Table Of ContentCognitive Computing with
IBM Watson
Build smart applications using artificial intelligence as a
service
Rob High
Tanmay Bakshi
BIRMINGHAM - MUMBAI
Cognitive Computing with IBM Watson
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Contributors
About the authors
Rob High is an IBM Fellow and CTO of IBM's Edge Computing business. Previously, he
was CTO for IBM Watson, responsible for setting the technical strategy and vision for IBM's
leadership in AI computing. High is recognized as a global technical leader, author, and
evangelist, and was named an IBM Fellow in 2008 for his pioneering work in the field of
SOA and for his leadership on WebSphere.
Tanmay Bakshi is a cognitive computing and AI developer, a keynote and TED speaker, an
algorithm-ist, a YouTuber, IBM Champion for Cloud, an Honorary IBM Cloud Advisor,
and a Google Developer Expert for Machine Learning. He has resolved to reach out to and
help at least 100,000 novices and beginners so they can learn how to code.
About the reviewer
Mayur Narkhede has good blend of experience in data science and industry. He is a
researcher with a B.Tech in Computer Science and an M.Tech. in Computer Science
Engineering with a specialization in artificial intelligence.
Mayur is a data scientist with experience of building automated, end-to-end solutions. He
is proficient at applying technology, AI, machine learning, data mining, and design
thinking for better understanding and predictions in order to improve business functions
and to meet the desired requirements with a growth in profitability.
He has worked on multiple advanced solutions, such as machine learning and predictive
model development for oil and gas, utilities, financial services, road traffic and transport,
life science, and big data platforms for asset-intensive industries. He also played a key role
in setting up the Data Science and Big Data lab for R&D work. He has built solutions in
areas such as asset health monitoring, batch process performance analysis, smart metering
for water utility clients, predictive model development for oil and gas customers, big data
platforms for asset-intensive industries, a traffic prediction algorithm for top Indian cities,
and clustered graph algorithms for life sciences.
He likes to play badminton and carrom, and sometimes he goes trekking, and loves to
travel new places.
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Table of Contents
Preface 1
Chapter 1: Background, Transition, and the Future of Computing 6
Transitioning from conventional to cognitive computing 7
Limitations of conventional computing 7
Solving conventional computing's problems 9
Workings of machine learning 10
Machine learning and its uses 11
Cons of machine learning 12
Introduction to IBM Watson 15
Hardware and software requirements 16
Signing up for IBM Cloud 16
Summary 17
Chapter 2: Can Machines Converse Like Humans? 18
Creating a conversational agent workspace 20
Creating an instance of Watson Assistant and a workspace 20
The sample application 23
Creating a set of conversational intents 24
Recognizing entities 32
Identifying entities through annotators 37
Building a dialog 39
Creating the dialog for a complex Intent using Frame Slots 42
Context variables 45
Programming your conversation application 51
Emerging features 59
Summary 60
Further reading 60
Chapter 3: Computer Vision 61
Can machines visually perceive the world around them? 61
The past – classical computer vision 62
The present – deep learning for computer vision 63
Creating a basic image-recognition system 64
Creating an instance of Watson Visual Recognition and a classifier 64
Uploading data and training the classifier 66
Testing the classifier 68
Creating a Python application to classify with Watson 69
Handling the case where you don't have training data 72
Using the facial detection model 73
Table of Contents
Summary 77
Chapter 4: This Is How Computers Speak 78
A computer that talks 79
Playing sound through the speaker 81
Getting fancier with how to speak 82
Controlling pronunciation 83
Customizing speech synthesis 84
Using sounds-like customization 86
Streaming and timing 86
A fun application of the speech service 88
Talking to the computer 89
Getting voice from a microphone 90
Using the WebSockets interface to speech recognition 93
Telephones are not good microphones 100
More about base models 101
Dealing with speaker hesitations 102
Customizing the speech recognition service 103
Customizing Watson's language model 103
Customizing the acoustic model for Watson 105
Leveraging batch processing 106
Summary 107
Further reading 108
Chapter 5: Expecting Empathy from Dumb Computers 109
Introducing empathy 109
Understanding the complexities of sentiment 110
The functionality of the Tone Analyzer API 111
How you can use the Tone Analyzer API 112
Understanding personality through natural language 116
Using natural language to infer personality traits 117
Calling the Personality Insights API 119
Summary 121
Chapter 6: Language - How Watson Deals with NL 122
Natural language translation – the past 123
Natural language – it's intrinsically unstructured 123
Natural language translation – the present 126
Translating between languages with Language Translator 133
Training custom NMT models with Watson 134
Categorizing text using Natural Language Classifier 135
Summary 144
Further reading 145
Chapter 7: Structuring Unstructured Content Through Watson 146
Using computers that recognize what you mean 147
[ ii ]
Table of Contents
Introducing the NLU service 148
Alternative sources of literature 151
Types of analyses 152
Categories 153
Concepts 154
Emotion 155
Sentiment 156
Entities 156
Relations 158
Keywords 159
Semantic roles 159
Parts of speech (syntax) 160
Customizing NLU 162
Preparing to annotate 163
Creating a type system 165
Adding documents 169
As an aside 170
Preparing documents for use in Watson Knowledge Studio 171
Loading documents into Watson Studio 172
Performing annotations 175
Editing the type system 182
The importance of being thorough 184
Coreferences 185
Training Watson 188
Deploying the custom model to NLU 193
Using a custom model in NLU 195
Summary 197
Chapter 8: Putting It All Together with Watson 198
Recapping Watson Services 199
Building a sample application from Watson Services 200
The use case and application 201
The program flow 202
Translating voice input 204
Determining intent 206
Prompting the user for their input 207
Setting the document of interest 209
Summarizing entities and concepts 211
Identifying an entity of interest 211
Assessing the personality of the entity 212
Assessing the tone of the entity 212
Translating text 213
Classifying text 213
Running the program 213
Setup 214
Summary 216
Chapter 9: Future - Cognitive Computing and You 217
[ iii ]
Table of Contents
Other services and features of Watson 218
Compare and Comply 218
Discovery 219
Watson Studio 220
Machine learning 220
Knowledge catalog 221
Watson OpenScale 221
The future of Watson 222
Advances in AI 223
Generative adversarial networks 223
Conversational systems 224
Deep learning 225
Edge computing 226
Bias and ethics in AI 226
Robotics and embodiment 227
Quantum computing and AI 228
The future of AI 229
Summary 231
Another Book You May Enjoy 232
Index 234
[ iv ]
