Conversational AI: chatbots that work
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Shelter Island, NY
Manning Publications Co.
[2021]
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxiv, 291 Seiten Illistrationen, Diagramme 24 cm |
| ISBN: | 9781617298837 1617298832 |
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Record in the Search Index
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| adam_text | brief contents Part 1 Foundations......................................................................... 1 1 ■ Introduction to conversational AI 3 2 ■ Building your first conversational AI 22 Part 2 Designing for success....................................................... 53 3 ■ Designing effective processes 55 4 ■ Designing effective dialogue 75 5 ■ Building a successful AI assistant 92 Part 3 Training and testing............................. 6 ■ Training your assistant 115 117 7 ■ How accurate is your assistant? 139 8 ■ Testing your dialogue flows 164 Part 4 Maintenance.................................................................... 185 9 ■ Deployment and management 10 ■ Improving your assistant 203 vii 187
viii Part 5 BRIEF CONTENTS Advanced/optional topics.......................................... 229 11 ■ Building your own classifier 231 12 ■ Additional training for voice assistants 252
contents preface xv acknowledgments xvii about this book xix about the author xxiii about the cover illustration Part xxiv Foundations............ 1 ƒ ՜^՜ Introduction to conversational AI 1.1 4 5 · A snapshot ofAI assistant Primary use cases for AI assistant technology 17 ■ Agent assist Self-service assistant routing 18 1.3 3 Introduction to AI assistants and their platforms Types ofAI assistants platforms 15 1.2 1 Follow along with this book 17 17 ■ Classification and 19 What you need to create your assistant 19· Useful spreadsheet software 20 ■ Recommended programming language and code repository 20 ix
CONTENTS Building yourfirst conversational AI 22 2.1 Building a conversational AI for Fictitious Inc. 2.2 What’s the user’s intent? 23 26 What’s an utterance ? 27 ■ What’s a response? 27 ■ How does the assistant understand what the user means? 28 ■ Why machine learning? 29· What’s an intent? 29· What’s an entity? 30 Combining intents and entities 31 ■ Adding intents to the Fictitious Inc. assistant 32 2.3 Responding to the user 34 Simple question-and-answer responses 36 ■ Contextualizing a response by using entities 38 · An alternative way to provide contextual responses 41 · Responding with a process flow 42 2.4 Other useful responses 46 Detecting low confidence 46 ■ Counting misunderstandings 48 Implementing confidence detection and the two-strikes rule 48 2.5 Try to build this conversational AI yourself Part 2 Designing for success 53 ········· Designing effective processes 3.1 51 55 What processes will the assistant handle? 57 Designingfor the most common user needs 57 ■ Assembling a plan and a dream team 60 · Managing the design process 63 Cross-cutting design aspects 66 3.2 Choosing the channel to implement first 68 How users receive information in voice and web 69 ■ How the assistant receives information in voice and web 71 Designing effective dialogue 4.1 Writing dialogue 75 77 Take a conversational tone Acknowledge the user 80 77 · Don’t repeat yourself (much) 4.2 Asking questions 4.3 What if the assistant doesn’t understand? Reprompting 83 85 ■ Disambiguation Building a succesfful AI assistant 5.1 AI assistant use cases 93 92 85 87 ■ Escalation 89 79
