Making Intelligent Virtual Assistants a Reality

Copyright © 2016 Earley Information Science1
Intelligent Virtual Agents
– What’s Needed to
Make Them a Reality
Copyright © 2016 Earley Information Science
Seth Earley, EIS
Sue Feldman, Synthexis
Paul Wlodarczyk, EIS
Dino Eliopulos, EIS

Today’s Agenda
• Welcome & Housekeeping
• Dave Zwicker, CMO, Earley Information Science
• Session duration & questions
• Session recording & materials
• Take the polls & the survey!
• The Panelist Point of View
• Paul Wlodarczyk, VP, Client Services, Earley Information Science
(@twitcontentguy)
• Dino Eliopulos, Managing Director, Earley Information Science
(@deliopulos)
• Seth Earley, CEO, Earley Information Science (@sethearley)
• Sue Feldman, CEO, Synthexis (@susanfeldman)
• Expert Panel Discussion
• Questions & Answers
• Join the conversation: #earleyroundtable

Copyright © 2016 Earley Information Science3 Copyright © 2016 Earley Information Science
Intelligent Virtual Agents
What’s Needed to Make Them a Reality

Paul Wlodarczyk - Biography
• VP, Client Services & Industrial Practice Lead
• Joined Earley in 2008, with 30 years’ experience in unstructured content lifecycle
and related technologies (search, content management, classification, taxonomy,
localization)
• Currently working with enterprises leading digital transformation projects
• Deep product lifecycle experience for high-tech discrete products, software, and
batch process manufacturing, and industry experience in consumer products, life
sciences, energy & water infrastructure, finance, insurance, and aerospace.
• Former CEO of Jorsek Software, makers of the easyDITA XML Content Suite
• Established ECM practices at Xerox Global Services, Blast Radius, and
JustSystems
• Sought-after speaker and writer for such industry organizations as AIIM, B2B On-
line, CIDM, ebiz, Intelligent Content, Gartner, Gilbane, KM World, LavaCon,
Linked Data, LISA, MESA, STC, and TechLearn
• MBA, William E. Simon School of Business; BA, University of Rochester
(Psychology)
Paul Wlodarczyk
VP, Client Services
Earley Information Science

in·tel·li·gent /inˈteləjənt/ adjective
(of a device, machine, or building) able to vary its state or action in response to varying
situations, varying requirements, and past experience.
as·sis·tant /əˈsistənt/ noun
a person who helps in particular work
Intelligent Assistant – a device or application that helps in particular work
that is able to vary its state or action in response to varying situations, varying
requirements, and past experience.
What is an Intelligent Assistant?

Intelligent Assistant – a device or application that helps in particular work
that is able to vary its state or action in response to varying situations, varying
requirements, and past experience.
What is an Intelligent Assistant?
• Implication of “assistant” is anthropomorphic (Siri, Cortana, ABIe,
etc.)
• How do we make it intelligent – i.e. not static – to vary its state or
action?
• Continuously curated content & information architecture
• The curator as “Mechanical Turk”
• Human Intelligence (live chat, The Amazon Turk)
• Artificial Intelligence (Inference Engine, Machine Learning, Cognitive Computing,
etc.)
• Not a continuum – these approaches can be used in combination.
"Kempelen chess1" by Carafe at en.wikipedia. Licensed under CC BY-SA 3.0 via Commons -
https://commons.wikimedia.org/wiki/File:Kempelen_chess1.jpg#/media/File:Kempelen_chess1.jpg

“A problem well-stated is a problem half-solved.” – Charles Kettering
Information architecture involves structuring the information-
seeking behavior of the knowledge worker and the content itself:
– Use cases
– Domain models
– Contextualization
– Content models / structured content
– Search curation (best bets, query suggestions, redirects)
– Taxonomy development & curation (synonyms, hierarchy)
Curation required to “feed the beast” – keep it intelligent.
Curated IA as Stepping Stone to Machine Solutions

“A problem well-stated is a problem half-solved.” – Charles Kettering
A curated knowledge-based solution lowers the risk of a machine-based
solution:
– Defines and highly strutures the problem
– Implements a more manageable, lower-cost, lower risk solution
– Makes crystal-clear the opportunities for machines to help
• E.g. – the long-tail inquiry
– Organizes the “training sets” for the machine
• Example: User “Chats” with ABIe
Curated IA as Stepping Stone to Machine Solutions

