The document provides an overview of cognitive solutions using Watson APIs. It discusses IBM's cognitive reference architecture, emerging cognitive patterns, best practices with Watson APIs, the art of conversation design, and future trends. A case study is presented on using Watson to help customers complete the mortgage application process for an Italian bank.

1. Building Cognitive
Solutions with Watson APIs
University of Jyväskylä 2.2.2017
Jouko Poutanen
Cognitive Solution Architect

2. Agenda
• Cognitive Reference Architecture
• Emerging Cognitive Patterns
• Best Practices with Watson APIs
• The Art of Conversation Design
• Future Trends

3. Watson in Different Industries Today
https://youtu.be/PujCkDAXji8

4. Cognitive Reference Architecture

5. 5
Advisors
Developer Cloud
Specialties
Models
Content
Tooling
Assemble
Train
Deploy
Admin
Data Services IngestExtract AnnotateCurate
Design
Engagement Discovery
Decision Policy
Cross Industry Editions
Oncology Wealth Mgmt.
Intelligence Cooking
Target Industry Editions Powered by Watson Offerings
App Store
Healthcare
Financial Svc.
Travel
...
Call Center
User Profiling
Research
...
Core Offerings Watson Analytics Watson Explorer
Industry Aligned Market Aligned
Visualize
Cognitive Services (APIs)
The same services are used by business partners, customers, and IBM Developers.
Watson Portfolio (partial)

6. © 2015 INTERNATIONAL BUSINESS MACHINES CORPORATION
Relationshi
p
Extraction
Question
s
&
Answers
Languag
e
Detectio
n
Personalit
y
Insights
Keyword
Extraction
Image
Link
Extraction
Feed
Detection
Visual
Recognition
Concept
Expansion
Concept
Insights
Dialog
Sentime
nt
Analysis
Text to
Speech
Tradeoff
Analytic
s
Natural
Languag
e
Classifie
r
Author
Extraction
Speech
to
Text
Retrieve
&
Rank
Watson
News
Language
Translatio
n
Entity
Extractio
n
Tone
Analyzer
Concept
Tagging
Taxonomy
Text
Extraction
Message
Resonanc
e
Image
Tagging
Face
Detectio
n
Answer
Generation
Usage
Insights
Fusion
Q&A
Video
Augmentatio
n
Decision
Optimizatio
n
Knowledge
Graph
Risk
Stratification
Policy
Identificatio
n
Emotion
Analysis
Decision
Support
Criteria
Classificatio
n
Knowledge
Canvas
Easy
Adaptatio
n
Knowledg
e Studio
Service
Statistic
al Dialog
Q&A
Qualificatio
n
Factoid
Pipeline
Case
Evaluation
6
The Waston that competed on
Jeopardy! in 2011 comprised what
is now a single API—Q&A—built
on five underlying technologies.
Since then, Watson has grown to
a family of 28 APIs.
By the end of 2016, there will
be nearly 50 Watson APIs—
with more added every year.
Natural
Language
Processing
Machine
Learning
Question
Analysis
Feature
Engineering
Ontology
Analysis

7. This is the runtime architecture
which showcases the components
that are involved in the usage of a
trained and deployed Cognitive
Engagement System
Cognitive-Reference Architecture
IBM Architecture Center
https://www.ibm.com/devops/method/content/
architecture/cognitiveArchitecture

8. Developer
Administrator
Solution User Develops Custom
Application Componentry +
UI
Local User Administration
Analyzes Usage Metrics
IBM
Administrator
Manages Cloud Based
Services
Analyzes Usage Metrics
Client
Systems
Data Sources
Subject
Matter Expert
Provides context specific data
Executes business transactions
Content
Curator
Manages Corpus Content
Writes/Edits Content
Finds Content for Corpus
Creates Training Data
8
© 2015 International Business Machines Corporation
Other
Services
Provide additional functionality to
extend the capability of the base
solution
Watson powered
solution
Responses
Interactions
Client Content
Training Data/models
Content
Writer/Editor
Watson High Level Reference Architecture – System Context View
Process
Author
Creates /Updates Processes
Maintains Processes

