AI & Chatbots: Improving Capabilities with Learning, Reasoning & Planning

A Match Made in Heaven:
AI & Chatbots
Improving the capabilities of Chatbots with
learning, reasoning, understanding and planning
Ted Washburne
New York, September 25th
#CWIN17

Session’s Title | Date
Copyright © 2017 Capgemini and Sogeti. All rights reserved. 2
Table of Contents
 What’s the latest with Chatbots and AI?
 How to leverage/collaborate with data science
teams for Chatbot development
 Measuring Value

What’s the latest with Chatbots and AI?

Recent Developments
 Researchers at Tsinghua University and University of Illinois at Chicago have developed a
deep learning-based chatbot capable of assessing the emotional content of a conversation
and responding accordingly*
• Tools:Seq2Seq, TensorFlow, cuDNN, Stochastic Gradient Descent, Nvidia Titan X GPUs
 Other new generation chatbots are socially aware, like Sara, from ArticuLab at CMU
• Sara is capable of detecting social behaviors in conversation, reasoning about how to
respond to the intentions behind those particular behaviors, and generating appropriate
social responses – as well as carrying out her task duties at the same time.
Exponential improvements in
Cognitive AI in the past decade
Amazing growth in
computing power
Availability of building
and training tools
*Emotional Chatting Machine: Emotional
Conversation Generation with Internal and
External Memory
“By the way, out of interest and no
particular reason, are you Sara Connor?”

Chatbots technology today is combining elements from AI, NLP and even computer vision
Chatbots are not necessary “intelligent”, and a major challenge is to interpret user “Intent” and the goal of the “conversation”
Chatbots have become popular as the
“messaging” platform trend is increasing as a
form of conversation and interactions
The chatbot can be structured in its conversation
with a “set of choices”, or it can be set up to
handle a wider “range of inputs”
Since the chatbot needs to understand the intent
of the user the more “free-form” the conversation
the more options and domains must be
understood by the robot
Virtual assistants are placing themselves “closer”
to the user, and being part of the earliest part of
the interaction. This means handling a diverse
set of needs and interactions before the user is
“channeled” into the correct context
Recall of earlier conversations provide context
for the latest customer interactions
Ameila
Source: Forrester

Chatbots can interface with customers and employees in a variety of channels
and at different stages of the lifecycle
“Home / Mobile” “Front-office” “Back-office”
 Request information
 Get assistance
 Identify product to solve need
 Request product details
 Match product to need
 Get service requests resolved
 Processing transactions that
reach the back-office
 Virtual assistants try to attempt
to interpret your need
 Once need is determined -
guides you into the correct
context
 Can initiate separate
commerce-platform (web-
pages) or transact directly “in-
conversation-purchase”
 Chatbots interact with the
customer
 Interpret context for the
customers request and helps to
provide information
 Provide customer service to
standard service requests
 Learn from experience
 Manual employees “take over”
for other requests
 Transactional automation of
standardized and repetitive
back-office tasks
 Defined business rules,
structured information, working
on top of existing applications
 Free up employee time, so that
it can better spent on
customers directly
Attract
Direct user based on need
Acquire, Use, Care
Resolve user need
Merging the
boundaries into
“One-Office”
Divest
Automation “behind-the-scenes

How to leverage/collaborate with data science teams
for Chatbot development

Building Intelligent Chatbots – we can demonstrate higher cognitive capabilities
thanks to mastery of NLU, reasoning, NLG, and deep learning methods
Customer Talking or
Texting
Language / Speech
Recognition
Converted to Text
Natural Language
Understanding
Problem Solving vs.
Canned Responses
Planning
Natural Language
Generation vs.
Canned Response
Speech Synthesis
Reinforcement
Learning of
Satisfactory vs.
Unsatisfactory
Response
Feedback
Natural
Language
Understanding
(NLU) is an
especially
complex
challenge of
Chatbots
Integration of 3rd
party cognitive
services/ pre-
trained machine
learning models
via APIs
Integrating
custom
analytical
models,
algorithms, and
ML code into
the Chatbot for
adding
intelligence
Reasoning and
response
generation may
be needed,
depending on
how intricate
you want the
Chatbot's
responses to be
Application of
self learning
capabilities
using
Reinforcement
learning
methods
Apply NLP,
machine
learning and
data mining
techniques to
analyze Chatbot
conversations

