Chatbots have entered our lives unknowingly. Little do we realize that when that lil window pops up asking if we need support or help- it could just be a chatbot that we are talking to...

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Chatbots
Automated Conversational Model using
Machine Learning
Microsoft Silver
Partner for Analytics

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CEO and company Profile
Aniruddha Pant
CEO and Founder of AlgoAnalytics
PhD, Control systems, University of
California at Berkeley, USA 2001
•20+ years in application of advanced mathematical techniques
to academic and enterprise problems.
•Experience in application of machine learning to various
business problems.
•Experience in financial markets trading; Indian as well as global
markets.
Highlights
•Experience in cross-domain application of basic scientific
process.
•Research in areas ranging from biology to financial markets to
military applications.
•Close collaboration with premier educational institutes in India,
USA & Europe.
•Active involvement in startup ecosystem in India.
Expertise
•Vice President, Capital Metrics and Risk Solutions
•Head of Analytics Competency Center, Persistent Systems
•Scientist and Group Leader, Tata Consultancy Services
Prior Experience
• Work at the intersection of mathematics and other
domains
• Harness data to provide insight and solutions to our
clients
Analytics Consultancy
• +30 data scientists with experience in mathematics
and engineering
• Team strengths include ability to deal with
structured/ unstructured data, classical ML as well as
deep learning using cutting edge methodologies
Led by Aniruddha Pant
• Develop advanced mathematical models or solutions
for a wide range of industries:
• Financial services, Retail, economics, healthcare,
BFSI, telecom, …
Expertise in Mathematics and Computer
Science
• Work closely with domain experts – either from the
clients side or our own – to effectively model the
problem to be solved
Working with Domain Specialists
About AlgoAnalytics

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AlgoAnalytics - One Stop AI Shop
Healthcare
•Medical Image diagnostics
•Work flow optimization
•Cash flow forecasting
Financial Services
•Dormancy prediction
•Recommender system
•RM risk analysis
•News summarization
Retail
•Churn analysis
•RecSys
•Image recognition
•Generating image description
Others
•Algorithmic trading strategies
•Risk sensing – network theory
•Network failure model
•Clickstream analytics
•News/ social media analytics
Aniruddha Pant
CEO and Founder of AlgoAnalytics
• Structured data is utilized to
design our predictive analytics
solutions like churn,
recommender sys
• We use techniques like
clustering, Recurrent Neural
Networks,
Structured
data
• We use text data analytics for
designing solutions like sentiment
analysis, news summarization
and much more
• We use techniques like Natural
Language Processing (NLP),
word2vec, deep learning, TF-IDF
Text data
• Image data is used for predicting
existence of particular pathology,
image recognition and many
others
• We employ techniques like deep
learning – convolutional neural
network (CNN), artificial neural
networks (ANN) and technologies
like TensorFlow
Image
data
• We apply sound data to design
factory solutions like air leakage
detection, identification of empty
and loaded strokes from press
data, engine-compressor fault
detection
• We use techniques like deep
learning
Sound
Data

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Technologies

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What are Chatbots – how do they work?
Closed domain bots are most suited for specific tasks.
• Rule-Based chatbots are easiest to program; need
to be prepared for all scenarios in advance.
• If it’s purely based on Retrieval, it’ll need a
sufficiently large corpus to handle all variety of
queries.
• Generative-based bots are smarter, but harder to
develop; need lots of data to train.
• Provides an interface that is more natural and
familiar.
• Lets a program handle repetitive tasks with well set
pattern (eg., recharge my mobile phone, log a service
request, etc.)
• Can be used to gather information before a human
can take over the conversation, say for customer
support.
• Can work through different channels like SMS,
WebChat, Skype (with and w/o voice).
Kinds of ChatbotsWhy Chatbots –
some key reasons/use cases:

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Why Chatbots?
With Chats:
• Instant delivery to clients. Allows more freedom to start a conversation whenever and wherever they need
to, for as long as they need to
• Automatically Profiles your customers for remarketing in the future
• Streamlines logistics right from management of orders to after sales & complaints
• Minimizes Human Management
• Eliminates the use/need for customer service representatives
• More economical in the long run
Customers get problems fixed with ease
and speed
A Solution to the Service side
companies inundated with
customer requests
When it serves as a communicator:

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Rules based, supported by LUIS (MS Language Framework)
• Predefined flow – good for automation of repetitive tasks.
• Microsoft’s LUIS - Language understanding API (or custom
“intelligence”) used to understand “intent” of the user’s message
text.
• Entities are separated out and used in designing response.
• Possible to integrate multiple channels through Microsoft’s
botframework.
• Can be hosted on Azure (to scale automatically).
Sample Conversation
Architecture

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Retrieval based and Generative approaches
Generative Based
• It produces (creates) responses based on past history
of responses.
• Would need a large dataset to train the model.
Source
Sentence
Encoder
Retrieval Based
• Searches through query/response database to find
most appropriate response.
• A large dataset would be able to produce different
responses for variations in queries.
• Cornell Movie Dialogs Dataset: Large metadata rich
dataset from raw movie dialogs. Contains over
100,000 conversations.
• OpenSubtitles: Movie conversations in XML format.
Datasets
• Scotus: Supreme court conversation data. It contains
more than ~52K dialogs.
• Ubuntu Dialogue Corpus: Containing almost 1 million
multi-turn dialogues.
Context
Predicted
Response
Response
Decoder
Generatd
Answer

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Demo – Rule Based
▪ Approach : Rule Based without the help of Language Understanding. ▪ Approach : Rule Based with the help of Microsoft’s LUIS (Language
Understanding Framework).
The standard chatbot will ask many questions before
ascertaining the problem
But with Microsoft’s LUIS, it goes to the core of the
problem and processes the details in the background

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Summary
▪ Chatbots provide a natural interface for automating simpler tasks.
▪ Rule based bots are easier to program, but all scenarios need to be thought of in
advance.
▪ Retrieval based and Generative bots are harder and require more data and larger
machines to train.
▪ Microsoft has an array of tools that can be used to program and deliver bots. From
the botframework, to LUIS (Language Understanding API), and CNTK for general
purpose deep learning.
▪ Azure can be used for deployment so the bot can scale well.

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Interested in knowing more:
Contact us: info@algoanalytics.com