there's no ai without ia with seth earley

Information Development World

November 30th, 2017

There’s No AI without IA

#idw2017

Copyright © 2017 Earley Information Science, Inc. All Rights Reserved.

Agenda

Perspective on Intelligent Technologies

Our Approach

Conversational Commerce

Search & Retrieval

Knowledge Portal

Virtual Agent

Human – Bot Collaborations

#idw2017


SETH EARLEY - BIOGRAPHY

CEO and FounderEarley Information

Science

@[email protected]

www.linkedin.com/in/sethearley

Over 20 years experience

Current work

Co-author

Editor

Member

Former Co-Chair

Founder

Former adjunct professor

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

Industry conferences on knowledge and information management

Northeastern University

Boston Knowledge Management Forum

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

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AI and The Hype Curve

“What seems to be AI, is really vast knowledge,

combined with a sophisticated UX”

https://www.theregister.co.uk/2017/01/02/ai_was_the_fake_news_of_2016/

http://www.rogerschank.com/fraudulent-claims-made-by-IBM-about-Watson-and-

AI

“The definition of “AI” has been stretched so

that it generously encompasses pretty much

anything with an algorithm”

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When it works, we don’t call it AI

5

“When [AI] finally works, it gets

co-opted by some other part of

the field. So, by definition, no AI

ever works; if it works, it’s not AI”

MIT AI Course*

*https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture1Final.pdf

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When it works, we don’t call it AI

6

AI is embedded in almost every technology we touch.

In fact, an early “AI application” was word processing- the software we take for granted applied

the judgment that a skilled typesetter would use when laying out a document.

Spell checkSelf driving cars

Speech recognition

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Copyright © 2017 Earley Information Science, Inc. All Rights Reserved. 7

Turning your friends into creepy pictures of dogs and cats

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How is AI used in the workplace?

Most AI is behind the scenes, embedded in application functionality rather

than being used as stand alone tools.

There are few “pure” AI applications that the typical knowledge worker

leverages

Most are used to identify patterns in large data sets (for example, anomaly

detection, risk analysis, customer purchase patterns, market

segmentation, next best action, demand predictions)

Unless you are a data scientist, many of these applications are not readily

usable Text Analytics, a long time staple of KM and

content management, is now called “AI”

#idw2017


What was once called Text Analytics is now called AI

9

Content/Text Analytics allows derivation of structure and identification of patterns within unstructured content and text.

• Knowledge extraction

• Mitigation of compliance risks

• Removal of Personally Identifiable Information (PII)

• Removal of Redundant, Outdated and Trivial (ROT) content

• Protection of intellectual property

• Identification of patterns of fraud

• Development of Question Answering systems

• Training of Intelligent Virtual Assistants and Chat bots

• Detection of customer sentiment

• Prediction of credit risks

• Feature extraction from product data

These approaches have always

leveraged some form of machine

learning algorithm

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Machine Learning

10

Machine learning algorithms iteratively use results of analysis to refine an outcome.

Outputs are fed back to refine how the algorithm produces an answer.

Example: Spell correction on your smart phone can learn unique spelling of words

• Unsupervised learning – look at this data and identify patterns and anomalies – “make sense

of the information”

• Supervised learning – look for this particular pattern of information based on examples

Providing multiple examples of user questions (“I need to change my password”, “I

forgot my password”, “I can’t log in”, etc) allows supervised learning to classify intent

– the user’s objective or goal.

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What is Cognitive Computing?

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

By using diverse data sources as “signals”

• 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

Source: Sue Feldman, Synthexis

…by leveraging machine learning and AI

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Perspective on Intelligent

Technologies

(There is no magic …)

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Reality versus Aspiration

Market place is crowded and noisy

Vendors hype is difficult to separate from reality

Significant amounts of functionality is aspirational

Vendors R&D will be at customer’s expense

Technology is quickly evolving and capabilities will accelerate

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Market Size vs Investment

Opus Research: Growth of

industry from $1 billion in 2016 to

$4.5 billion globally by 2021

CB Insights: $14.9 billion in investment

between 2012 and 2016

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What is the implication of $15b in funding

for companies chasing a $1b market?

You will be receiving a

lot of phone calls.

