent visual format and ideally delivered several days before the board meeting…a tall order for resource constrained competitive intelligence analysts.  As a result, “burning the midnight oil” sessions are the rule not the exception.

How Do You Avoid This Last Minute Stress?

Avoiding this fire drill (without hiring on more resources) is possible by using a service that has:

  • Standardized the data
  • Developed the visuals, charts and scenarios
  • Loaded and analyzed the latest data, and ideally,
  • Used A.I. to “read” and organize the relevant text from the docs.

That is, a source that will deliver a finished, interactive solution in a timely manner allowing you to focus on insight and analysis of the financial data, so you’re ready to be brilliant on demand (and getting more sleep).

Introducing the Financial Analyst Toolbox (FaTbx™), financial competitive intelligence as a service.

Currently in beta as a custom service, FaTbx™ is a set of more than 30 presentation ready Tableau dashboards, displaying interactive, comparative financial data for your company and other public companies critical to your business ecosystem.

Get the big picture fast: rankings and financial health, trends and topic heat maps (from our tech stocks demo).

FaTbx BIg Pic 3 slides

Then drill down:  Income statement waterfalls, balance sheet, cash flow details and topics (see how Apple, Google and Microsoft 2015 Q3 results compare below).

FaTbx Waterfall Pic 3 slides

Developed by experienced competition, artificial intelligence, analytics and visualization experts, FaTbx™ shows your company and competitors’ financials in a consistent, standardized and easily digestible manner.  Using the financials to spot issues and trends, the AI engine powers drill down to the disclosure text in the filings: no need to pull up a 10k and look for the narrative.

Filters adjust for financial category (e.g. income, cash flow, balance sheet, ratios), company, growth measure (e.g. quarter over quarter, year over year, CQGR), TTM and displayed time span.  Custom filters can be added based on your company’s need.

FaTbx™ is available as a cost effective annual subscription with quarterly or monthly updates.  The standard service includes comparatives for three publicly traded competitors, suppliers or customers.  It is delivered online via Tableau Server Edition or through a private web portal.  Subscription tiers depend on the level of support, customization, information sources and macroeconomic data desired.  Custom integration with internal KPIs can be provided.

FaTbx™ – Financial CI as a Service.   We streamline the grunt work of financial competitive analysis so you can focus on your company’s strategy and response. To learn more, contact me about the beta program or request a live demo.


ai-one’s Biologically Inspired Neural Network

Sunday, February 1st, 2015

ai-one’s Learning Algorithm: Biologically Inspired Neural Network
– Introduction to HSDS vs ANN in Text Applications

Unlike any of the traditional neural nets, the neural network based on ai-one, the HoloSemantic Data Space neural network (invented by Manfred Hoffleisch) or in short “HSDS”, are massively connected, asymmetrical graphs which are stimulated by binary spikes. HSDS do not have any neural structures pre-defined by the user. Their building blocks resemble biological neural networks: a neuron has dendrites, on which the synapses from other neurons are placed, and an axon which ends in synapses at other neurons.

The connections between the neurons emerge in an unsupervised manner while the learning input is translated into the neural graph structure. The resulting graph can be queried by means of specific stimulations of neurons. In traditional neural systems it is necessary to set up the appropriate network structure at the beginning according to what is to be learned. Moreover, the supervised learning employed by neural nets such as the perceptron requires that a teacher be present who answers specific questions. Even neural nets that employ unsupervised learning (like those of Hopfield and Kohonen) require a neighborhood function adapted to the learning issue. In contrast, HSDS require neither a teacher nor a predefined structure or neighborhood function (note that although a teacher is not required, in most applications programmatic teaching is used to insure the HSDS has learned the content needed to meet performance requirements). In the following we characterize HSDS according to their most prominent features.

