Ian Cameron is the VP of Media & Entertainment at Expert System, a large European AI vendor. He gave a presentation on developing an AI strategy. He discussed how AI can enrich media company's content by automatically classifying it and finding related stories. He also proposed using AI to automatically generate ontologies and taxonomies by feeding them with company content. Implementing an AI strategy requires experts, testing, and careful planning and execution. AI is not a magic solution and has limitations, but can solve real problems when done right.

1. Ian Cameron, VP Media & Entertainment
AI
MAKE THE HIDDEN SEEN
Presentation Prepared for
DIGIPUBLISH

2. Agenda
≠
AI

3. • Expert System is the largest European
vendor of Artificial Intelligence & Text
Analytics software and solutions
• Public company (EXSY) with offices in
Europe and North America; R&D labs in
Italy, France, Spain and the USA
• 250+ global employees
• Award-winning, patented technology
• The Cognitive Computing technology of
choice for enterprises in all sectors and
government agencies
Global Organization

4. Selected Media & Entertainment References

5. Who is Ian Cameron?

6. Artificial Intelligence:
What is it?
 Sometimes it seems like science fiction
(or magic), but Artificial Intelligence is
always scientific and predictable
 As a science, it advances step by step,
with continuous improvements: a
sudden revolution from one day to the
next is highly improbable
 AI matches the results of some human
cognitive processes using approaches
and methods that are different from
the ones of the human brain

7. More importantly is it product or a strategy?
• Natural Language Processing (NLP)
• Natural Language Generation (NLG)
• Robotic Process Automation (RPA)
• Machine Learning
• Supervised
• Unsupervised
• Voice to Text (V2T)
• Image Recognition
• Deep Learning
• BioMetrics
• ????
What is AI?

8. IDEA #1 –Today External Content enters your content storage system
Wires/Feeds
• Financial
• Subscription (AP,
Reuters, etc.)
Archive/Legacy
Content
Social
Media
Public
Domain
Current
Content
CMS/DAM

9. An AI strategy in your workflow tomorrow
AI Strategy for
Content Enrichment
Content classification
i.e. Identification of people names,
places, events, organizations, topics,
facts and quotes in content
Related stories and
content recommendation

10. Potential Information Workflow
Wires/Feeds
Archival
& Current
Client CMS/DAM
Bureau/Newsroom
News
Sports
Fashion &
Entertainment
Sentiment
Emotion
Analytics
Trailers
Reviews
Analytics
Enriched Content in your CMS/DAM
Advertising/Marketing Film/Television
Social
Media
Public
Domain
Automated Source
Collection & Alerting
Content Creation
Classification Methodology

11. AI enhanced network connects knowledge
to content creators

12. ROI – Idea #1
AI Enriched Content – Upstream
• Increased Content Generation = Incremental Revenue Increase
• Automated Categorization & Extraction = Reduced Production Costs
• Asset Harmonization = Discoverability
• Disambiguated Knowledge Graph = Relevant Insights

13. Idea #2 - Use an AI strategy to automate Ontology/Taxonomy
creation. (classification strategy)
Some definitions:
Ontology : a conceptual model representing a specific field of information
• Classes (e.g. “person”, “organization”, “dog”)
• Attributes of classes (“age”, “birth date”, “foundation date”)
• Relationships between classes (“marry”, “own”, “hire”)
Taxonomy : is the science of classification according to a predetermined system – hierarchical
• Taxonomy is the process of naming and classifying things such as animals and plants into groups within a
larger system, according to their similarities and differences.
Knowledge Base (KB) : contains the ontology model plus the instances of
• Classes (“John Smith”, “Expert System”, “Buddy”),
• Properties (“John Smith is 25 years old”)
• Relationships (“Expert System hired John Smith”)
• Adding instances to an ontology model = “populating” the ontology

14. High-Level Workflow
METADATA
APPLICATIONS
SEARCH
ANALYTICS
VISUALIZATION
FACETS
RECOMMENDATIONS
PORTALS
TOPIC PAGES
LINKED DATA
KNOWLEDGE BASES
ONTOLOGY
DESIGN
DATABASE/BIDOCUMENTS
NLP
ENGINE
NLP
DESIGN
…
Subject
Matter ExpertSME
End-User
U
CREATE
ONTOLOGY
MODEL
1.
NLP ENGINE
FEEDS YOUR
ONTOLOGY
3.
CREATE
NLP
PIPELINE
2.

