A.I. Revealed E-Book

Erik Herman

• Herausgeber: Packt Publishing
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 175

Veröffentlichungsjahr: 2025

Beschreibung

Unlock the transformative power of artificial intelligence in this comprehensive journey through its foundational principles and practical applications. Start by grasping the essentials of AI and machine learning, progressing to advanced topics like deep learning, neural networks, and cutting-edge applications in natural language processing and computer vision. Each concept is delivered with real-world relevance, ensuring a clear understanding of the theory and its implementation.

Dive deeper into the ethical dimensions of AI, exploring critical issues such as bias and fairness. Gain insights into how AI is revolutionizing industries through case studies, bridging the gap between theoretical knowledge and practical application. The course culminates with an exploration of emerging technologies and the future of AI, equipping you with foresight into its transformative potential.

Whether you're taking your first steps in AI development or seeking to enhance your existing skills, this course offers a structured pathway to mastery. With hands-on guidance, you'll develop a robust foundation and confidence to contribute meaningfully in the rapidly evolving field of artificial intelligence.

Leseprobe

AI

REVEALED

LICENSE, DISCLAIMER OF LIABILITY, AND LIMITED WARRANTY

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AI

REVEALED

Theory • Applications • Ethics

Erik Herman

MERCURY LEARNING AND INFORMATION

Boston, Massachusetts

Copyright ©2025 by MERCURY LEARNING AND INFORMATION.An Imprint of DeGruyter Inc. All rights reserved.

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E. Herman. AI Revealed: Theory • Applications • Ethics

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To the dearly departed, now,and forever one of the grateful dead.

CONTENTS

Preface

Chapter 1: The Foundations of Artificial Intelligence

What is Artificial Intelligence?

Definition of AI

Types of AI

Narrow AI

General AI

Artificial Superintelligence

Core Components of AI Systems

Machine Learning (ML)

Neural Networks

Robotics

Expert Systems

The History of AI

Early Concepts and Theories

Key Milestones

Modern Developments

Importance and Applications of AI

Transformational Impact

Healthcare

Automotive

Finance

Customer Service

Daily Life

Ethical Considerations

Fundamental Questions

Bias and Fairness

Privacy

Regulation and Governance

AI Application: Create a Simple Rule-Based Chatbot

Step 1: Set Up the Development Environment

Step 2: Create the Chatbot Script

Step 3: Run the Chatbot

Step 4: Understand the Script

Conclusion

Chapter 2: Foundations of Machine Learning

Introduction to Machine Learning

Definition and Scope

How ML Works

Key Components

Data

Model

Learning Algorithm

Evaluation Metrics

Supervised Learning

Concept and Mechanism

Common Algorithms

Linear Regression

Logistic Regression

Decision Trees

Support Vector Machines (SVMs)

Applications

Spam Detection

Sentiment Analysis

Credit Scoring Systems

Medical Diagnosis

Fraud Detection

Predictive Maintenance

Customer Churn Prediction

Stock Market Prediction

Unsupervised Learning

Concept and Mechanism

Common Algorithms

Clustering Algorithms

Association Algorithms

Dimensionality Reduction Techniques

Applications

Customer Segmentation

Market Basket Analysis

Anomaly Detection

Social Network Analysis

Document Clustering

Image Compression

Bioinformatics

Model Evaluation and Selection

Evaluation Metrics

Precision and Recall

The F1 Score

Mean Squared Error (MSE) and Root Mean Squared Error (RMSE)

Area Under the Receiver Operating Characteristic Curve (AUC-ROC)

Validation Techniques

Cross-Validation

Bootstrapping

Model Selection

AI Application: Implement a Linear Regression Model to Predict House Prices

Step 1: Set Up the Development Environment

Step 2: Prepare the Dataset

Step 3: Implement the Linear Regression Model

Step 4: Run the Script

Conclusion

Chapter 3: Deep Learning and Neural Networks

Introduction to Deep Learning

Understanding Neural Networks

Basic Structure

How They Learn

Types of Neural Networks

Feedforward Neural Networks

Recurrent Neural Networks (RNNs)

Convolutional Neural Networks (CNNs)

Transformers

Key Architectures in Deep Learning

CNNs

RNNs

Transformers

Challenges and Ethical Considerations

Computational Demands

Data Requirements

Privacy and Security

Artificial Neural Networks (ANNs)