• Experienced leader and innovator in industry and high-end professional IT
consulting with deep specialization in user experience and highly complex
business applications.
• Has over 2 decades of experience in applying Machine Learning, Data
Mining and other AI techniques to deliver rich content-driven solutions for
Retail, CRM, hi-tech manufacturing, healthcare / insurance and financial
services.
• Has depth in many industries including Financial Services, Retail / CPG,
Telecommunications, Travel and Entertainment, Healthcare,
Pharmaceuticals, Hi-Tech Manufacturing and Energy.
• Expertise in all aspect of IT Professional Services including strategy,
planning, forecasting, budgeting, measurement, sales, talent acquisition /
management and retention, career stewardship, program management and
service delivery.
• Highly collaborative and results-oriented management style delivers
outstanding outcomes for his clients, his employers and his teams.
Dino Eliopulos - Biography
Dino Eliopulos
Managing Director
Earley Information
Science

Search to Intelligent Assistant Continuum
Basic
Search Engine
Knowledge
Portal
Virtual
Agent
Intelligent
Assistant
Knowledge
Base
Search
Interaction
Information
Architecture
User
Experience
Enabling
Technology
Any text
Multiple sources
Keyword or full text
query
None necessary, but
Improves with metadata
Search box, documents list
Search
Multiple sources, separate
Ontologies and schemas
Full text query or
Faceted Exploration
Ontologies, clustering,
classification
Role-Based
Search, classification,
databases
Domain specific
Highly curated sources
Query, explore facets
Offers related info
Conversational
NLP, search, classification
Process engines
Dynamic. Info enrichment
improves with interaction
Implicit query / recommends
based on users’ history
Conversational, personalized,
contextual
Machine Learning
classification, NLP
classification, NLP, personalization
Synthexis10

Synthexis11
Search to Intelligent Assistant Continuum
Knowledge
Base
Search
Interaction
Information
Architecture
User
Experience
Enabling
Technology
Any text
Multiple sources
Keyword or full text
query
None necessary, but
Improves with metadata
Search box, documents list
Search
Multiple sources, separate
Ontologies and schemas
Full text query or
Faceted exploration
classification
Role-based
Search, classification,
databases
Domain specific
Highly curated sources
Query, explore facets
Offers related info
Conversational
Process engines
Dynamic. Info enrichment
Improves with interaction
Implicit query / recommends
based on users’ history
Conversational, personalized,
contextual
Machine Learning
classification, NLP
classification, NLP, personalization
Intelligent Virtual Insurance Agent

The best results
come from a
human-machine
collaborative
approach to data
curation and
classification that
leverages both
doing what they do
best
Manual vs. automated data – a false dichotomy
• Machines leveraging seed data (e.g.,
supervised learning)
• Machines auto-classifying new data and
providing scale
• Machines mining analytics and learning,
to improve accuracy
• People identifying the best use cases
• People defining the meaningful
interactions with the data
• People creating the initial context and
examples (e.g., seed data)

Backing up an Intelligent
Assistant or Virtual Agent
with search:
– When the Intelligent
Assistant punts to
search, the value of the
information drops off
immediately
– E.g., linking to “5
Cheap Flights to
Chicago…” when I am
already in the Chicago
metro area
(Not so) Graceful degradation

Poll Question #1
How would you describe your company’s place on the Search to
IVA continuum?

Seth Earley - Biography
Seth Earley
CEO and Founder
Earley Information
Science
Over 20 years experience
Current work
Co-author
Editor
Member
Former Co-Chair
Founder
Former adjunct professor
Guest speaker
AIIM Master Trainer
Course Developer &
Master Instructor
Data science and technology, content and knowledge
management systems, background in sciences (chemistry)
Enterprise IA and Semantic Search
Information Organization and Access
US Strategic Command briefing on knowledge networks
Northeastern University
Boston Knowledge Management Forum
Long history of industry education and research in emerging fields
Academy of Motion Picture Arts and Sciences, Science
and Technology Council Metadata Project Committee
Editorial Journal of Applied Marketing Analytics
Data Analytics Department IEEE IT Professional Magazine
Practical Knowledge Management from IBM Press
Cognitive computing, knowledge and data
management systems, taxonomy, ontology and
metadata governance strategies

