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Chatbots: Automated Conversational Model using Machine Learning

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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

  1. 1. Page 1 Chatbots Automated Conversational Model using Machine Learning Microsoft Silver Partner for Analytics
  2. 2. Page 2 © AlgoAnalytics All rights reserved 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
  3. 3. Page 3 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
  4. 4. Page 4 © AlgoAnalytics All rights reserved Technologies
  5. 5. Page 5 © AlgoAnalytics All rights reserved 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:
  6. 6. Page 6 © AlgoAnalytics All rights reserved 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:
  7. 7. Page 7 © AlgoAnalytics All rights reserved 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
  8. 8. Page 8 © AlgoAnalytics All rights reserved 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
  9. 9. Page 9 © AlgoAnalytics All rights reserved 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
  10. 10. Page 10 © AlgoAnalytics All rights reserved 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.
  11. 11. Page 11 Interested in knowing more: Contact us: info@algoanalytics.com

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