Fundamentals of ANNs

Neuron Model

Layers of an ANN

Activation Functions

Training Neural Networks

Backpropagation

Loss Functions

Optimization Algorithms

Practical Applications of ANNs

Finance

Healthcare

Industrial Automation

CNNs

Architecture of CNNs

Convolutional Layers

Pooling Layers

Fully Connected Layers

Functionality and Training of CNNs

Feature Learning

Backpropagation in CNNs

Advanced Training Techniques

Real-World Applications of CNNs

Facial Recognition Systems

Medical Imaging

Automotive Industry

RNNs

Handling Sequential Data

Advanced Architectures

Applications of RNNs

Language Translation Services

Voice-Activated Assistants

Financial Forecasting

Advanced Architectures (for example, GANs, Transformers)

GANs

Generator

Discriminator

Training Process

Applications

Transformers

Core Mechanism

Training Efficiency

Applications

AI Application: Build a Basic Neural Network for Digit Classification Using MNIST Dataset

Step 1: Set Up the Development Environment

Step 2: Load and Preprocess the MNIST Dataset

Step 3: Create the Neural Network

Step 4: Train the Neural Network

Step 5: Evaluate the Model

Step 6: Run the Script

Conclusion

Chapter 4: Natural Language Processing (NLP)

Introduction to NLP

Fundamentals of NLP

Syntax

Semantics

Pragmatics

Techniques in NLP

Text Preprocessing

Parsing and Part-of-Speech Tagging

Machine Learning (ML) in NLP

Challenges in NLP

Ambiguity and Context

Slang and Dialects

Resource Availability

Real-World Applications of NLP

Voice-Activated Assistants

Customer Service Bots

Automated Translation Services

Text Preprocessing

Tokenization

Stemming

Lemmatization

Removing Stop Words

Sentiment Analysis

Named Entity Recognition

Machine Translation

AI Application: Perform Sentiment Analysis on a Set of Movie Reviews

Step 1: Set Up Development Environment

Step 2: Load and Preprocess the Movie Reviews Dataset

Step 3: Train the Sentiment Analysis Model

Step 4: Evaluate the Model

Step 5: Run the Script

Conclusion

Chapter 5: Computer Vision

Introduction to Computer Vision

Image Preprocessing

Grayscale Conversion

Histogram Equalization

Normalization

Edge Detection

Object Detection

Region-Based Convolutional Neural Networks (R-CNNs)

YOLO (You Only Look Once)

SSD (Single Shot Multidetector)

Image Classification

Convolutional Neural Networks (CNNs)

Machine Learning Algorithms

Image Segmentation

Thresholding

Clustering Methods

Advanced Methods

AI Application: Implement an Image Classification Model Using CIFAR-10 Dataset

Step 1: Set Up Development Environment

Step 2: Load and Preprocess the CIFAR-10 Dataset

Step 3: Build the Image Classification Model

Step 4: Train the Image Classification Model

Step 5: Evaluate the Model

Step 6: Run the Script

Conclusion

Chapter 6: Ethics and Bias in AI

Ethical Considerations in AI

Bias in AI Algorithms

Fairness and Accountability

Regulation and Governance

AI Application: Analyze Bias in a Dataset and Discuss Mitigation Strategies

Step 1: Set Up Development Environment

Step 2: Load and Explore the Dataset

Step 3: Preprocess the Data

Step 4: Train a Baseline Model

Step 5: Analyze Bias in the Model

Step 6: Mitigate Bias

Step 7: Run the Script

Conclusion

Chapter 7: AI in Practice: Industry Case Studies

Healthcare

Finance

Transportation

Retail

Manufacturing

AI Application: Predicting Patient Outcomes in Healthcare

Step 1: Set Up Development Environment

Step 2: Load and Explore the Healthcare Dataset

Step 3: Preprocess the Data

Step 4: Train a Predictive Model

Step 5: Evaluate the Model

Conclusion

Chapter 8: Future of AI and Emerging Technologies

Quantum Computing

Edge AI

Explainable AI

AI for Social Good

AI Application: Experiment With a Simple Quantum Computing Algorithm Using IBM’s Qiskit

Step 1: Set Up Development Environment

Step 2: Introduction to Quantum Computing Basics

Step 3: Implement a Basic Quantum Algorithm

Conclusion

Chapter 9: Getting Started With AI Development

Setting Up Development Environment

Introduction to Python for AI

Using Popular AI Libraries

AI Application: Set Up an AI Development Environment and Run a Basic Python Script

Step 1: Install Python

Step 2: Install Jupyter Notebook

Step 3: Set Up a Virtual Environment (optional but recommended)

Step 4: Create and Run a Jupyter Notebook

Conclusion

Appendix A: Overview of the Lisp Programming Language

Appendix B: Resources and Community

Index

PREFACE

Welcome to AI Revealed: Theory • Application • Ethics, an exploration into the world of artificial intelligence (AI). This book aims to unveil the multifaceted domain of AI, a field that has transformed the landscape of technology and its interaction with human society. Whether you are a student, a professional stepping into the realm of AI, or a curious mind eager to understand the underpinnings and implications of this technology, this book is designed to cater to your intellectual curiosity.

