1. Cognitive Computing
Discussion document
An introduction to cognitive systems tools and techniques that influence KM practitioners
development of strategies and solutions.
Presenter: Ken Martin
Email: Ken@KenMartinD.com

2. 2
Introduction to Ken Martin
Ken Martin
Summary work experience
• 25+ Years delivering business and IT transformations to National and International organizations such as: Swiss Re,
USAA, MetLife, Boeing; US Navy, US Airforce, NIH, VA, GSA, Microsoft Retail
• Leadership and practice development: Oracle, KPMG, Bearing Point, Atos Origin, HCL
• Developed web-based self-service Knowledge Analytics platform 2004-2008
– Innovator KM World Magazine: Knowledge Workers Marketplace Posted June 22, 2007
– Analytics kernel (eKM), which helps companies and their personnel transform information into reusable
knowledge solutions.
Currently
Center of Excellence Director for Business Analytics Services at HCL
• Strategic and advisory services in BI, Big Data, and Applied Analytics
• Big Data and Advanced Analytics
• Agile Analytics enterprise methodology delivery cross-functional business, data, and analytical services
19-Jan-2016

3. 3
Agenda
1 What you can expect from today’s presentation
2 Level setting on the basic terminology used to describe Cogitative Computing
4 Evolution from content creation and curation to applied analytical analysis and interaction
5 Venn Diagram of high-level Cognitive eco-system
6 Mapping Cognitive capabilities to the KM lifecycle
7 Contrasting Traditional and Cognitive Systems
8 Group discussion
19-Jan-2016

4. 4
What you can expect from today’s session
• Introduction to the basics terminology and technologies involved in Cognitive Computing
• Relating Cognitive Computing to the KM lifecycle with brief descriptions of key capabilities that enhance
delivering KM solutions
• Contrasting the current (traditional document management / content management, internet capabilities) with
cognitive computing technologies.
• Summarize the capabilities by mapping the KM lifecycle and cogitative computing capabilities
• Benefits of using cognitive computing technologies to enhance KM solutions
• Group discussion on customer interests and areas where cognitive computing impacts KM strategies
19-Jan-2016

5. Terminology
Cognition
Noun
conscious mental activities : the activities of thinking, understanding, learning, and remembering
Cognitive
Adjective
1: of, relating to, being, or involving conscious intellectual activity (as thinking, reasoning, or remembering)
2: based on or capable of being reduced to empirical factual knowledge
Cognitive computing is the simulation of human thought processes in a computerized model.
Cognitive computing involves self-learning systems that use data mining, pattern recognition and natural language processing to mimic the
way the human brain works.
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and
regularities in data, although it is in some cases considered to be nearly synonymous with machine learning.
Machine learning explores the study and construction of algorithms that can learn from and make
predictions on data.
Classification  Association  Clustering
Artificial intelligence
Noun
1 : a branch of computer science dealing with the simulation of intelligent behavior in computers
2 : the capability of a machine to imitate intelligent human behavior
• Dictionaries
• Lexicons
• Ontology
• Categorizations
• Natural Language
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6. Cognitive ComputingTraditional
The evolution from content creation and curation to applied analytical analysis and interaction
User interaction
• Static Library
• Taxonomy drive hierarchy
• FAQ
• Library of documents
Applications
• Static content
• Hierarchical search
• Ask and answer
• Limited social connections
• Document management
• Relational database systems
• Enterprise file management systems
• Limited workflow automation
Process & Store
• Internet of Things
• Any data / any device
• Integration of sensors
• Interactive interfaces support the KM lifecycle
• From create to share and enhance of content
• Continuous capture, analyze publish
• Machine learning categorization
• Advanced search –multidimensional
• Contextual analysis proposes new questions
• Shared social connections
• Store any data, any time, any volume
• Pattern recognition and machine learning
analysis and recommendations
• Integration workflow across communities
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7. Technologies behind Cognitive Computing
Cognitive Computing: simplified a broad field of study, sensor technology, computer science, data science, behavioral science, neuro science,
knowledge management and applied analytics.
Sharing information
• Written and Spoken language
• Vision
• Hearing
Limited or does not exist in digital formats
• Smell
• Taste
• Touch
• Feel
• Time
• Location
• Bio-markers / measurements
• Voice recognition
• Text to speech
• Preferences
• Images, videos
• Language / translations
• Time
• Geo-special
• Heat
• Temperature
• Pressure, depth
• Velocity, altitude, distance
• Chemicals
• Vibration, light waves etc.
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Sensors
Machine
Learning
Automation
Content
Curation
Social Media
Text Mining
SemanticsPredictive
Communications
Prescriptive
Recommend
Cognitive Computing
Corpus of data
Warbles
Internet of Things
Devices measuring People sharing
Social Networks
Enterprise ApplicationsExternal Applications
Advanced Analytics
Computer Science
Data Science
Contextual learning
& responding
Vehicles
Homes

