Democratization of Artificial Intelligence for the Future of Humanity 1st Edition by Chandrasekar Vuppalapati ISBN 9780367524098 0367524090

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Product details:

ISBN 10: 0367524090
ISBN 13: 9780367524098
Author: Chandrasekar Vuppalapati

Democratization of Artificial Intelligence for the Future of Humanity 1st Edition Table of contents:

Section I—Introduction to Artificial Intelligence & Frameworks

1. Introduction

What is AI?

Machine Learning

Types of Analytics

Descriptive Analytics

Predictive Analytics

Human vs. BOT Web Traffic Prediction ML Use Case

Prescriptive Analytics

Construction Management and Prescriptive Analytics Use Case

ML and Types of data

Structured data

Unstructured data

Semi-structured data

Machine Learning and Large-Scale Analytics

Big Data

Example of Large-scale Analytical Systems

Types of Learning

Eager Learner vs. Lazy Learner

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Deep Learning

What Is a Heuristic?

Choosing the Right Estimator

Mapping AI Technique to Classical ML

AI Epochs: Waves of Compute

First Wave of Computing

Second Wave of Computing

Third Wave of Computing

The Cray 2 Computer System vs. iPhone XS

Comparison of CRAY-2 vs. iPhone XS

Fourth Wave of Computing

Fifth Wave of Computing

AI Hype Cycle—Current and Emerging Technologies

Hype Cycle for AI, 2017

Hype Cycle for AI, 2018

Hype Cycle for AI, 2019

Digital Strategy

AI End-To-End (E2E) Process—Turning Data into Actionable Insights

Data Science Lifecycle

Data Sources

Prepare & Transform

Exploratory Data Analysis (EDA)

Model

Visualization

Microsoft Azure—AI E2E Platform

AI Development Operations (DevOps) Loop for Data Science

Data Preparation

Experiment

Build Model

Train & Test Model

Deployment

Edge Devices

ML Constrained Modeling

Constrained IoT Edge Devices

Infrastructure Constraints

Operating Environment

Device Characteristics

AI Performance and Computational Notations

Sample complexity

AI Algorithm and Computational Complexity

Analysis

Time Complexity

Space Complexity

Algorithm Performance Metrics—The Asymptotic Notions

Big-O Notation (O-Notation)

Omega Notation (Ω Notation)

Theta Notation (Θ Notation)

Space and Time constraint benchmarks

AI for Greater Good—Solving Humanity and Societal Challenges

References

2. Standard Processes and Frameworks

Digital Transformation

Digital Transformation at Salesforce Definition

Digital Feedback Loop

Insights Value Chain

Data

Analytics

IT

People

Process

Strategy and Vision

Operating Model

The CRISP-DM Process

Business Understanding

Data Understanding

Data Preparation Phase

Data Modelling

Best to Divide data into Training and Validation

Data Holdout

Data Stratification

Deployment

Building Blocks of AI—Major Components of AI

AI Reference Architectures

Knowledge Discovery in Databases (KDD)

Data Mining Reference Architecture

Streaming Processing Reference Architecture

Stream Data Examples

Sensor data

Image data

Internet and Web Traffic data

Streaming Processing Rules

BLAST—Stream Model

AI Data Pipeline

Edge Processing

End-to-End Platform

Data Shifting to the Edge

Harsh Conditions

Remote Locations

Quality of Service (QoS)/Low Latency

No-Touch/Self-Healing

Global Data Experience

Nomenclature of Embedded and Edge Devices

Microcontrollers [28]

Connectivity

Operating System

Power Optimization

References

Section II—Data Sources and Engineering Tools

3. Data—Call for Democratization

Call for Action

Creating Sustainable Food Future by 2050

Nitrogen and Phosphorus Pollution Data Access Tool (NPDAT)

Investments

Designing AI that Uses Less Energy

The Last Mile—Constrained Compute Devices & “AI Chasm”