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However…

Most bot and virtual assistant vendors have not exploited scalable and portable knowledge

engineering approaches

We have not located any knowledge base driven solutions from credible vendors

Instead, content is embedded into, and fine tuned for, highly custom configurations

The approach of “give us 6 months, $2mm and all of your content” ensures lock in

The answer to “where does the data come from?”:

- “the customer has a knowledge base”

- “you need the right learning content”

Market Hype + Lack of Maturity = Many Failures

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Intelligent Virtual Assistants Are Evolving…#idw2017


“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”

All Require Domain Modeling and Knowledge Base Development

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Governance

Models

There’s no magic

Knowledge Engineer

Knowledge Engineer

Knowledge Engineer

Assistant Supervisor

Integration Engine

Domain

Models

Knowledge

Bases

Harmonized

Metadata

Quality

Data

Curated

Content

Analytics

Programs

Content

Models

… and Knowledge Engineering Requires Human Intervention …

IPSoft’s Amelia Example

Example EIS

Knowledge

Artifacts

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The Knowledge Management challenge is usually put into

language that confuses the issue:

Vendors say that they need to “train the AI”

What do you “train the AI” with?

…high value knowledge assets (quality data and curated content)

The Knowledge Management Challenge

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Bots need good content and may

not always get it right…

Pizza ordering bot is “brittle”

Facebook search degrades inelegantly

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Watson Intelligent Assistant#idw2017


Hey Facebook… …How About We Start with Search?

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The Bot Factory

Most bot approaches are analogous to hand built

automobiles at the turn of the century.

Applications are brittle, content is not reusable and

the process is costly and labor intensive.

Factories, standardized components and assembly

lines are needed to scale deployments.

This approach is one of a “bot factory”.

Virtual agent and bot technology

requires standardized treatment of

architecture, terminology and

reusable content driven by domain

specific use cases.

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APPROACHES

…include the following:

• Natural language processing of search queries

• Normalization of content with consistent domain modeling

• Classification of intent based on phrase variations

• Use of crowd sourcing to gather phrase variations and derive terminology

• Incorporation of user paths into learning process

• Analysis of click streams to identify recommendations

• Tuning of clustering, auto categorization and entity extraction algorithms

• Inference engines for reasoning algorithms

• Metrics for manual improvements of performance and for incorporation into automated approaches

• Integration of ontology modeling into downstream systems

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Even the short term challenges in managing bot information will quickly

multiply without proper planning

• It’s one thing to build a single bot, what about

when there are 10, 100, 1000, 10,000?

• Will there be a repeatable framework or a

series of projects?

• Without metrics, governance and design

elements abstracted from the tools,

organizations will find their bots out of control.

• Content and design elements (intents, entities,

utterances, responses) have to be managed

separately from the application

Organizations need to think of

bots not as a one off but a

series of channels and data

sources that have to be

managed over time

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Elements of a scalable bot approach

27

Automation – organizations need a “bot factory” model rather than a one off, special purpose bot.

This approach enables:

Reusability

• Investments in training content and knowledge assets will fully utilized

• Standardized assets and design elements can be repurposed in new bots

Scalability

• By using a combination of automated and manual approaches for extraction of ontologies and content

components, new bots can be deployed more quickly and cost effectively

• Managing design elements in a platform agnostic tool is the only way to control deployment of hundreds or

thousands of bots across multiple technologies

Portability

• Standardizing content, assets and design elements outside of bot platform prevents vendor lock in, allows

for new modules and best of breed components

• Refactored content and design elements will be managed in an ontology for migration into other bots, tools,

technologies

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Why a bot “factory” approach?

28

• Large complex problems need to be broken into smaller pieces

• Bots will be solutions to specific problems

• Alexa has 6,000 “skills” – a skill is a set of intents, triggers and content

• These components need to be managed

• Rather than programming bots one by one, creating reusable components will

reduce costs and effort

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Ontology Development#idw2017


Domain Models and Content Normalization Add Context

30

Invest in content, processes and knowledge architecture (industry specific domain models, ontologies, metadata, metrics and governance)

Questions are normalized into a vector space and matched

with responses from a knowledge base

Content gets refactored and componentized

User’s information need

(intended question)

Question variants also

form test use cases for

technology evaluation

12

3

Process efficiencies achieved by refactoring and componentizing content for reuse

the better the content the easier it will be to train the AI

A domain model is used to describe processes, products and organizational knowledge

structures

Pre-processing of content is required to add the correct knowledge context for AI programs to ingest