ХІ CONTENTS 5.2 Conversational AI success metrics 94 Containment 95 · Time to resolution 98 ■ Net promoter score 100 · Coverage 101 ■ Instrumentingyour conversational AI 104 5.3 Command interpreter success metrics 107 ■ Instrumenting your command Usage 106 ■ Stickiness interpreter 108 5.4 105 Event classifier success metrics 109 Time to resolution 110 ■ Number of hand-offs 112 ■ Other customer satisfaction metrics 113· Instrumenting your classifier 113 Part 3 Training and testing............................................115 Training your assistant 117 6.1 Training an AI assistant 6.2 Finding training data 118 120 Production logs 121 ■ A mock user interface 125 Subject-matter experts 127· Organizing training data into intents 128 6.3 Assessing whether you have the right training data Training data variety 131 · Training data volume Training data veracity 135 ^7 How accurate is your assistant? 7.1 130 133 139 Testing an AI assistant for accuracy 140 Testinga single utterance 141 · Testing multiple utterances 143· Selecting a test data set 145 7.2 Comparing testing methodologies Blind testing 7.3 153 Selecting the right accuracy metric for the job Testing your dialogue flows 8.1 148 149 · kfolds cross-validation test 159 164 Functionally testing a dialogue flow 165 Manually testinga conversation flow 166 · Automatinga conversation flow test 168 ■ Testing the dialogue flowchart Testing the unexpected error paths 174 8.2 Nonfunctionally testing a dialogue flow User experience testing 179 · Load testing 179 181 172
xii CONTENTS Part 4 Maintenance.......................................................... 185 Q Deployment and management 187 Where to store your code 188 9.1 Taking the Wild West approach code 189 9.2 Where to run your code 188 · Using source controlfor 194 Development environment 194 · Test environment 195 Production environment 196 · After the first production deployment 198 9.3 Using source control for other assets J f i Improving your assistant x 10.1 201 203 Using a success metric to determine where to start improvements 204 Improving the first flow to fix containment problems 205 Inspecting other process flows for containment problems 212 10.2 Analyzing the classifier to predict future containment problems 215 Representative baseline data 219 10.3 215 · Finding gaps in the training When and why to improve your assistant 223 You can’t fix everything at once 223 ■ You can’t always predict how users will react 224 ■ User needs will change 225 Not every problem is technical 226 Part 5 Advanced/optional topics.................................. 229 ƒ ƒ Л. Љ Building your own classifier ļļ i 231 Why build your own classifier? 233 Classification is a differentiator 233 ■ Classification is a core competency 234 · Traceability 234 ■ To learn 234 ■ Build or buy ? 235 11.2 Build a simple classifier from first principles The simplest text classifier classifier 238 235 236 ■ The mathematics behind a simple
xiii CONTENTS 11.3 Expanding the simple classifier 242 Predicting more than one class 243 · An all-in-one classifier 247 · Comparing binary classifiers to all-in-one classifiers 248 11.4 Extending even further 249 What happens when you add more training datai Experiment on your own 251 249 ■ Exercise: Դ Additional trainingfor voice assistants 252 12.1 Collecting data to test a speech-to-text model Call recordings as speech training data speech data 258 12.2 Testing the speech-to-text model Word error rate rate 263 12.3 255 · Generating synthetic 259 261 ■ Intent error rate Training a speech-to-text model 254 262 ■ Sentence error 265 Custom training with a language model 267 ■ Custom training with an acoustic model 269 · Custom training with grammars 272 appendix Glossary of terms and abbreviations index 285 277
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| adam_txt |
brief contents Part 1 Foundations. 1 1 ■ Introduction to conversational AI 3 2 ■ Building your first conversational AI 22 Part 2 Designing for success. 53 3 ■ Designing effective processes 55 4 ■ Designing effective dialogue 75 5 ■ Building a successful AI assistant 92 Part 3 Training and testing. 6 ■ Training your assistant 115 117 7 ■ How accurate is your assistant? 139 8 ■ Testing your dialogue flows 164 Part 4 Maintenance. 185 9 ■ Deployment and management 10 ■ Improving your assistant 203 vii 187
viii Part 5 BRIEF CONTENTS Advanced/optional topics. 229 11 ■ Building your own classifier 231 12 ■ Additional training for voice assistants 252