Classes of Constrained Devices

Class 0 devices

Class 1 devices

Class 2 devices

Edge Device Architecture

AI Model

Custom Built Hardware ML Models

ML Models based Packaged Frameworks—TensorFlow for C

Linker Dependencies

Connectivity

Bluetooth Low Energy (BLE)

Hardware—Storage

Files in C

EPROM Data Storage

EPROM Read and Writes

References

4. Machine Learning Frameworks and Device Engineering

Machine Learning Device Deployments

Extremely Resource-Constrained (xRC) Systems

Deep Learning Device Deployment

Arduino Nano 33 BLE Sense

SparkFun Edge

Adafruit EdgeBadge—TensorFlow Lite for Microcontrollers

ESP32-DevKitC

Extremely Resource-Constrained (xRC) Systems

xRC Modeling: Model Accuracy-Connectivity-Hardware (MCH) Framework

The Trade-off Modeling

Hardware Economy—Model Accuracy—Connectivity Trade-off

Hardware Economy

Model (AI/ML) Accuracy

Connectivity

Hardware Economy—Model Accuracy trade-off

Modeling no connectivity

Modeling Low Power Bluetooth Connectivity

The BLE Device Connect

Offload data

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy trade-off

Modeling Memory

Memory Management

Simple Memory Application

IDE

Output

Circular Buffer Design

Circular Buffers

Circular Buffer C Code

Modeling Power

Storage Modeling

AI Democratization—“Crossing the Chasm”

Infrastructure Issues

Connectivity

AI Model Design Considerations: Connectivity

Electricity

Operating Environment

AI Model Design Considerations: Operating Environmental Factors

Variations in Targeted Platform

Perturbations

Thermal Characteristics

Data

Device Characteristics: Real-time, at the edge, and a Reasonable Price Point

References

5. Device Software and Hardware Engineering Tools

Software Engineering Tools

Machine Learning Tools

Anaconda

Jupyter Notebook

Spyder

Android Studio

Google Colaboratory

Microsoft Azure Machine Learning

Azure Databricks

TensorFlow

Install TensorFlow for C

Supported Platforms

TensorFlow Lite for Microcontrollers

Hardware and Engineering Tools

Eclipse IDE for C/C++ Developers

Microsoft Visual Studio 2019

Cortex M3 Processor

GNU Arm Embedded Toolchain

Pre-built GNU Toolchain for Arm Cortex-M and Cortex-R Processors

GNU C/C++ Compiler

Doxygen

Hyperload

Libraries

C Language – C11

GNU GCC Compiler

The Compiler Options

Microsoft Visual Studio—C++ Compiler

MSVC Compiler Options

Optimization

References

Section III—Model Development and Deployment

6. Supervised Models

Decision Trees

Managing Temperature Effects in Edge IoT Deployments and Adaptive Coefficients

Temperature Variations and Sensor Data Errors

Design of Adaptive System to Autocorrect

Model Development (on Paper)

Model Development (Python)

Model Development (Citizen Data Scientist) Using Machine Learning User Interface

Constrained Environment Considerations

Decision Rule

Build the Model

Inference

Pre-Run Compute & Memory Statistics

Post Run – Compute & Memory Statistics

Hardware Economy—Model Accuracy tradeoff

Modeling for no connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy tradeoff

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware Tradeoff

Modeling: Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

XGBoost

AdaBoost Algorithm

Generalization of AdaBoost as Gradient Boosting

XGBoost

Install

Coding XGBoost

Code

The Output

Constrained Environment Considerations

Decision Paths—Simplified Rules

Time Complexity

Output

Hardware Economy—Model Accuracy trade-off

Modeling No Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy tradeoff

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware trade-off

Modeling Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

Random Forrest

Output

Random Forest Model Image

Model Generated Code

Model Equation

Naïve Bayesian

Bayesian for Multivariate

Multinomial Naiïve Bayes

Bernoulli Naive Bayes

Gaussian Naive Bayes

Device Health Prognostics

Data Issues

Device Malfunction Observations

Data Models

Naive Bayesian Model in Python

Output

Pre-Run Compute & Memory Statistics

Post Run – Compute & Memory Statistics

Constrained Environment Considerations

Naïve Bayesian C Code & ML deployment using device Firmware

ML Mode C Constants

Embed C Code

Time Complexity

Output

Hardware Economy—Model Accuracy trade-off

Modeling no Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi Fi Connectivity

