Watson DevCon 2016: Why Audience Intelligence Requires a Modular AI Approach

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Presentation given by Francesco D'Orazio at Watson DevCon 2016. Not all data is created equal. Different datasets, industries, audiences, and use cases require different techniques to make sense of the data and help craft insights beyond simple analytics. At Pulsar, we deal with complex and varied social and behavioral datasets that require a very diverse range of data mining techniques—and there’s only so much a single company can do to keep up with the level of specialism needed to make sense of it all.Pulsar has built a modular system for AI solutions. Integrating Watson has been essential in helping to support a wide range of research use cases – from image analysis to topic extraction and emotion analysis. Come explore different Watson-enabled use cases and hear plans for future integrations.

Technology

Watson Developer Conference 2016
Francesco D’Orazio
Why Audience Intelligence Requires
a Modular AI Approach

AUDIENCE
INTELLIGENCE
ACTIONABLE
INSIGHTS
ARTIFICIAL
INTELLIGENCE

The challenges of human data
MULTI-MEDIA MULTI-DOMAIN CONTEXT-DEPENDENTMULTI-DIMENSIONAL

SPECIALIZED
VERTICAL
CUSTOM
A modular architecture for AI on demand

CORE SERVICES MODULAR SERVICES
How Pulsar integrates Watson

How Pulsar integrates Watson
SELECTAN ALGORITHM AT STUDY SETUP
MODULES

How Pulsar integrates Watson
IMAGE
CLASSIFICATION
animal mammal cat car

How Pulsar integrates Watson
TEXT EXTRACTION
“leningrad ghetto god” 92.81%

How Pulsar integrates Watson
TEXT IDENTIFICATION
“Mc Donald’s” 83.51% “Halloween” 93.81% “Burger King” 79.15%

How Pulsar integrates Watson
TEXT IDENTIFICATION

How Pulsar integrates Watson
EMOTION ANALYSIS

TRAINING MODULE
What’s next: build your own classifiers

Thank you.
Join me for a Meet the Experts
session to ask questions and
continue the discussion!

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Watson DevCon 2016: Why Audience Intelligence Requires a Modular AI Approach

1. Watson Developer Conference 2016 Francesco D’Orazio Why Audience Intelligence Requires a Modular AI Approach

4. The Pulsar platform

5. Some of our partners

6. How do they use Pulsar AUDIENCE UNDERSTANDING TREND ANALYSIS PLANNING INSIGHTS MARKETING MEASUREMENT CATEGORY MAPPING BRAND HEALTH

7. AUDIENCE INTELLIGENCE ACTIONABLE INSIGHTS ARTIFICIAL INTELLIGENCE

8. The challenges of human data MULTI-MEDIA MULTI-DOMAIN CONTEXT-DEPENDENTMULTI-DIMENSIONAL

9. SPECIALIZED VERTICAL CUSTOM A modular architecture for AI on demand

10. CORE SERVICES MODULAR SERVICES How Pulsar integrates Watson

11. How Pulsar integrates Watson SELECTAN ALGORITHM AT STUDY SETUP MODULES

12. How Pulsar integrates Watson IMAGE CLASSIFICATION animal mammal cat car

13.

14. auto-pilot

15.

16. family

17. loyalty

18. How Pulsar integrates Watson TEXT EXTRACTION “leningrad ghetto god” 92.81%

19. How Pulsar integrates Watson TEXT IDENTIFICATION “Mc Donald’s” 83.51% “Halloween” 93.81% “Burger King” 79.15%

20. How Pulsar integrates Watson TEXT IDENTIFICATION

21. How Pulsar integrates Watson EMOTION ANALYSIS

22. How Pulsar integrates Watson EMOTION ANALYSIS

23. How Pulsar integrates Watson EMOTION ANALYSIS

24. TRAINING MODULE What’s next: build your own classifiers

25. Thank you. Join me for a Meet the Experts session to ask questions and continue the discussion!

Watson DevCon 2016: Why Audience Intelligence Requires a Modular AI Approach

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