AI today is not just a field of study; it’s an integral and dynamic part of our daily lives. From the algorithms that curate our social media feeds to the sophisticated systems driving autonomous vehicles, AI’s applications are vast and expanding at an unprecedented rate. However, the journey of AI from theoretical concepts to real-world applications is layered with intricate developments, challenges, and ethical debates.

This book begins with Chapter 1, offering an introduction to AI, including its definition, the various types of AI, and the core components such as machine learning, neural networks, robotics, and expert systems. We delve into the history of AI, tracing early concepts, key milestones, and the evolution of modern AI technologies.

In Chapter 2, we lay the foundations of machine learning, exploring essential concepts like supervised and unsupervised learning, and discussing model evaluation and selection methods. This chapter sets the stage for understanding how machines learn from data to make intelligent decisions.

Chapter 3 examines deep learning and neural networks, covering artificial neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). We also touch upon advanced architectures like Generative Adversarial Networks (GANs) and Transformers, essential for understanding the cutting-edge developments in AI.

Chapter 4 introduces natural language processing (NLP), examining how AI understands and processes human language. We cover topics like text preprocessing, sentiment analysis, named entity recognition, and machine translation, showcasing AI’s capabilities in interpreting and generating human language.

Chapter 5 focuses on computer vision, exploring the techniques used to enable machines to see and understand visual information. It includes topics like image preprocessing, object detection, image classification, and image segmentation, demonstrating AI’s ability to interpret and interact with visual data.

In Chapter 6, we address the critical ethical considerations in AI, including issues of bias in AI algorithms, fairness, accountability, and the need for regulation and governance. This chapter emphasizes the importance of developing AI technologies responsibly to avoid unintended consequences.

Chapter 7 presents real-world industry case studies, showcasing AI’s impact across sectors like healthcare, finance, transportation, retail, and manufacturing. We highlight both the potential and challenges of implementing AI technologies in these industries.

Chapter 8 looks ahead to the future of AI and emerging technologies. Topics such as quantum computing, edge AI, explainable AI, and AI for social good are explored, giving insight into the next frontier of AI innovation.

Chapter 9 serves as a practical guide for readers interested in AI development. It covers setting up a development environment, introduces Python for AI, and provides an overview of popular AI libraries, helping readers start their journey in AI programming.

AI Revealed is not just a textbook; it is a comprehensive guide to understanding and engaging with AI at multiple levels. It includes practical applications and hands-on projects, helping readers bridge theory with practice and apply AI creatively and ethically.

Join us as we embark on this journey, aiming to define AI, explain its workings, and explore how it can be harnessed responsibly to benefit humanity. Together, we will unfold the layers of AI as we step into a future where the fusion of human and artificial intelligence continues to shape new frontiers.

Acknowledgments

Without Grace Hopper, these are all just words in the wind. I acknowledge that on the minds and backs of too many great women and men to mention does this work come forth, so I pause and continue the work.

Erik HermanOctober 2024

CHAPTER 1

THE FOUNDATIONSOF ARTIFICIAL INTELLIGENCE

This opening chapter explores the realm of artificial intelligence (AI), starting with its definition and spanning its rich history, including the importance, and diverse applications. As AI continues to integrate into various facets of modern life, understanding its fundamentals becomes essential. This chapter lays the groundwork by introducing key concepts and terms, tracing the evolution of AI technologies, and discussing their transformative impact on society. This chapter also begins the exploration of the ethical considerations that underpin AI development and deployment, setting the stage for deeper discussions in later chapters.

FIGURE 1.1  A conceptual representation of AI.

Figure 1.1 illustrates a conceptual representation of AI, depicted as a human brain formed by interconnected circuits and glowing nodes. The intricate network symbolizes the complexity and interconnectivity of AI technologies, reflecting the integration of digital and cognitive processes that mimic human intelligence. This visual metaphor highlights the technological foundation of AI, emphasizing its role in processing information and generating intelligent behavior.

WHAT IS ARTIFICIAL INTELLIGENCE?

Artificial intelligence (AI) is the scientific field dedicated to creating machines capable of performing tasks that typically require human intelligence. These tasks include decision-making, language translation, visual perception, speech recognition, and problem-solving. AI encompasses a broad spectrum of technologies and methodologies aimed at building systems that can adapt to new inputs, learn from data, and improve over time without human intervention. The goal of AI research and development is to enhance the ability of machines to mimic cognitive functions and carry out complex tasks with efficiency and accuracy.