Connectivity—Model Accuracy trade-off

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware trade-off

Modeling: Hardware Refresh

Modeling Over-The-Air Firmware (OTA) Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

Linear Regression

Crowdedness to Temperature Modeling (Edge State Model)

Dataset

Model Development

Model Validation

F-Test

T-Test

Data Assumptions

Assumptions 2: Linear – Residuals are Independent

Model Equation

Model Equation & Independent Parameters Coefficients

Model Development in Python

Checking Linearity

Scatter Diagram

Constrained Environment Considerations

Time Complexity

Pre-Run Compute & Memory Statistics

Post-Run – Compute & Memory Statistics

Output

Hardware Economy—Model Accuracy trade-off

Modeling no Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi Fi Connectivity

Connectivity—Model Accuracy trade-off

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware trade-off

Modeling: Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

Kalman Filter

Kalman Filter Block Diagram Representation

Kalman Filter for Smart City

Constrained Environment Considerations

Computational Complexity

Output

Pre-Run Compute & Memory Statistics

Post-Run Compute & Memory Statistics

Hardware Economy—Model Accuracy trade-off

Modeling no Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy trade-off

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware trade-off

Modeling Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

References

7. Unsupervised Models

Hierarchical Clustering

Merging Cluster Techniques

Agglomerative Cluster (Python) Code

Agglomerative Hierarchical Code in C

Single Linkage Distance Formula

Time Complexity

Pre-Run Compute & Memory Statistics

Post-Run Compute & Memory Statistics

Hardware Economy—Model Accuracy trade-off

Modeling no Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy trade-off

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware Trade-off

Modeling Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

Deployment of Climate Models in Extremely Constrained Devices (xCDs)

Data Attributes

Local Temperatures

Hyderabad India, Climate Data (From 1850 to 2012)

San Francisco Climate Data (From 1850 to 2012)

Climate Change—Hierarchical Cluster

Clusters to C Array

K-Means Clustering

Time Complexity of K-Means

Use Case: Sensor Signal and Data Interference & Machine Learning

K-Means Example

K-Means Clustering—Python Code

Computational Complexity

Output

Hardware Economy—Model Accuracy trade-off

Modeling no Connectivity

Modeling Low Power Bluetooth Connectivity

Modeling Wi-Fi Connectivity

Connectivity—Model Accuracy tradeoff

Modeling Memory

Modeling Processing Power

Modeling Storage

Modeling Environmental Perturbations

Connectivity to Hardware trade-off

Modeling Hardware Refresh

Modeling Over-The-Air (OTA) Firmware Update

Modeling Active Learner vs. Lazy Learner

Modeling Model Invocation

References

Section IV—Democratization & Future of AI

8. National Strategies

National Technology Strategies for Serving People

Artificial Intelligence of the American People

China’s New Generation of Artificial Intelligence Development Plan

Strategic Goals

Japan Strategic Council for AI & Strategy

Germany Government—AI Strategy

National Institution for Transforming India Aayog national strategy for AI

French Strategy for Artificial Intelligence—AI for Humanity

The United Nations AI Technology Strategy

The Role of the UN

AI in the Hands of People

References

9. Future

Democratization of Artificial Intelligence for the Future of Humanity

Appendix

Appendix A

Windows AI Platform—AI Platform for Windows Developers

nVidia Jetson TX2

Google Edge TPU

Intel Low Power VPU

Neural Compute Engine

Arduino Board Specification

Milk Producing Data Center using AI

Index

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Tags: Chandrasekar Vuppalapati, Democratization, Artificial Intelligence, Humanity