Come see how to create a chatbot with Amazon Lex. Learn how to set a goal, define your first few intents for the chatbot and then build an end-to-end serverless architecture to handle your common questions across multiple entry channels – from web to interative voice response (IVR). Learn how to train a chatbot and create AWS Lambda functions to find the answers and fulfill the user’s questions with hands-on code demonstrations. Speaker: Kurt Heiz, Solutions Architect, AWS

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C anb er r a, AC T

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Let a ChatBot do the Heavy Lifting
of the Simple Stuff
Kurt Heiz
Senior Solutions Architect
ANZ Public Sector, AWS

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Agenda
What is a ChatBot?
Amazon Lex
Demo
Sample use cases and architecture
Code

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ChatBot definition
Wikipedia defines a ChatBot as:
“A chatbot is a computer program which conducts a conversation via
auditory or textual methods. Such programs are often designed to
convincingly simulate how a human would behave as a conversational
partner, although as of 2019, they are far short of being able to pass the
Turing test”
“A Turing test is a method of inquiry in artificial intelligence (AI) for
determining whether or not a computer is capable of thinking like a
human being.” Developed by Alan Turing in 1950, who cracked the Nazi
Enigma code

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Amazon Lex
• Service to build conversational interfaces
• Integrates tightly with other AWS services
• Fulfilment using AWS Lambda at two stages
• Authentication with IAM or Amazon Cognito
• Built in channel support for
• Kik
• Slack
• Twilio
• Facebook
• Amazon Connect integration

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Structure of a ChatBot example

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Sample use case (anonymous user)
• Support desk: internal or external
• ‘Low hanging fruit’ questions
• How do I reset my password for [system]?
• What are the term dates?
• Where can I find [something] ?

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Sample use case (authenticated user)
• Support desk: internal or external
• ‘Low hanging fruit’ questions
• Book an appointment with [person] for [date] at [time]
• How many days [leaveType] do I have?
• Spin up a new [environmentType] for [solutionName]

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Our sample use case scenario
• External support desk (anonymous user)
• Business problem
• Customers are currently waiting up to one hour to have simple questions answered
• Estimates that offloading the simple questions could save call wait times by up to 66%
• Channels in use:
• Telephone
• Facebook Messenger
• Proposal
• Create a ChatBot to answer simple questions as defined by the business, using existing call
logs to define those questions
• Introduce a new interface, web, and integrate with FB
• Leave telephone integration to future phase once ChatBot has proven itself in two to three
months
• Where do we start?

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Where do we start?
• Define a goal
• “Assist new students with basic tasks and finding their way around campus”
• Define intents from top simple requests, aim for between 5-10
• Action or Assistance based – be sure to make use of slots
• Sample utterances for training
• Where can answers and responses be found?
• Technical architecture
• Operational support
• Continuous training and updating of ChatBot
• Set expectations for users upfront about limits of ChatBot capability

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Test – train – test (ensure it understands correctly)
• Create an automated
testing regime
• Ensure expected intent is
matched and correct answer
is given
• Create list of non-trained
utterances with expected
positive and negative
matches
• Learn about the confusion
matrix

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Confusion matrix
• A confusion matrix is a
table that is often used to
describe the performance
of a classification model
(or "classifier") on a set of
test data for which the
true values are known.

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Technical serverless architecture option
AWS Cloud
Customer
desktop
client
Amazon API Gateway
AWS Lambda Amazon Lex AWS Lambda
AWS Lambda Amazon DynamoDB
Amazon Elasticsearch
Service
Customer
mobile
client
Amazon Kinesis
Data Firehose
Support
desktop
client
Amazon Cognito

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What more can the interceptor do for you?
AWS Cloud
Desktop
client
Amazon API Gateway
AWS Lambda Amazon Lex AWS Lambda
AWS Lambda Amazon DynamoDB
Amazon Elasticsearch
Service
Mobile
client
Amazon Kinesis
Data Firehose
Desktop
client
Amazon Cognito
Amazon Translate
Amazon Comprehend
Third Party web chat systems
e.g. LiveChat

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DynamoDB sample table structure
• Table can be generic to handle any intent and slot values