Table Of ContentDemocratization of Artificial
Intelligence for the Future of
Humanity
Chandrasekar Vuppalapati
Senior Vice President – Products & Programs
Hanumayamma Innovations
and Technologies, Inc.
Fremont, California, USA
p,
A SCIENCE PUBLISHERS BOOK
First edition published 2021
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Library of Congress Cataloging‑in‑Publication Data
Names: Vuppalapati, Chandrasekar, 1972- author.
Title: Democratization of artificial intelligence (AI) for the future of
humanity / Chandrasekar Vuppalapati.
Description: First edition. | Boca Raton : CRC Press ; Taylor & Francis
Group, 2021. | Includes bibliographical references and index.
Subjects: LCSH: Artificial intelligence--Social aspects. | Artificial
intelligence--Industrial applications. | Artificial intelligence--Government policy.
Classification: LCC Q335 .V88 2021 | DDC 006.3--dc23
LC record available at https://lccn.loc.gov/2020036006
ISBN: 978-0-367-52409-8 (hbk)
ISBN: 978-0-367-52412-8 (pbk)
ISBN: 978-1-003-05778-9 (ebk)
Typeset in Times New Roman by
Radiant Productions
Dedication
The author dedicates his works, book, and efforts of this book to his late father Murahari Rao Vuppalapati,
who had inspired him to write the book and guided the author’s pursuits of life, and to his loving mother
Hanumayamma Vuppalapati, who was the inspiration to develop the company and dairy products for the
betterment of humanity and future generations.
Preface
Artificial Intelligence (AI) refers to the ability of machines to perform cognitive tasks like thinking, perceiving,
learning, problem solving and decision making. Initially conceived as a technology that could mimic human
intelligence, AI has evolved in ways that far exceed its original conception. With incredible advances made
in data collection, processing and computation power, intelligent systems can now be deployed to take over
a variety of tasks, enable connectivity and enhance productivity.
As AI’s capabilities have dramatically expanded, so have its utility in a growing number of fields. AI
holds tremendous promise to benefit nearly all aspects of society, including the economy, healthcare, security,
the law, transportation, even technology itself. Increased use of Artificial Intelligence (AI) can bring major
social and economic benefits to the World. AI offers massive gains in efficiency and performance to most
or all industry sectors, from drug discovery to logistics. It is no wonder that the Artificial intelligence (AI)
stands out as a transformational technology of our digital age—and its practical application throughout the
economy is growing apace.
One of the key factors that has contributed to the success of AI is data revolution and availability of
large data. To continue developing and applying AI, the world and humanity will need to increase ease of
access to data in a wider range of sectors. These sectors are outside traditional business forms and are at
large spread throughout the world operated as social businesses, small corporations, individual businesses,
and other industrial sectors. One common character these sectors endure, importantly, is lack of Information
Communication Technology (ICT) to reap benefits of AI. For these AI-enabled interventions to be effectively
applied, several barriers must be overcome. These include the challenges of data, computing, and talent
availability, as well as more basic challenges of access, infrastructure, and financial resources that are
particularly acute in remote or economically challenged places and communities. To successfully disseminate
AI to masses and enable successful democratization, we need to bring these sectors and rural communities to
digital revolution (people, technology and data together).
In order to harvest the benefits of AI revolution to humanity, the traditional AI software development
paradigms must be fully upgraded to function successfully in environments that have resource constraints; small
form factor compute devices with limited crunch power, intermittent or no connectivity and/or powered by
non-perpetual source or battery-powered. The mission of our book, Democratization of Artificial Intelligence
(AI) for the Future of Humanity, is to prepare current and future software engineering teams with the skills and
tools to fully utilize AI capabilities in extremely resource-constrained devices. The book introduces essential
AI concepts from the perspectives of full-scale software development with emphasis on creating niche Blue
Ocean small form factored compute environment products. It also
• Outlines AI software architecture & Cloud architecture with emphasis on Edge Computing
• Provides comprehensive comparison and applicability of AI algorithms in constrained environments:
Supervised and unsupervised.
• Emphasizes real-time embedded storages for AI applications, specifically operating in constrained
environments.
• Develops AI driver software code with real-time deep learning on small footprint frameworks such as
tensor flow, python, C and Android
Preface v
• Provides exclusive examples of AI field deployments that operate in remote & non-connected environments
• Explains AI solution development from a product management perspective.
The strategy should strive to leverage AI for economic growth, social development and inclusive growth,
and finally as an “enabler” for emerging and developing economies and humanity at large. We strongly believe
that “technologies and innovations for helping humanity” to achieve such we need to democratize AI to the
people who need the most.
Acknowledgements
The author is sincerely thankful and indebted to the emergency staff first responders, medical professionals,
doctors, nurses, grocery shop personnel, farmers, teachers, individuals and all the wonderful leaders of the
world for actively taking care lovingly and honourably of citizens across the world from Corona virus. As of
completion of the book, 20 March, 2020, the Coronavirus disease (COVID-19) is affecting people all over the
world and many cities across the world are in lockdown. Without the support of these true heroes, it would
be an impossible task to focus on the book and complete the book. I sincerely salute to all the people who are
working hard to maintain normalcy in this exceptional and unbelievable hard times.
Second, the author is deeply indebted to the love, support, and encouragement of his wife Anitha Ilapakurti,
and the active support of his daughters, Sriya Vuppalapati and Shruti Vuppalapati who helped to complete
the manuscript.
The author is deeply thankful to the support of his elder brother, Rajasekar Vuppalapati, and the joyful
support of his younger brother, Jaya Shankar Vuppalapati. Additionally, the author is also very thankful to his
elder sisters Padmavati Vuppalapati and Sridevi Vuppalapati.