8. Providing flexibility and agility to executing KM Strategies
Observation: Applied and theoretical “Knowledge” capture within the human experience then transferred and retained digitally in books, manuals, videos,
emails, spoken etc.
Hypothesis: Using cognitive systems provides Knowledge Management and Strategy practitioners with effective tools throughout the KM lifecycle.
KM Lifecycle stage Brief description of how cognitive system enhance KM Management and strategy
Work / Experience Individuals and organizations digitally store work history, resume, time-on-job, white-papers, presentations, job locations etc.
Create Any type of digital content: Text, video, photo, voice, geo-special, sensors
Identify Automated search creates a metadata catalog: Authors, reviewers, devices, locations, accreditations
Value Machine learning identifies: Work history, peer-reviews, accreditations
Capture Big Data storage of any data: structured, semi-structured (sensor), and unstructured data
Organize Machine learning recommendation of categorization, association, and clustering by topic, region, KM domain, security policies etc.
Validate Social network analysis (Expertise, peer-reviews)  Content analytics identifies usage, extracts terms, authors, feedback, and peer-reviews
Document Advanced text mining and text analytics links sources and methods with authors and papers. (Disambiguation and Contextual correlation)
Publish Crowd sourcing of documents, reviews, and methods is managed based on roles, responsibilities and security profiles
Share Secure role-based distribution via advanced search connecting people and social networks based on security profile
Apply Extractions and use captured as (time-on-page, downloads, contributions, reviews) survey cycles, mentions in emails and other references
Measure Applied analytics provided via rich-interactive interface at the individual, social network group and organizational level
Enhance Updates are monitored and routed near-real-time (accounting for reviews and approvals) based on changes in content and feedback
Retire Outdated content is retained per the organization policy and mapped within the metadata catalog when retired
819-Jan-2016

9. Contrasting Traditional and Cognitive Systems
Traditional system
• Pre-configured answers and rules based recommendations
• Decision support system providing results based past performance and standard math
• Content and document management systems connected to search features
Cognitive systems
Automated interactive and self-learning systems connecting people to knowledge assets while expanding the
conversation through the introduction of new content and insights.
A cognitive system:
1. Learns from interactions based on a variety of inputs including text, speech, gestures, sensors (heat, pressure, time
voltage, auditory levels), images, motion etc.
2. Adapts to language, timing, situational (cultural, life-stage, life-event)
3. Interacts and responds to the environment via (displays, text, speech, adjusting settings, movement, etc.)
4. Identifies context: Geo-special, cultural life (age)-stage, life-event , syntax, domain (knowledge and experience)
5. Extends context within limited information by proposing questions: Propose a question, respond to a question
6. Retains information and interactions based on measured outcomes
7. Retires knowledge assets when they are not longer used or content is outdated
919-Jan-2016

10. Wrap-up: Mapping the KM lifecycle to sources and Cognitive capabilities
Enterprise data sources
• Structure data (Employees, contractors)
• Personal profiles
• Education
• Work experience
External Sources
• Other public data (by subject / domain)
• Historical submission (matches)
• Internal “Wiki” data
• Social submissions
• Research (shared libraries)
Create Identify Value Capture Organize Validate Document Publish Share Apply Measure Enhance Retire
Identify & Capture Evaluate & Enrich Publish & Apply Sustain Retire
KM Officers and Sponsors
Communities of Practice
Exemplar sets / library
• Notes and reports
• Work papers (RFPs, Responses)
• Standard of Practice
• Operating Procedures
Natural Language Processing
Text Mining
• Themes / summarize content
• Categorize
• Catalog
Text Analytics
• Link phrasing, subjects etc.
Social Network Analysis
• Link authors & reviewers
• RACI Matrix
• Security profiles
Automated workflow
• Interactive dashboards connect content to
reviewers, approvers
Cognitive interactions
Machine learning and patter matching parses the document for
relevance and generates hypothesis
Evaluation is based KM structure and exemplar sets developed as
part of the analysis process
Reduces the time to (value) submission with automated analyze
and categorize (informs ontology and taxonomy) document
Categorization are viewed in rich visualization and tested for
consistency and accuracy
Internet of Things & Big Data
System capture any data / any time / any volume
Distribution and interactions to any device
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11. Benefits to applying Cognitive Computing capability in your KM strategy
• Extend expertise and personalized delivery of knowledge assets
• Simplify the submission and curation of knowledge assets
• Reduce the time to market for KM solutions
• Promote adoption with rich content, simplified access, and personalized services
• Provide additional enriched guidance as right-time responses, feedback, questions
• Deliver highly valued KM assets, fastest path to the answer
• Improve connections to the right people, their expertise, and associated knowledge assets
1119-Jan-2016

12. 12
Posing the questions for our group discussion
Let’s have a group discussion
1. Are your customer using or considering cognitive systems?
2. What capabilities do you see being used by 1st adopters?
3. What barriers to adoption do you see across your customers?
4. How do you envision cognitive technology impacting f KM strategy and methods?
19-Jan-2016

13. 13
Thank you for participating
19-Jan-2016

14. Additional information
Cognitive computing platforms
• IBM Watson
• Numenta
• Google Now
• Enterra Cognitive Reasoning Platform (CRP)
• Saffron
Emerging
• MS Cortana
• Open source custom niche’ system based on
Big Data and Advanced analytics
Use-cases include
Across-industries
• Help / service desk
• Training
• Predictive Maintenance
Insurance / Financial
• Customer Services
• Fraud detection
• Changes in customers behavior / life-event
• Multiple Industries
14
It’s time to add KM to the list of growing investment in Cognitive Computing 
19-Jan-2016

15. 15
Detailed Venn diagram of the Cognitive Computing ecosystem
19-Jan-2016