Communicating information to each other is at the heart of the human experience. Data, and the analysis of it, often drives this communication in a business setting. This session aims to give you an understanding of how advances in Artificial Intelligence, specifically Natural Language Interaction (NLI), Natural Language Generation coupled with Deep Learning can create new and exciting opportunities for building analytical based chatbots. We will talk about how to design and train an NLI system that map requests to deep learning pipelines to derive insights. You will also learn how to apply NLG templates to help facilitate improved understanding and interaction with the chat-bot.

1. Ben Tomlinson & Andre Violante, SAS
Conversational Artificial
Intelligence
#AISAIS17

2. Agenda
• SAS
• Architecture
• Demo
– Building a bot
– Running a bot
• Q&A
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3. Broad Range of
Capabilities
• Reporting - BI
• Explore - Data Visualization
• Forecast – Time Series
• Statistics – Explain
• Prediction –ML and AI
• Optimize – Best outcome
The Analytics Process
Foundation for
Analytics
• Access - All sources
• Build – Merge and transform
• Profile – Explore your data
• Fix Quality - Standardize
• Governance and Lineage
Creation of Analytic
Assets
• Centrally Manage
• Deploy in database
• Create Efficiencies
• Control & Adjust
ModelAccess Explore AnalyzeCleanse MonitorIntegrat
e
Govern Embed
DISCOVERYDATA DEPLOYMENT
AUTOMATE &
ORCHESTRATE
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4. WHAT DO THEY DO?
Analytics Personas
Understands the business
context of underlying data & is
focused on answering a defined
question.
Provides guidance and support
to stakeholders by building
reports and interpreting results.
Extends the business
analyst work pattern with
data preparation, exploration
and visualization.
Explores the use of
advanced analytic modeling
techniques to solve a
particular business problem
using wizards and prebuilt
logic.
Performs complex exploratory
analysis, descriptive
segmentation and predictive
modeling.
Investigate advanced analytic
techniques and machine
learning.
Identifies new data needed for
model development and defines
analytical tables.
Facilitates data preparation,
model deployment and
monitoring.
Integrates analytics into
existing production systems
and business processes.
Develops new applications
with embedded analytics.
Leverages documented,
public REST APIs to
reference shared models
and individual actions.
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WHAT DO THEY DO?
Flexible, easy-to-use graphical
user interfaces.
Some coding capability.
Agility and consistency.
WHAT DO THEY NEED?WHAT DO THEY NEED?
Flexible, easy-to-use graphical
user interfaces
A choice of tools.
Advanced coding capability.
Ability to scale methods
without redefining code.
WHAT DO THEY NEED?
Uptime and reliability.
WHAT DO THEY NEED?
Portability and
consistency.
WHAT DO THEY NEED?
WHAT DO THEY DO? WHAT DO THEY DO? WHAT DO THEY DO?
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5. Diversity
Consistent Visual Interfaces
Programming Interfaces
API Interfaces
Support all users
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6. DATA
DISCOVERY
TIME SERIES
FORECASTING
STATISTICS
DATA & TEXT
MINING
MACHINE
LEARNING
OPERATIONS
RESEARCH
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7. Natural Language Processing
Natural language processing (NLP) is a branch of artificial intelligence that
helps computers interpret, analyze, and manipulate human language
Natural Language Processing
Machine
Translation,
OCR, etc.
Speech/Audio
Processing
Text Analytics
Information Extraction
Text Classification
Sentiment Analysis
Topic Modeling
Summarization
…
Natural
Language
Understanding
Natural
Language
Generation
SAS Visual Text Analytics
(VTA)
Natural Language
Interaction
#AISAIS17

8. Chatbot Business Benefits
Ø Operational Efficiency
Reduce mundane tasks, improve speed
and consistency of case handling, lower-
cost channel
Ø Availability
24/7 always-on access, multiple
concurrent sessions
Ø Global Reach
Bots can be trained to communicate in
multiple languages
Ø Smarter Responses
More opportunities to embed advanced
analytics for better outcomes (retention,
cross-sell, upsell)
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9. Sample NLU/NLG Use Cases
Voice
activated/
enabled
devices and
apps
Automated
Image or Text
Captioning
Call Steering
and IVR
Smart Search
Automated
reports,
summaries,
or narratives
Machine
Learning
Model
Explanability
#AISAIS17

10. Voiceactivated/
enableddevices
andapps
Automated
Imageor Text
Captioning
CallSteeringand
IVR
Smart Search
Automated
reports,
summaries, or
narratives
Machine
LearningModel
Explanability
Today’s Focus: Chatbot Use Case
Chatbots
• Customer service bot
• Order fulfillment
• Q & A systems
• Therapy bot
(“Woebot”)
• Law bot
• Health bot
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11. #AISAIS17

12. USER APPLICATION
NATURAL
LANGUAGE
UNDERSTANDING
BOT LOGIC ACTIONS
Conceptual Architecture
Incomplete intent:
prompt for more
info
User input
NLU returns
predicted intent
and
parameters/
slots Submit inputs
to execution
engine
Submit outputs
to speech
template
Response
NATURAL LANGUAGE
GENERATION
If Speech-to-Text
and Text-to-Speech
are relevant, that is
handled by the
application layer
Enough
info to
fulfill
intent?
NO
YES
Submit retained
+ new info to
NLU
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13. Demo