The author sincerely thanks Santosh Kedari and his team in India for conducting field studies in India
for Dairy and Sanjeevani Healthcare analytics products and bringing the market feedback for the betterment
of services.
The author is deeply indebted to Sharat Kedari, who helped in setting Cloud and ML algorithms. In
addition, the author is thankful for the support, hard work and dedication of Vanaja Mamidi who helped to solve
embedded systems and build of iDispenser for running AI & ML algorithms on embedded device architectures.
Finally, the author is sincerely and deeply thankful to Sandhya Vissapragada for her contributions in Data
Science and in creating Machine Learning models on Microsoft Azure and Google Cloud.
The author is sincerely thankful to the support of fellow researchers and engineers including Sudha Rama
Chandran for creating Android Mobile Applications and reviewing Chapters 1 through 4 from engineering
point of view, Sneha Iyer for reviewing the engineering details of Chapter 3 and Chapter 4, Surbhi Rautji for
developing ML validations scripts and Tessie Powers for her review of the first three chapters of the book
from business and product management point of view and providing valuable feedback to improve the overall
quality of the book.
Contents
Dedication iii
Preface iv
Acknowledgements vi
SECTION I—Introduction to Artificial Intelligence & Frameworks
1. Introduction 1
What is AI? 2
Machine Learning 3
Types of Analytics 3
Descriptive Analytics 3
Predictive Analytics 4
Human vs. BOT Web Traffic Prediction ML Use Case 5
Prescriptive Analytics 6
Construction Management and Prescriptive Analytics Use Case 7
ML and Types of data 7
Structured data 8
Unstructured data 8
Semi-structured data 9
Machine Learning and Large-Scale Analytics 9
Big Data 9
Example of Large-scale Analytical Systems 11
Types of Learning 11
Eager Learner vs. Lazy Learner 11
Supervised Learning 11
Unsupervised Learning 14
Reinforcement Learning 15
Deep Learning 15
What Is a Heuristic? 16
Choosing the Right Estimator 17
Mapping AI Technique to Classical ML 18
AI Epochs: Waves of Compute 18
First Wave of Computing 19
Second Wave of Computing 19
Third Wave of Computing 19
The Cray 2 Computer System vs. iPhone XS 19
Comparison of CRAY-2 vs. iPhone XS 20
Fourth Wave of Computing 20
Fifth Wave of Computing 22
AI Hype Cycle—Current and Emerging Technologies 23
viii Democratization of Artificial Intelligence for the Future of Humanity
Hype Cycle for AI, 2017 23
Hype Cycle for AI, 2018 23
Hype Cycle for AI, 2019 25
Digital Strategy 27
AI End-To-End (E2E) Process—Turning Data into Actionable Insights 27
Data Science Lifecycle 27
Data Sources 28
Prepare & Transform 28
Exploratory Data Analysis (EDA) 28
Model 28
Visualization 29
Microsoft Azure—AI E2E Platform 30
AI Development Operations (DevOps) Loop for Data Science 30
Data Preparation 31
Experiment 31
Build Model 31
Train & Test Model 31
Deployment 32
Edge Devices 32
ML Constrained Modeling 33
Constrained IoT Edge Devices 34
Infrastructure Constraints 35
Operating Environment 35
Device Characteristics 35
AI Performance and Computational Notations 37
Sample complexity 37
AI Algorithm and Computational Complexity 38
Analysis 38
Time Complexity 38
Space Complexity 38
Algorithm Performance Metrics—The Asymptotic Notions 38
Big-O Notation (O-Notation) 39
Omega Notation (Ω Notation) 40
Theta Notation (Θ Notation) 41
Space and Time constraint benchmarks 42
AI for Greater Good—Solving Humanity and Societal Challenges 42
References 45
2. Standard Processes and Frameworks 48
Digital Transformation 50
Digital Transformation at Salesforce Definition 51
Digital Feedback Loop 51
Insights Value Chain 52
Data 53
Analytics 53
IT 53
People 53
Process 53
Strategy and Vision 53
Operating Model 53
The CRISP-DM Process 54
Contents ix
Business Understanding 54
Data Understanding 55
Data Preparation Phase 55
Data Modelling 56
Best to Divide data into Training and Validation 57
Data Holdout 59
Data Stratification 59
Deployment 59
Building Blocks of AI—Major Components of AI 59
AI Reference Architectures 62
Knowledge Discovery in Databases (KDD) 62
Data Mining Reference Architecture 64
Streaming Processing Reference Architecture 64
Stream Data Examples 65
Sensor data 65
Image data 65
Internet and Web Traffic data 66
Streaming Processing Rules 66
BLAST—Stream Model 66
AI Data Pipeline 66
Edge Processing 67
End-to-End Platform 68
Data Shifting to the Edge 70
Harsh Conditions 71
Remote Locations 71
Quality of Service (QoS)/Low Latency 71
No-Touch/Self-Healing 71
Global Data Experience 72
Nomenclature of Embedded and Edge Devices 72
Microcontrollers [28] 73
Connectivity 74
Operating System 74
Power Optimization 77
References 79
SECTION II—Data Sources and Engineering Tools
3. Data—Call for Democratization 81
Call for Action 84
Creating Sustainable Food Future by 2050 84
Nitrogen and Phosphorus Pollution Data Access Tool (NPDAT) 86
Investments 86
Designing AI that Uses Less Energy 87
The Last Mile—Constrained Compute Devices & “AI Chasm” 88
Classes of Constrained Devices 88
Class 0 devices 89
Class 1 devices 89
Class 2 devices 89
Edge Device Architecture 89
AI Model 90
Custom Built Hardware ML Models 91