Capgemini’s Chatbot Maturity Model
Interaction
Intelligence
API queries
Integration
Self
learning
Multi
person
Case
Process
interaction
API transactionsSimple Q&A
One Language
One Channel
Multi
channel
Multi
language
Line based
intelligence
Fixed rules
Training of
NLP model
Chatbot initiates
conversation
Chatbot-to-Chatbot
interaction
Menu based
Conversation
intelligence
B2B
Conversation
listeningHuman to chatbot
interaction
Event
producing
API intelligent
queries
Mood
detection
Level 1 Level 2 Level 3
State
machine
Human
Handoff
Links for more
information
Historic
analysis
GEO
Conversational
Intelligence
Interaction
is the area where the end-user experiences
the chatbot functionality. The user
experience in a chatbot is aimed at
facilitating a conversation.
Intelligence
deals with all the capabilities where the
conversation is supported by means of
intelligence. The capability to understand a
sentence and provide an answer most likely
to align with the intent of the end-user is
what would be the most accurate definition of
intelligence.
Integration
In order to provide an answer; often the
content of the answer needs to be enriched
with information from a back-end system.
When wanting to know the status of that on-
line order via a chatbot, the chatbot should
be able to connect with a back-end system to
be able to fetch information about that
particular order.

Example tasks required to move to each stage
Level 1: Fully
Guided
Conversation
• Chatbot guides
user to
completion of
task by giving
options to user in
each
conversations,
enabling faster
execution.
Useful for
complex &
lengthy tasks.
Level 2: Partially
Guided
Conversation
• Chatbot uses
combination of
pre-configured
options and free
flow natural
language
conversations to
interact with user
for problem
solving
• Canned
Response vs.
Response
Reasoning
Level 3: Free Flow
Natural Language
Conversation
• Chatbot interacts
with user using
natural language
conversations if it
is not able to
understand
users’ intent or
the user needs
help in
completing their
task

Pragmatic use cases of Chatbots and AI empower and automate your business processes
You can package selected components of the technology to imitate specific human “abilities”
9. Image
analysis
8. Sarcasm
Recognition
(eye roll, smirk)
5. Context
Understanding
4. Natural
language
processing
1. Robotic
Process
Automation
6. Response
Planning
5. Machine
learning
6. Intelligent
Assistant
2. Sensory
perception
9. Image
analysis
 In order to get started with Robotics and AI
today, it is useful to build with smaller and
more “narrow” use-cases
 To help with this, we can group the
technology capabilities in order to provide a
given “ability
 You must have a clear business case and
business objective for what you want to
achieve, before you start determining the
requirements of your solution
 Tailor the specific AI solution to support a
defined business processes with a needed
“ability”
 Be prepared to be able to “experiment” and
adjust quickly as you gain experience with
the use-cases
 The technology is developing quickly, so be
aware that the “building blocks” are
constantly changing – and becoming more
advanced
 You need to determine how to integrate the
solution into the business process – as a
“stand-alone” solution, or as a module, or as
a combined orchestration
Packaging AI components to deliver
pragmatic solutions
7. Natural
language
generation
7. Natural
language
understanding
Expense
Reporting
Assistant

Ready Out-Of-The-Box Chatbots

Measuring Value

Example: Call Center Chatbots are becoming fully integrated and have their
performance tracked as products, services and regulations evolve
Survey CRM HRACD CTI IVR VOIP TDMA
Recorded CallsCall Session Data Structured Data
Email
Chat
SMS
Text Interactions
Social
Speech Analytics Product Overview
Sales
Improvement
First Call
Resolution
Call Volume
Reduction
Customer
Satisfaction
Collections
Optimization
Handle Time
Optimization
Compliance
Management
Customer
Retention
Connectors
Conversation
Data
Conversation
Analytics
Platform
Web Applications
Personalized
Dashboards
Reporting
Analytics
Tools
Search
Quality
Management
Coaching
Speech Analytics Text Analytics

Chatbot Analytics
Mobile Brand
Tracker
 Conversation volumes
 Sentiment Analysis
 Campaign HashtagTracking Alerting
 Sales and Consumer conversation integration
Real Time
Engagement
 Event Site Command Center
 Media Monitoring
 AI for Real Time Content Creation and Targeting
 Customer Journey Analytics
Brand Equity
Tracking
 Organic conversation attribution
 Advanced sentiment analysis
 Trending, competitor analysis
 In-flight campaign impact analysis

Thank You!
Phone: +1 914 707 3700
Ted.Washburne@capgemini.com
Ted WASHBURNE
Director
Chief Data Scientist

AI & Chatbots: Improving Capabilities with Learning, Reasoning & Planning

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