Conversational AI

Name: Conversational AI
Brand: Manning
Price: 49.44 EUR
Availability: OnlineOnly

Andrew Freed(Author)

Manning (Publisher)

1st Edition

Published on 2. November 2021

320 pages

E-Book

ePUB with Adobe-DRM

System requirements

978-1-63835-100-9 (ISBN)

€49.44incl. 7% vat

System requirements

for ePUB with Adobe-DRM

E-Book Single Licence

Available for download

Description

Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.

Alles über E-Books, Kopierschutz & Dateiformate finden Sie in unserem Info- & Hilfebereich.

"A thorough guide to the entire process of designing and implementing virtual assistants. Goes way beyond the technicalities." - Maxim Volgin, KLM

Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services.

In Conversational AI, you will learn how to:

Pick the right AI assistant type and channel for your needs
Write dialog with intentional tone and specificity
Train your AI's classifier from the ground up
Create question-and-direct-response AI assistants
Design and optimize a process flow for web and voice
Test your assistant's accuracy and plan out improvements

Conversational AI: Chatbots that work teaches you to create the kind of AI-enabled assistants that are revolutionizing the customer service industry. You'll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. This engaging and entertaining book delivers the essential technical and creative skills for designing successful AI solutions, from coding process flows and training machine learning, to improving your written dialog.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the technology
Create AI-driven chatbots and other intelligent agents that humans actually enjoy talking to! Adding intelligence to automated response systems saves time and money for you and your customers. Conversational AI systems excel at routine tasks such as answering common questions, classifying issues, and routing customers to the appropriate human staff. This book will show you how to build effective, production-ready AI assistants.

About the book
Conversational AI is a guide to creating AI-driven voice and text agents for customer support and other conversational tasks. This practical and entertaining book combines design theory with techniques for building and training AI systems. In it, you'll learn how to find training data, assess performance, and write dialog that sounds human. You'll go from building simple chatbots to designing the voice assistant for a complete call center.

What's inside

Pick the right AI for your needs
Train your AI classifier
Create question-and-direct-response assistants
Design and optimize a process flow

About the reader
For software developers. Examples use Watson Assistant and Python.

About the author
Andrew R. Freed is a Master Inventor and Senior Technical Staff Member at IBM. He has worked in AI solutions since 2012.

Table of Contents
PART 1 FOUNDATIONS
1 Introduction to conversational AI
2 Building your first conversational AI
PART 2 DESIGNING FOR SUCCESS
3 Designing effective processes
4 Designing effective dialogue
5 Building a successful AI assistant
PART 3 TRAINING AND TESTING
6 Training your assistant
7 How accurate is your assistant?
8 Testing your dialogue flows
PART 4 MAINTENANCE
9 Deployment and management
10 Improving your assistant
PART 5 ADVANCED/OPTIONAL TOPICS
11 Building your own classifier
12 Additional training for voice assistants