9. Emerging Cognitive Patterns
Best Practices with Watson APIs

10. Cognitive technology will also lead to industry transformation, e.g. in healthcare

13. The rest of the used materials are in the course site.

14. The Art of Conversation Design

15. • Getting the conversation design right requires information, skills and expertise
• Designing effective and engaging conversational interaction that achieves your clients’
aims can be harder than it seems
• It draws on skills other than ‘hands on the keyboard’ Dialog skills
– Language skills
– Strategic thinking
– Deep knowledge of your client’s business and their customers (the end user)
– Psychological insights
– how people interact conversational solutions (virtual assistants)
– how to establish trust and achieve behavior change
Introduction

16. Introduction
• How do we get the conversation design right?
– Carefully designing key moments in the conversational interaction
– Using proactive and reactive behavior in the right balance
– Proactively engaging users at the right time with key messages and
questions
– Using the right language
– Developing the right approach to ‘chit chat’
– Leveraging profiling capability to
– Keep track of things about the user and tailor the interaction to them
– Gather key information about users’ interests, concerns, behaviors
– Ensuring UI behavior supports the conversational interaction
– And so on …

17. The Elements of Conversation Design
– Understanding the benefits of conversational solutions (virtual assistants)
– Positioning a conversational solution
– Defining the purpose
– Identifying the view point
– Specifying the proactivity
– Defining tone and personality
– Designing the right approach to ‘chit chat’
– Writing for conversational interaction
derive

18. Case Study – xCredit Prototype
Client’s Problem: In Italy, the process of getting a mortgage is very long and convoluted,
for both the bank customer and the branch manager. Life-time renting is common. A lot of
bank customer give up part-way through the mortgage application process
Client’s Vision: xCredit wants to leverage Watson technology to increase the number of
customers who complete the mortgage application process, and assist branch managers in
their mortgage-related work
Defining the purpose

19. Case Study – xCredit Prototype
•Initial Dialog scope – before conversation design:
– Purpose: To answer questions about mortgages
– Conversational elements: Intro statements, some off-topic Q&A, a simple
process flow to help customers choose a mortgage
Defining the purpose

20. Case Study – xCredit Prototype
•Revised Dialog scope after conversation design:
– Primary purpose: Watson should act as a facilitator in the relationship
between the branch manager and the bank, to support both parties through the
process
– Conversation design:
– Watson proactively drives the conversation with the customer, guiding
them through processes, asking questions, suggesting things they might
want to know about, or need to do; provides up-to-date information to
keep the customer informed about the process and next steps
– Watson proactively prompts the branch manager with information and
reminders and the customer’s mortgage application and required next
steps with customer and bank manager
Defining the purpose

21. Case Study – xCredit Prototype
•Result:
– The conversational part of the solution took a leading and guiding role, with the
long-tail solution providing on-topic question-answering capability
– The scope leveraged the technology to address the client’s problem in a way that
met the client’s vision and showed the power of cognitive technology in this
context
Defining the purpose

22. This Is the runtime architecture which showcases the components
that are involved in the usage of a trained and deployed Cognitive
Engagement System
Cognitive
Reference
Architecture
IBM Architecture Center
https://www.ibm.com/devops/method/con
tent/architecture/cognitiveArchitecture

23. Future Trends

24. Smart Care Room
https://youtu.be/VWCL72V4zEw

25. Hotel Concierge Powered by Watson
https://youtu.be/jC0I08qt5VU

26. Check Out – Project Intu
• http://www.ibm.com/watson/developercloud/project-intu.html

27. 27
Cognitive Computing Will Evolve Over Five Dimensions
What are the various types of
inputs it can sense and interpret?
How ubiquitous is the
capability?
How personalized
and interactive is it?
How can capability
scale to meet demand?
What is the degree
of autonomy in
learning?
Scalability Evolving
Dimensions
Learning
Ubiquity Sensing
Personalized
Interaction
• from passive to active
• interaction with each other, collective
intelligence
• understand the locative and temporal
context
• Unsupervised learning of new concepts
• selftraining to be experts
• Able to process e.g. video, image,
audio
• market place of millions of cognitive agents
or avatars
• personal virtual assistants
• part of our daily lives
• As a fabric via APIs
• Cognition-as-a-Service (CaaS)