Exploitation of context

In ai-one applications like BrainDocs, HSDS is used for the learning of associative networks and feature extraction. The learning input consists of documents from the application domains, which are broken down into segments rather than entered whole: all sentences may be submitted as is or segmented into sub-sentences according to grammatical markers. By way of experimenting, we have discovered that a segment should ideally consist of 7 to 8 words. This is in line with findings from cognitive psychology. Breaking down text documents into sub-sentences is the closest possible approximation to the ideal segment size. The contexts given by the sub-sentence segments help the system learn. The transitivity of term co-occurrences from the various input contexts (i.e. segments) are a crucial contribution to creating appropriate associations. This can be compared with the higher-order co-occurrences explored in the context of latent semantic indexing.

Continuously evolving structure
The neural structure of a HSDS is dynamic and changes constantly in line with neural operations. In the neural context, change means that new neurons are produced or destroyed and connections reinforced or inhibited. Connections that are not used in the processing of input into the net for some time will get gradually weaker. This effect can also be applied to querying, which then results in the weakening of connections that are rarely traversed for answering a query.

Asymmetric connections
The connections between the neurons need not be equally strong on both sides and it is not necessary that a connection should exist between all the neurons (cp. Hopfield’s correlation matrix).

Spiking neurons
The HSDS is stimulated by spikes, i.e. binary signals which either fire or do not. Thresholds do not play a role in HSDS. The stimulus directed at a neuron is coded by the sequence of spikes that arrive at the dendrite.

Massive connectivity
Whenever a new input document is processed, new (groups of) neurons are created which in turn stimulate the network by sending out a spike. Some of the neurons reached by the stimulus react and develop new connections, whereas others, which are less strongly connected, do not. The latter nevertheless contribute to the overall connectivity because they make it possible to reach neurons which could not otherwise be reached. Given the high degree of connectivity, a spike can pass through a neuron several times since it can be reached via several paths. The frequency and the chronological sequence in which this happens determine the information that is read from the net

General purpose
There is no need to define a topology before starting the learning process because the neural structure of the HSDS develops on its own. This is why it is possible to retrieve a wide range of information by means of different stimulation patterns. For example, direct associations or association chains between words can be found, the words most strongly associated with a particular word can be identified, etc.

Rumsfeld Conundrum- Finding the Unknown Unknown

Tuesday, January 27th, 2015

Since we began the process of building applications using our AI engine, we have been focused on working with ideas or concepts. With BrainDocs we built intelligent agents to find and score similarity for ideas in paragraphs, but still fell short of the vision we have for our solution. Missing was an intuitive and visual UI to explore content interactively using multiple concepts and  metadata (like dates, locations, etc). We want to give our users the power to create a rich and personal context to power through their research. What do I call this?

Some Google research led me to a great visualization and blog by David McCandless on the Taxonomy of Ideas. While the words in his viz are attributes of ideas, not the ideas themselves, it got me thinking in different ways about the problem.

Taxonomy of Ideas

If you substitute an idea (product or problem) in David’s matrix and add the dimension of time, you create a useful framework. If the idea above was “car”, then the top right might be Tesla and bottom left a Yugo (remember those?). Narrow the definition to “electric car” or generalize to “eco-friendly personal transportation” and the matrix changes. But insert an unsolved problem and now you have trouble applying the attributes. You also arrive at an innovator’s dilemma (not the seminal book by Clayton Christensen), the challenge of researching something that hasn’t been labeled and categorized yet.

Ideas begin in someone’s head. With research, debate, and engineering, they become products. Products have labels and categories that facilitate communication, search and commerce. The challenge for idea search on future problems is that the opposite occurs: products are not yet ideas and the problems they solve may not have been defined yet. If I may, Donald Rumsfeld nailed the problem with this famous quote:

“There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know.”

And if it’s an unknown unknown, it certainly hasn’t been labeled yet so how do you search for it? Our CEO Walt Diggelmann used to say it this way, “ai-one gives you an answer to a question, you did not know that you have to ask….!

Innovators work in this whitespace.

If you could build and combine different intelligent (idea) agents for problems as easily as you test different combinations of words in a search box, you could drive an interactive and spontaneous exploration of ideas. In some ways this is the gift of our intelligence. New ideas and innovation are in great part combinatorial, collaborative and stimulated by bringing together seemingly unrelated knowledge to find new solutions.