More details

Other editions

Person

Content

Intro
inside front cover
Conversational AI
Copyright
dedication
brief contents
contents
front matter
preface
acknowledgments
about this book
Who should read this book
How this book is organized: A roadmap
About the code
liveBook discussion forum
Other online resources
about the author
about the cover illustration
Part 1. Foundations
1 Introduction to conversational AI
1.1 Introduction to AI assistants and their platforms
1.1.1 Types of AI assistants
1.1.2 A snapshot of AI assistant platforms
1.2 Primary use cases for AI assistant technology
1.2.1 Self-service assistant
1.2.2 Agent assist
1.2.3 Classification and routing
1.3 Follow along with this book
1.3.1 What you need to create your assistant
1.3.2 Useful spreadsheet software
1.3.3 Recommended programming language and code repository
Summary
2 Building your first conversational AI
2.1 Building a conversational AI for Fictitious Inc.
2.2 What's the user's intent?
2.2.1 What's an utterance?
2.2.2 What's a response?
2.2.3 How does the assistant understand what the user means?
2.2.4 Why machine learning?
2.2.5 What's an intent?
2.2.6 What's an entity?
2.2.7 Combining intents and entities
2.2.8 intents to the Fictitious Inc. assistant
2.3 Responding to the user
2.3.1 Simple question-and-answer responses
2.3.2 Contextualizing a response by using entities
2.3.3 An alternative way to provide contextual responses
2.3.4 Responding with a process flow
2.4 Other useful responses
2.4.1 Detecting low confidence
2.4.2 Counting misunderstandings
2.4.3 Implementing confidence detection and the two-strikes rule
2.5 Try to build this conversational AI yourself
Summary
Part 2. Designing for success
3 Designing effective processes
3.1 What processes will the assistant handle?
3.1.1 Designing for the most common user needs
3.1.2 Assembling a plan and a dream team
3.1.3 Managing the design process
3.1.4 Cross-cutting design aspects
3.2 Choosing the channel to implement first
3.2.1 How users receive information in voice and web
3.2.2 How the assistant receives information in voice and web
Summary
4 Designing effective dialogue
4.1 Writing dialogue
4.1.1 Take a conversational tone
4.1.2 Don't repeat yourself (much)
4.1.3 Acknowledge the user
4.2 Asking questions
4.3 What if the assistant doesn't understand?
4.3.1 Reprompting
4.3.2 Disambiguation
4.3.3 Escalation
Summary
5 Building a successful AI assistant
5.1 AI assistant use cases
5.2 Conversational AI success metrics
5.2.1 Containment
5.2.2 Time to resolution
5.2.3 Net promoter score
5.2.4 Coverage
5.2.5 Instrumenting your conversational AI
5.3 Command interpreter success metrics
5.3.1 Usage
5.3.2 Stickiness
5.3.3 Instrumenting your command interpreter
5.4 Event classifier success metrics
5.4.1 Time to resolution
5.4.2 Number of hand-offs
5.4.3 Other customer satisfaction metrics
5.4.4 Instrumenting your classifier
Summary
Part 3. Training and testing
6 Training your assistant
6.1 Training an AI assistant
6.2 Finding training data
6.2.1 Production logs
6.2.2 A mock user interface
6.2.3 Subject-matter experts
6.2.4 Organizing training data into intents
6.3 Assessing whether you have the right training data
6.3.1 Training data variety
6.3.2 Training data volume
6.3.3 Training data veracity
Summary
7 How accurate is your assistant?
7.1 Testing an AI assistant for accuracy
7.1.1 Testing a single utterance
7.1.2 Testing multiple utterances
7.1.3 Selecting a test data set
7.2 Comparing testing methodologies
7.2.1 Blind testing
7.2.2 k-folds cross-validation test
7.3 Selecting the right accuracy metric for the job
Summary
8 Testing your dialogue flows
8.1 Functionally testing a dialogue flow
8.1.1 Manually testing a conversation flow
8.1.2 Automating a conversation flow test
8.1.3 Testing the dialogue flowchart
8.1.4 Testing the unexpected error paths
8.2 Nonfunctionally testing a dialogue flow
8.2.1 User experience testing
8.2.2 Load testing
Summary
Part 4. Maintenance
9 Deployment and management
9.1 Where to store your code
9.1.1 Taking the Wild West approach
9.1.2 Using source control for code
9.2 Where to run your code
9.2.1 Development environment
9.2.2 Test environment
9.2.3 Production environment
9.2.4 After the first production deployment
9.3 Using source control for other assets
Summary
10 Improving your assistant
10.1 Using a success metric to determine where to start improvements
10.1.1 Improving the first flow to fix containment problems
10.1.2 Inspecting other process flows for containment problems
10.2 Analyzing the classifier to predict future containment problems
10.2.1 Representative baseline
10.2.2 Finding gaps in the training data
10.3 When and why to improve your assistant
10.3.1 You can't fix everything at once
10.3.2 You can't always predict how users will react
10.3.3 User needs will change
10.3.4 Not every problem is technical
Summary
Part 5. Advanced/optional topics
11 Building your own classifier
11.1 Why build your own classifier?
11.1.1 Classification is a differentiator
11.1.2 Classification is a core competency
11.1.3 Traceability
11.1.4 To learn
11.1.5 Build or buy?
11.2 Build a simple classifier from first principles
11.2.1 The simplest text classifier
11.2.2 The mathematics behind a simple classifier
11.3 Expanding the simple classifier
11.3.1 Predicting more than one class
11.3.2 An all-in-one classifier
11.3.3 Comparing binary classifiers to all-in-one classifiers
11.4 Extending even further
11.4.1 What happens when you add more training data?
11.4.2 Exercise: Experiment on your own
Summary
12 Additional training for voice assistants
12.1 Collecting data to test a speech-to-text model
12.1.1 Call recordings as speech training data
12.1.2 Generating synthetic speech data
12.2 Testing the speech-to-text model
12.2.1 Word error rate
12.2.2 Intent error rate
12.2.3 Sentence error rate
12.3 Training a speech-to-text model
12.3.1 Custom training with a language model
12.3.2 Custom training with an acoustic model
12.3.3 Custom training with grammars
Summary
Appendix. Glossary of terms and abbreviations
index
inside back cover

System requirements

Save as PDF Copy link into clipboard

Schweitzer Fachinformationen

Conversational AI

Description

More details

Other editions

Additional editions

Person

Content

System requirements