contents preface xv acknowledgments xvii about this book xix about the author xxiii about the cover illustration Part xxiv Foundations. 1 ƒ ՜^՜ Introduction to conversational AI 1.1 4 5 · A snapshot ofAI assistant Primary use cases for AI assistant technology 17 ■ Agent assist Self-service assistant routing 18 1.3 3 Introduction to AI assistants and their platforms Types ofAI assistants platforms 15 1.2 1 Follow along with this book 17 17 ■ Classification and 19 What you need to create your assistant 19· Useful spreadsheet software 20 ■ Recommended programming language and code repository 20 ix
CONTENTS Building yourfirst conversational AI 22 2.1 Building a conversational AI for Fictitious Inc. 2.2 What’s the user’s intent? 23 26 What’s an utterance'? 27 ■ What’s a response? 27 ■ How does the assistant understand what the user means? 28 ■ Why machine learning? 29· What’s an intent? 29· What’s an entity? 30 Combining intents and entities 31 ■ Adding intents to the Fictitious Inc. assistant 32 2.3 Responding to the user 34 Simple question-and-answer responses 36 ■ Contextualizing a response by using entities 38 · An alternative way to provide contextual responses 41 · Responding with a process flow 42 2.4 Other useful responses 46 Detecting low confidence 46 ■ Counting misunderstandings 48 Implementing confidence detection and the two-strikes rule 48 2.5 Try to build this conversational AI yourself Part 2 Designing for success 53 ········· Designing effective processes 3.1 51 55 What processes will the assistant handle? 57 Designingfor the most common user needs 57 ■ Assembling a plan and a dream team 60 · Managing the design process 63 Cross-cutting design aspects 66 3.2 Choosing the channel to implement first 68 How users receive information in voice and web 69 ■ How the assistant receives information in voice and web 71 Designing effective dialogue 4.1 Writing dialogue 75 77 Take a conversational tone Acknowledge the user 80 77 · Don’t repeat yourself (much) 4.2 Asking questions 4.3 What if the assistant doesn’t understand? Reprompting 83 85 ■ Disambiguation Building a succesfful AI assistant 5.1 AI assistant use cases 93 92 85 87 ■ Escalation 89 79
ХІ CONTENTS 5.2 Conversational AI success metrics 94 Containment 95 · Time to resolution 98 ■ Net promoter score 100 · Coverage 101 ■ Instrumentingyour conversational AI 104 5.3 Command interpreter success metrics 107 ■ Instrumenting your command Usage 106 ■ Stickiness interpreter 108 5.4 105 Event classifier success metrics 109 Time to resolution 110 ■ Number of hand-offs 112 ■ Other customer satisfaction metrics 113· Instrumenting your classifier 113 Part 3 Training and testing.115 Training your assistant 117 6.1 Training an AI assistant 6.2 Finding training data 118 120 Production logs 121 ■ A mock user interface 125 Subject-matter experts 127· Organizing training data into intents 128 6.3 Assessing whether you have the right training data Training data variety 131 · Training data volume Training data veracity 135 ^7 How accurate is your assistant? 7.1 130 133 139 Testing an AI assistant for accuracy 140 Testinga single utterance 141 · Testing multiple utterances 143· Selecting a test data set 145 7.2 Comparing testing methodologies Blind testing 7.3 153 Selecting the right accuracy metric for the job Testing your dialogue flows 8.1 148 149 · kfolds cross-validation test 159 164 Functionally testing a dialogue flow 165 Manually testinga conversation flow 166 · Automatinga conversation flow test 168 ■ Testing the dialogue flowchart Testing the unexpected error paths 174 8.2 Nonfunctionally testing a dialogue flow User experience testing 179 · Load testing 179 181 172