Instead of pumping everything into your brain (or an AI) and hoping the ideas pop out, we want to give you the ability to mix combinations of brains, add goals and constraints and see what you can create. Matt Ridley termed this “ideas having sex”. This is our goal for Topic-Mapper (not the sex part).

So what better place to apply this approach than to the exploration of space? NASA already created a “taxonomy of ideas” for the missions of the next few decades. In my next blog I’ll describe the demo we’re working on for the grandest of the grand challenges, human space exploration.


ai-one and the Machine Intelligence Landscape

Monday, January 12th, 2015

In the sensationally titled Forbes post, Tech 2015: Deep Learning And Machine Intelligence Will Eat The World, author Anthony Wing Kosner surveys the impact of deep learning technology in 2015. This is nothing new for those in the field of AI. His post reflects the recent increase in coverage artificial intelligence (AI) technologies and companies are getting in business and mainstream media. As a core technology vendor in AI for over ten years, it’s a welcome change in perspective and attitude.

We are pleased to see ai-one correctly positioned as a core technology vendor in the Machine Intelligence Landscape chart featured in the article. The chart, created by Shivon Zilis, investor at BloombergBETA, is well done and should be incorporated into the research of anyone seriously tracking this space.

Especially significant is Zilis’ focus on “companies that will change the world of work” since these are companies applying AI technologies to innovation and productivity challenges across the public and private sectors. The resulting solutions will provide real value through the combination of domain expertise (experts and data) and innovative application development.

This investment thesis is supported by the work of Erik Brynjolfsson and Andrew McAfee in their book “The Second Machine Age”, a thorough discussion of value creation (and disruption) by the forces of innovation that is digital, exponential and combinatorial. The impact of these technologies will change the economics of every industry over years if not decades to come. Progress and returns will be uneven in their impact on industry, regional and demographic sectors. While deep learning is early in Gartner’s Hype Cycle, it is clear that the market value of machine learning companies and data science talent are climbing fast.

This need for data scientists is growing but the business impact of AI may be limited in the near future by the lack of traditional developers who can apply them. Jeff Hawkins of Numenta has spoken out on this issue and we agree. It is a fundamentally different way to create an application for “ordinary humans” and until the “killer app” Hawkin’s speaks about is created, it will be hard to attract enough developers to invest time learning new AI tools. As the chart shows, there are many technologies competing for their time. Developers can’t build applications with buzzwords and one size fits all APIs or collections of open source algorithms. Technology vendors have a lot of work to do in this respect.

Returning to Kosner’s post, what exactly is deep learning and how is it different from machine learning/artificial intelligence? According to Wikipedia,

Deep learning is a class of machine learning training algorithms that use many layers of nonlinear processing units for feature extraction and transformation. The algorithms may be supervised or unsupervised and applications include pattern recognition and statistical classification.

  • are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher level features are derived from lower level features to form a hierarchical representation.
  • are part of the broader machine learning field of learning representations of data.
  • learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
  • form a new field with the goal of moving toward artificial intelligence. The different levels of representation help make sense of data such as images, sounds and texts.

These definitions have in common (1) multiple layers of nonlinear processing units and (2) the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features.

While in the 4th bullet this is termed a new field moving toward artificial intelligence, it is generally considered to be part of the larger field of AI already. Deep learning and machine intelligence is not the same as human intelligence. Artificial intelligence in this definition above and in the popular press usually refers to Artificial General Intelligence (AGI). AGI and the next evolution, Artificial Super Intelligence (ASI) are the forms of AI that Stephen Hawking and Elon Musk are worried about.

This is powerful stuff no question, but as an investor, user or application developer in 2015 look for the right combination of technology, data, domain expertise, and application talent applied to a compelling (valuable) problem in order to create a disruptive innovation (value). This is where the money is over the new five years and this is our focus at ai-one.


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.

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  |  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  |  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.