15. ROI – Idea #2
• Feeding your ontology/taxonomy is essential
• AI strategy leverages semantic reasoning to :
• Automatically feed your ontology/taxonomy based on your content
• Resolve ambiguity for a comprehensive view of each entity
• Maintain up-to date knowledge
• The result ? A well-fed ontology/taxonomy
• Provides end-users with relevant insights based upon knowledge extracted
from your content

16. Copyright © 2018 Expert System - All Rights Reserved - Slide 17 Confidential – do not distribute
Deep indexing and Data Extraction at
• Goals : Dow Jones Professional Information Business
• Drive a Compelling User Experience with Efficient Search & Alerting
• Create a Differentiated Data Offering
• The Solution : Expert System
• Speed and Quality critical
• Combining Classification and Extraction approaches
• 11 languages
• Classification
• Applying Dow Jones Intelligent Identifiers
• 3000+ Region/Topic/Industries
• Extraction
• First Area : Regulatory & Compliance :
• Special Interest People & Adverse Media Entities
Processing 1.5 Million Articles Every Day

17. ‘‘ AI is critical
to our business’’
Bob Pashinsky
Director, Content Strategy & Metadata

18. Copyright © 2018 Expert System - All Rights Reserved - Slide 19 Confidential – do not distribute
Creating Meaningful Metadata at
• Goals
• Tag movie clips with meaningful metadata
• Resolve cost and inconsistency of Manual indexing
• The Solution : Expert System AI Capable Deployment
• Harmonize terminology and clip tagging based on SONY term taxonomy
• Real-time Taxonomy validation & enhancement
• Indexers spend less time validating tags
• Seamless integration with other platforms via Expert System REST API’s
Overview

20. AI : State of the Art
 AI is here to stay: there are many
problems that can be solved right now
in a very effective way
 To implement a solution based on AI,
you need experts, time, analysis and
hard work it is no different than
anything else in the world
 Some use cases which may seem to be
a good fit for AI are not, cost analysis
before implementation of any AI
strategy may show that it is not cost
effective

21. AI after the Hype
 It is not the solution to all world’s
problems and not the most important
after the discovery of the wheel
 It is not so advanced that a small
amount of work can solve complex
problems and it cannot learn without
supervision;
 Remember what Edison said about
genius:
“Genius is one percent inspiration and
ninety-nine percent perspiration”

22. AI after the Hype
 It is not machine learning or deep
learning: ML is only a very common
strategy used in many AI use cases
 It is not an omniscient program that
only needs to be configured or trained
to solve real problems
 It is not something that programs itself
and functions without the help of
experts which anyone can easily adapt
to solve real problems

23. AI : State of the Art
 Implementing an AI solution always
means programming a computer, in
one way or another
 In many cases, the best way to
understand if an AI project can be a
success is to do a Proof of Concept
 AI is not the General Intelligence of
human beings
 An existing process doesn’t need to be
necessarily replicated in the same way
when AI is used

24. Hints for a Successful AI
Strategy
 A good amount of healthy skepticism is
always a good start to set the right
expectations for AI
 Have confidence in the value of AI and
of experience: sometimes the road can
be long but at the end the results are
real, solid and measurable
 Start with a gradual approach: targeting
a step-by-step implementation of an AI
Strategy is often the best recipe for
success

25. Thank You / Your
Questions
Ian Cameron, VP Media & Entertainment
icameron@expertsystem.com
443 979 3869