AI can be categorized into distinct types based on their capabilities and applications. Narrow AI, also known as weak AI, is designed to perform a specific task, such as facial recognition or language translation, and operates within a limited scope. In contrast, general AI, or strong AI, aims to replicate human intelligence and can perform any intellectual task that a human can. The most advanced form, superintelligence, refers to AI that surpasses human intelligence in all aspects, including creativity, problem-solving, and emotional understanding. Core components of AI include machine learning, which involves training algorithms on large datasets to make predictions or decisions, and neural networks, which are modeled after the human brain and enable deep learning and pattern recognition. Understanding these fundamentals is crucial for grasping the full potential and implications of AI in various fields.

Definition of AI

Artificial intelligence (AI) is defined as the capability of a machine or system to perform tasks that typically require human intelligence. These tasks encompass a wide range of activities, such as visual perception, speech recognition, decision-making, and language translation. AI systems leverage algorithms and statistical models to execute these complex functions. By processing vast amounts of data, they can identify patterns and make informed decisions, simulating elements of human cognitive function.

At its core, AI aims to mimic the human mind’s ability to learn, reason, and solve problems. Through techniques like machine learning and deep learning, AI systems improve their performance over time as they are exposed to more data. This adaptability allows AI to handle increasingly sophisticated tasks, from recognizing faces in photos to translating entire documents across languages. Understanding these foundational aspects of AI is crucial for appreciating its potential to transform various industries and enhance everyday life.

Types of AI

AI can be categorized into three main types based on its capabilities and scope of function:

Narrow AI

Also known as weak AI, these systems are designed to handle a single or limited task. Examples include speech recognition, image recognition, and search engines. Narrow AI operates under a set of constraints and limitations, performing predefined functions without possessing consciousness or understanding. It excels at specific tasks but lacks the ability to perform beyond its programmed scope. Common applications include virtual assistants like Siri and Alexa, recommendation systems on streaming platforms, and autonomous vehicles.

General AI

These systems possess the capability to understand and learn any intellectual task that a human being can. This type of AI, also referred to as strong AI, is still largely theoretical and not yet fully realized in practical applications. General AI would require a machine to have the same cognitive abilities as humans, including reasoning, problem-solving, and abstract thinking. It would be able to transfer knowledge from one domain to another, learn new tasks without human intervention, and adapt to new situations autonomously.

Artificial Superintelligence

A hypothetical form of AI that surpasses human intelligence and ability across a wide range of disciplines, including scientific creativity, general wisdom, and social skills. This type of AI would not only perform tasks better than humans but also make decisions and solve complex problems in ways that are currently beyond human comprehension. The development of artificial superintelligence raises significant ethical and existential questions, including the potential risks of losing control over such powerful systems and the impact on human society.

Core Components of AI Systems

AI systems are composed of several core components that enable their functionality:

Machine Learning (ML)

Machine learning (ML) is the backbone of most AI systems, where algorithms learn from and make predictions based on data. ML enables systems to improve their performance over time by identifying patterns and relationships within the data. It encompasses various techniques, including supervised learning, unsupervised learning, and reinforcement learning, each suited to distinct types of tasks and data structures.

Neural Networks

Neural networks are inspired by the human brain, these networks are a series of algorithms that capture relationships among data. They are particularly effective at processing patterns or trends within large sets of data. Neural networks, especially deep learning models, have revolutionized fields such as image and speech recognition, natural language processing, and game playing by enabling machines to perform complex tasks with high accuracy.

Robotics

The field of robotics integrates AI with mechanical and electronic systems to create physical entities that perform tasks autonomously or with minimal human intervention. Robotics leverages AI to enhance capabilities such as navigation, object manipulation, and interaction with environments, leading to advancements in areas like manufacturing, healthcare, and service industries. Autonomous drones, robotic surgical systems, and warehouse automation are prime examples.

Expert Systems

Expert systems are AI systems that mimic the decision-making ability of a human expert. By processing a set of rules, these systems provide conclusions, solutions, or diagnoses, applying reasoning capabilities and knowledge to a broad range of activities. Expert systems are widely used in medical diagnosis, financial forecasting, and customer support, where they enhance decision-making processes by offering expert-level insights and recommendations.

These components represent the foundational technologies that enable AI to act and react in a manner that closely resembles human intelligence, making them essential to the development and advancement of AI applications. They collectively contribute to the versatility and adaptability of AI systems, driving innovation across various sectors.

THE HISTORY OF AI