#idw2017


Standardized/Normalized Content is Portable and Reusable

31

Standardized

domain specific

schemas for reuse

Field 1

Field 2

Field n…

Field 1

Field 2

Field 3

Field n…

ELearning, FAQ’s,

Troubleshooting

charts, support

articles

Componentized

content

Tagging for ingestion

Componentized content can

be repurposed across tools

and technologies Improved CSR

Information Access

Faster time to value for all

information access scenarios

Portability across AI and

Chatbot systems

Improved customer self

service

Metrics aligned with specific

content performance

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TRAINING AIDS

CALL LOGS CUSTOMER PROFILE DATA

ANALYTICS & ACTIVITY

SOCIALNETWORKS

DEMOGRAPHIC & ETHNOGRAPHIC

DATA

SENTIMENTANALYSIS

SERVICES & OFFERS

CUSTOMER EXPERIENCE ENRICHED BY KNOWLEDGE

BOT MATURITY & SCALABILITY

Combining Platform Independent Knowledge with

Agent-Bot Collaboration for Scalability & Customer Experience

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CONTACT US

GENERAL INFORMATION

www.earley.com

PO Box 292Carlisle, MA 01741

781-444-0287

Seth EarleyCEO/[email protected]://www.linkedin.com/in/sethearley

Jeanna GiordanoClient [email protected]://www.linkedin.com/in/jeannagiordano

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APPENDIX

34

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Detailed Process View

35

Document

Decomposition &

Learning &

Componentization

WordMap ML

Ontology Manager

Knowledge Engineer

Dialog Designer

Dialog Development

ELearning,

FAQ’s, Troubleshooting

charts, support articles

Componentization

Domain Modeling

AI Engineer

Content Processing

Content Analysis

Ingestion into Ontology

Dialog Tagging

(Redacted view

of a client

ontology)

#idw2017


OUR CLIENTS

17

of the Fortune 10

Pharmaceuticals Companies

of the Fortune 10

Retailing Companies

of the Fortune 100

12

of the Internet Retailer Top 50

7

6

5

of the Fortune 20

Life Sciences Companies

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A BROAD SPECTRUM OF

SOLUTIONS

B2CDigital

Commerce

B2BDigital

Commerce

B2EDigital Workplace

CUSTOMER

EXPERIENCE

DIGITAL

ASSET

MGMT.

CONTEXTUAL

SEARCH

DIGITAL

COMMERCE

WEB

CONTENT

MGMT.

METRICS &

ATTRIBUTION

CONTENT

MARKETING

MASTER

DATA

MGMT.

BIG DATA

ANALYTICS

PRODUCT

INFORMATION

MGMT.

ENTERPRISE

CONTENT

MGMT.

BUSINESS

INTELLIGENCE

CONTEXT-AWARE

INFORMATION ARCHITECTURE

Strategy

Taxonomy

Metadata

Integration

Workflow

Governance

37

#idw2017


“This is awesome, you

have exceeded my

expectations on what I

thought was possible.”

Mike Barton, President

Allstate Business Insurance

WHAT CLIENTS SAY ABOUT EIS

“We spent millions

upgrading technology ….

Looking back, I’d get the

taxonomy right from the

beginning.”

Chief Marketing Officer$8B Scientific Equipment Maker

“The value that Earley

brought was visible

from the beginning -

Helping us to arrive at a

consensus and a path

forward.”

VP Product Management

High-tech Manufacturer

38

#idw2017


1994YEAR FOUNDED.

BostonHEADQUARTERED.

50+SPECIALISTS & GROWING.

Earley Information Science is a specialized information agency. We support measurable

business outcomes by organizing your data, content and knowledge assets.

Our proven methodologies are designed specifically to address product data, content

assets, customer data, and corporate knowledge bases. We deliver scalable governance-

driven solutions to the world’s leading brands, driving measurable business results.

We make information more

useable, findable, and valuable.

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0

AWARDS & RECOGNITION

2017 100 Companies that Matter in KM




2014 Trend-Setting Products Award

2013 Trend-Setting Products Award

2008 Trend-Setting Products Award (Wordmap)

2013 Applied Materials’ added to

InformationWeek 500 List of Business

Innovators

• “Cognitive Search Is Ready To Rev Up

Your Enterprise’s IQ”

• “Google-ize Your Site-Search Experience”

• “Polishing Up Your Products —

Why PIM Really Matters”

• “Artificial Intelligence Solution Landscape”

ANALYST MENTIONS

• “Unlocking the Hidden Value of

Information (Applied Materials)”

2015 KM Reality Award

(Allstate Business Insurance, ABIe project)

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THOUGHT LEADERSHIP

41

Founded in 2005

>3,400 members

worldwide

Founded in 2015

800 attendees

Educational Courses

Information

Organization and Access

Enterprise IA and

Semantic Search

Podcast on

Information

Science topics

#idw2017