• Algorithm – a list of steps to solve a problem (a program)
• Machine Learning - the study and construction of algorithms that can learn
from and make predictions on data
Algorithms, Predictions, Data and Machine Learning
Source: “The Master Algorithm” by Pedro Domingos
Examples of data predictions (and therefore machine learning) –
spell correction, voice to text, handwritten character recognition,
language translations, autonomous vehicles, recommendation
engines from Amazon, Yelp, Netflix and others, ad pricing, fraud
detection, credit authorizations, route optimization, traffic
predictions, image recognition…

• At its core, is an information access mechanism
• Leverages many principles of search
• Contextualizes the user’s task, intent, objective
• Provides specific answers, not a list of
documents
• Based on use cases and user scenarios
An Intelligent Agent
Example Intelligent Agent
Outcome: Improved Call Center Efficiency and Agent
Productivity

Intelligent Agents Are Evolving

“But even those personalities required
proficiency in other facets of the technology
such as an expertly developed domain model”
“Because intelligent virtual assistants are
focused within a domain model, they benefit
from a clearly defined knowledge base and are
able to go much deeper and stay within those
bounds…”
Source: Analyst Gigaom Research https://gigaom.com/2014/09/01/the-next-step-for-intelligent-virtual-
assistants-its-time-to-consolidate/
“…domain models and ontologies are important”
But Require Domain Modeling and Knowledge Base
Development

All Require Knowledge a Architecture
Knowledge Engineer
Knowledge Engineer
Knowledge Engineer
Assistant Supervisor
Integration Engine
Domain models
Knowledge bases
Harmonized metadata
Quality data
Curated content
Governance models
Analytics programs
Content models
And Human Intervention

We Are in for Some Hype…
Virtual Personal
Assistants
Natural Language Question
Answering
Machine Learning
…and Disappointment

Hey Facebook…
…How About We Start with Search?

Machine Learning is not Perfect

• Currently: Synthexis (CEO): a business advisory service specializing in
cognitive computing, search and text analytics technologies.
• Cognitive Computing Consortium (Managing Director and co-founder)
• Frequent speaker, writer and commentator on cognitive computing,
conversational systems, big data technologies, and the hidden costs of
information work.
• History: 25+ years shaping market research on user information interaction,
search and text analytics.
– IDC: VP for Search and Discovery (research on the technologies and markets for
search, text analytics, categorization, translation, mobile and rich media search)
– Author of The Answer Machine (Morgan & Claypool, 2012)
– Datasearch (President)
• Education: Cornell University, University of Michigan
Sue Feldman - Biography
Sue Feldman
CEO, Synthexis
Managing Director,
Cognitive Computing
Consortium
sue@synthexis.com

© 2015
Synthexis
Cognitive Computing…
…makes a new class of problem computable:
• Ambiguous, unpredictable
• Conflicting data
• Require exploration, not searching
• Need to uncover patterns and surprises
• Shifting situation, goals, information
• Best answers based on context
• Problem solving: beyond information gathering

© 2015
Synthexis
What Cognitive Systems Do
Act as an intelligent partner:
– Analyze BIG data
– Understand human language on multiple levels
– Analyze and merge all formats and sources of information
– Uncover relationships across sources
– Understand and filter by context
– Find patterns in the data that are both expected and unexpected
– Learn from new information, new interactions

© 2015
Synthexis
Cognitive Computing Systems
• Highly integrated: Search, BI, analytics, visualization, voting algorithms, categorization,
statistics, machine learning, NLP, inferencing, content management, voice recognition,
etc.
• Meaning-based
• Probabilistic
• Iterative and conversational
• Interactive
• Contextual
• Learn and adapt based on interactions, new information, users
• Big data knowledge base—multiple sources, formats
• Analytics: text, predictive, BI…

© 2015
Synthexis
When to Use Cognitive Technologies
• Diverse data sources, including unstructured (text, images)
• No clearly right answers:
– Data is complex and ambiguous
– Conflicting evidence
• Ranked (confidence scored) answers are acceptable
• Process intensive and difficult to automate because of unpredictability
• Time dependent: need up to the minute information (evidence) to support
decisions
• Big data
• Exploration is a priority
• Interaction on human terms

© 2015
Synthexis
And When NOT…
• When predictable, repeatable results are required (e.g. sales
reports)
• When all data is structured, numeric and predictable
– e.g. Internet of Things
• When shifting views and answers are not appropriate or are
indefensible due to industry regulations
• When interaction, especially in natural language, is not
necessary
• When a probabilistic approach is not desirable