xii CONTENTS Part 4 Maintenance. 185 Q Deployment and management 187 Where to store your code 188 9.1 Taking the Wild West approach code 189 9.2 Where to run your code 188 · Using source controlfor 194 Development environment 194 · Test environment 195 Production environment 196 · After the first production deployment 198 9.3 Using source control for other assets J f'i Improving your assistant x 10.1 201 203 Using a success metric to determine where to start improvements 204 Improving the first flow to fix containment problems 205 Inspecting other process flows for containment problems 212 10.2 Analyzing the classifier to predict future containment problems 215 Representative baseline data 219 10.3 215 · Finding gaps in the training When and why to improve your assistant 223 You can’t fix everything at once 223 ■ You can’t always predict how users will react 224 ■ User needs will change 225 Not every problem is technical 226 Part 5 Advanced/optional topics. 229 ƒ ƒ Л. Љ Building your own classifier ļļ i 231 Why build your own classifier? 233 Classification is a differentiator 233 ■ Classification is a core competency 234 · Traceability 234 ■ To learn 234 ■ Build or buy ? 235 11.2 Build a simple classifier from first principles The simplest text classifier classifier 238 235 236 ■ The mathematics behind a simple
xiii CONTENTS 11.3 Expanding the simple classifier 242 Predicting more than one class 243 · An all-in-one classifier 247 · Comparing binary classifiers to all-in-one classifiers 248 11.4 Extending even further 249 What happens when you add more training datai Experiment on your own 251 249 ■ Exercise: Դ Additional trainingfor voice assistants 252 12.1 Collecting data to test a speech-to-text model Call recordings as speech training data speech data 258 12.2 Testing the speech-to-text model Word error rate rate 263 12.3 255 · Generating synthetic 259 261 ■ Intent error rate Training a speech-to-text model 254 262 ■ Sentence error 265 Custom training with a language model 267 ■ Custom training with an acoustic model 269 · Custom training with grammars 272 appendix Glossary of terms and abbreviations index 285 277 |
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| index_date | 2024-07-03T20:49:38Z |
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| spelling | Freed, Andrew R. ca. 20./21. Jh. Verfasser (DE-588)1274233283 aut Conversational AI chatbots that work Andrew R. Freed Shelter Island, NY Manning Publications Co. [2021] xxiv, 291 Seiten Illistrationen, Diagramme 24 cm txt rdacontent n rdamedia nc rdacarrier Kommunikation (DE-588)4031883-7 gnd rswk-swf Sprachkompetenz (DE-588)4077722-4 gnd rswk-swf Gestaltung (DE-588)4157139-3 gnd rswk-swf Natürliche Sprache (DE-588)4041354-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Assistenzsystem (DE-588)4690490-6 gnd rswk-swf Kunde (DE-588)4114330-9 gnd rswk-swf Chatbot (DE-588)4841533-9 gnd rswk-swf Intelligent personal assistants (Computer software) / Design Künstliche Intelligenz (DE-588)4033447-8 s Assistenzsystem (DE-588)4690490-6 s Chatbot (DE-588)4841533-9 s Natürliche Sprache (DE-588)4041354-8 s DE-604 Gestaltung (DE-588)4157139-3 s Sprachkompetenz (DE-588)4077722-4 s Kunde (DE-588)4114330-9 s Kommunikation (DE-588)4031883-7 s Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033896757&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
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| title | Conversational AI chatbots that work |
| title_auth | Conversational AI chatbots that work |
| title_exact_search | Conversational AI chatbots that work |
| title_exact_search_txtP | Conversational AI chatbots that work |
| title_full | Conversational AI chatbots that work Andrew R. Freed |
| title_fullStr | Conversational AI chatbots that work Andrew R. Freed |
| title_full_unstemmed | Conversational AI chatbots that work Andrew R. Freed |
| title_short | Conversational AI |
| title_sort | conversational ai chatbots that work |
| title_sub | chatbots that work |
| topic | Kommunikation (DE-588)4031883-7 gnd Sprachkompetenz (DE-588)4077722-4 gnd Gestaltung (DE-588)4157139-3 gnd Natürliche Sprache (DE-588)4041354-8 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Assistenzsystem (DE-588)4690490-6 gnd Kunde (DE-588)4114330-9 gnd Chatbot (DE-588)4841533-9 gnd |
| topic_facet | Kommunikation Sprachkompetenz Gestaltung Natürliche Sprache Künstliche Intelligenz Assistenzsystem Kunde Chatbot |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033896757&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT freedandrewr conversationalaichatbotsthatwork |