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1 .Conversational Artificial
Intelligence
Ben Tomlinson & Andre Violante, SAS
#AISAIS17
2 .Agenda
• SAS
• Architecture
• Demo
– Building a bot
– Running a bot
• Q&A
#AISAIS17
3 . The Analytics Process
DATA DISCOVERY DEPLOYMENT
Access Integrat Cleanse Explore Analyze Model Govern Embed Monitor
e
AUTOMATE &
ORCHESTRATE
Foundation for Broad Range of Creation of Analytic
Analytics Capabilities Assets
• Access - All sources • Reporting - BI • Centrally Manage
• Build – Merge and transform • Explore - Data Visualization • Deploy in database
• Profile – Explore your data • Forecast – Time Series • Create Efficiencies
• Fix Quality - Standardize • Statistics – Explain • Control & Adjust
• Governance and Lineage • Prediction –ML and AI
• Optimize – Best outcome
#AISAIS17
4 . Analytics Personas
2 3 4 5
1
WHAT DO THEY DO? WHAT DO THEY DO? WHAT DO THEY DO? WHAT DO THEY DO? WHAT DO THEY DO?
Performs complex exploratory
Understands the business Extends the business analysis, descriptive
context of underlying data & is analyst work pattern with Facilitates data preparation, Develops new applications
focused on answering a defined segmentation and predictive model deployment and with embedded analytics.
data preparation, exploration
question. modeling. monitoring.
and visualization. Leverages documented,
Provides guidance and support Investigate advanced analytic Integrates analytics into public REST APIs to
Explores the use of
to stakeholders by building techniques and machine existing production systems reference shared models
advanced analytic modeling learning.
reports and interpreting results. techniques to solve a and business processes. and individual actions.
particular business problem Identifies new data needed for
using wizards and prebuilt model development and defines
logic. analytical tables.
WHAT DO THEY NEED? WHAT DO THEY NEED? WHAT DO THEY NEED? WHAT DO THEY NEED? WHAT DO THEY NEED?
Flexible, easy-to-use graphical Flexible, easy-to-use graphical A choice of tools. Uptime and reliability. Portability and
user interfaces user interfaces. Advanced coding capability. consistency.
Some coding capability. Ability to scale methods
Agility and consistency. without redefining code.
#AISAIS17
5 . Diversity
Support all users
Consistent Visual Interfaces
Programming Interfaces
API Interfaces
#AISAIS17
6 .DATA TIME SERIES STATISTICS
DISCOVERY FORECASTING
DATA & TEXT MACHINE OPERATIONS
MINING LEARNING RESEARCH
#AISAIS17
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
Text Analytics Natural Speech/Audio
Language Processing
Information Extraction Understanding
Text Classification
Sentiment Analysis
Topic Modeling Natural Machine
Summarization Language Translation,
…
Generation OCR, etc.
SAS Visual Text Analytics Natural Language
(VTA) 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)
#AISAIS17
9 . Sample NLU/NLG Use Cases
Voice
activated/ Automated
Call Steering
enabled Image or Text
and IVR
devices and Captioning
apps
Automated Machine
reports, Learning
Smart Search
summaries, Model
or narratives Explanability
#AISAIS17
10 . Today’s Focus: Chatbot Use Case
Voice activated/ Automated
Call Steering and
enabled devices Image or Text
IVR
and apps Captioning
•Customer service bot
•Order fulfillment
•Q & A systems
Chatbots •Therapy bot
(“Woebot”)
• Law bot
Automated
• Health bot Machine
reports,
Smart Search Learning Model
summaries, or
Explanability
narratives
#AISAIS17
12 . Conceptual Architecture
If Speech-to-Text
NATURAL
and Text-to-Speech
LANGUAGE
are relevant, that is
UNDERSTANDING
handled by the NLU returns
application layer Submit retained predicted intent
+ new info to and
NLU parameters/
slots Submit inputs
User input Enough to execution
info to YES engine
fulfill
intent?
Response
BOT LOGIC NO ACTIONS
USER APPLICATION Incomplete intent:
prompt for more
info
Submit outputs
to speech
NATURAL LANGUAGE template
GENERATION
#AISAIS17 #AISAIS17
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