© 2015
Synthexis
First: Frame the Project
• What is the goal of the project? What do you want to hand
to your users?
• Who are the users? Level of skill
• Speed
• Variety: Formats and sources
• Volume
• Velocity

© 2015
Synthexis
Trade-offs and Choices
What is good enough? It depends on the use:
• Serendipity vs. high confidence level
• Preprocessing and ingestion: depth vs. speed
• Speed of response: real time vs. a few seconds, days, or weeks
• Impact of outcome: life and death vs. trend detection in social media
• Thoroughness and type of data
• Thoroughness of analysis
• Type of use: question answering/monitoring/trend analysis/risk alerts/customer
interaction…

© 2015
Synthexis
The Future is Cognitive
• Improved, individualized healthcare
• A cognitive-assisted stockbroker
• Improved, individualized sales and customer support
• Computer-orchestrated political campaigns
• Executive Advisor: tells you the top 3 things to pay attention to
Interactive, Contextual, Personalized, Relevant

Poll Question #2
What topic areas would be most helpful for your situation?

Panel Discussion

Roundtable Discussion
Paul Wlodarczyk
VP, Client Services
Earley Information
Science
Dino Eliopulos
Managing Director
Earley Information
Science
Seth Earley
CEO
Earley Information
Science
Sue Feldman
CEO
Synthexis

Suggested Resources
• Allstate’s ABIe project case study http://www.earley.com/knowledge/case-studies/allstate%E2%80%99s-intelligent-agent-reduces-call-
center-traffic-and-provides-help
• Sue Feldman’s website http://synthexis.com/
• Cognitive Systems Institute Group website http://cognitive-science.info/community/weekly-update/
• IBM Watson Outthink website www.ibm.com/outthink
• IBM Research on Cognitive Computing http://www.research.ibm.com/cognitive-computing/#fbid=WaBFwxRAB0M
• Inside IBM: The Inventors Who Are Creating the Era of Cognitive Computing http://www.ibm.com/blogs/think/2016/01/13/inside-ibm-the-
inventors-who-are-creating-the-era-of-cognitive-computing/
• The Answer Machine by Susan Feldman. Morgan & Claypool, 2012 http://www.amazon.com/Synthesis-Lectures-Information-Concepts-
Retrieval/dp/1608459349
• Online Searcher Magazine, Jan-Feb. 2016 issue (v.40 no. 1). P. 38. “If I only had a(other) brain.” By Sue Feldman
http://www.infotoday.com/OnlineSearcher/
• Cognitive Computing Consortium http://www.cognitivecomputingconsortium.com/
• Enterprise Search: 14 Industry Experts Predict the Future of Search http://www.docurated.com/enterprise-search/enterprise-search-14-
industry-experts-predict-future-search

• Intelligent Assistance Landscape http://1u88jj3r4db2x4txp44yqfj1.wpengine.netdna-cdn.com/wp-content/uploads/2015/10/Intelligence-
Assistant-Landscape-Final.jpg
• Intelligent Virtual Assistants. Virtual Agents. What’s the Difference? http://www.intelliresponse.com/blog/intelligent-virtual-assistant
• Artificial Intelligence is Resurrecting Enterprise Search http://www.cmswire.com/cms/information-management/artificial-intelligence-is-
resurrecting-enterprise-search-026427.php
• Evaluating Enterprise Virtual Assistants
http://info.intelliresponse.com/rs/intelliresponse/images/Opus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).pdf
• Intelligent Virtual Agent and Intelligent Personal Assistant News and Views http://virtualagentchat.com/
• Characteristics of Highly Effective Enterprise Virtual Assistants http://www.slideshare.net/intelligentfactors/characteristics-of-highly-
effective-enterprise-virtual-assistants
• Artificial Intelligence Is Almost Ready for Business https://hbr.org/2015/03/artificial-intelligence-is-almost-ready-for-business
• The Knowledge Graph and Its Importance for Intelligent Assistance http://opusresearch.net/wordpress/2016/01/12/the-knowledge-graph-
and-its-importance-for-intelligent-assistance/
Suggested Resources (continued)

Earley Information Science helps
organizations establish a strong
information architecture and content
management foundation
Realize your digital transformation
vision with EIS.
Earley Information Science (EIS)
Information Architects for the Digital Age
Founded – 1994
Headquarters – Boston, MA
www.earley.com
For more info contact:
info@earley.com
careers@earley.com

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