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, EISSue Feldman, SynthexisPaul Wlodarczyk, EISDino 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 WlodarczykVP, 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 EliopulosManaging DirectorEarley Information

Science


Search to Intelligent Assistant Continuum Basic

Search EngineKnowledge

PortalVirtualAgent

IntelligentAssistant

KnowledgeBase

SearchInteraction

InformationArchitecture

UserExperience

Enabling Technology

Any textMultiple sources

Keyword or full textquery

None necessary, but Improves with metadata

Search box, documents list

Search

Multiple sources, separate Ontologies and schemas

Full text query orFaceted Exploration

Ontologies, clustering,classification

Role-Based

Search, classification,databases

Domain specific Highly curated sources

Query, explore facetsOffers related info

Conversational

NLP, search, classification Process engines

Dynamic. Info enrichmentimproves with interaction

Implicit query / recommends based on users’ history

Conversational, personalized,contextual

NLP, search, classificationMachine Learning

Ontologies, clustering,classification, NLP

Ontologies, clustering,classification, NLP, personalization

Synthexis10

Copyright © 2016 Earley Information Science11 Synthexis11

Search to Intelligent Assistant Continuum

KnowledgeBase

SearchInteraction

InformationArchitecture

UserExperience

Enabling Technology

Any textMultiple sources

Keyword or full textquery

None necessary, but Improves with metadata

Search box, documents list

Search

Multiple sources, separate Ontologies and schemas

Full text query orFaceted exploration

Ontologies, clustering,classification

Role-based

Search, classification,databases

Domain specific Highly curated sources

Query, explore facetsOffers related info

Conversational

NLP, search, classification Process engines

Dynamic. Info enrichmentImproves with interaction

Implicit query / recommends based on users’ history

Conversational, personalized,contextual

NLP, search, classificationMachine Learning

Ontologies, clustering,classification, NLP

Ontologies, clustering,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 EarleyCEO and Founder


Over 20 years experience

Current work

Co-authorEditor

MemberFormer Co-Chair

FounderFormer adjunct professor

Guest speakerAIIM 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 EngineDomain 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 FeldmanCEO, Synthexis

Managing Director,Cognitive Computing

Consortium

[email protected]

© 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 DoAct 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 ChoicesWhat 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 WlodarczykVP, Client Services Earley Information

Science

Dino EliopulosManaging Director Earley Information

Science

Seth EarleyCEO


Sue FeldmanCEO

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

http://www.earley.com/knowledge/case-studies/allstate%E2%80%99s-intelligent-agent-reduces-call-center-traffic-and-provides-help

http://synthexis.com/

http://cognitive-science.info/community/weekly-update/

http://www.ibm.com/outthink

http://www.research.ibm.com/cognitive-computing/#fbid=WaBFwxRAB0M

http://www.ibm.com/blogs/think/2016/01/13/inside-ibm-the-inventors-who-are-creating-the-era-of-cognitive-computing/

http://www.amazon.com/Synthesis-Lectures-Information-Concepts-Retrieval/dp/1608459349

http://www.infotoday.com/OnlineSearcher/

http://www.cognitivecomputingconsortium.com/

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)

http://1u88jj3r4db2x4txp44yqfj1.wpengine.netdna-cdn.com/wp-content/uploads/2015/10/Intelligence-Assistant-Landscape-Final.jpg

http://www.intelliresponse.com/blog/intelligent-virtual-assistant

http://www.cmswire.com/cms/information-management/artificial-intelligence-is-resurrecting-enterprise-search-026427.php

http://info.intelliresponse.com/rs/intelliresponse/images/Opus_EvaluatingEnterpriseVirtualAssistants_Jan2014%20(2).pdf

http://virtualagentchat.com/

http://www.slideshare.net/intelligentfactors/characteristics-of-highly-effective-enterprise-virtual-assistants

http://opusresearch.net/wordpress/2016/01/12/the-knowledge-graph-and-its-importance-for-intelligent-assistance/


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:

[email protected]

[email protected]