Book Image

Neuro-Symbolic AI

By : Alexiei Dingli, David Farrugia
Book Image

Neuro-Symbolic AI

By: Alexiei Dingli, David Farrugia

Overview of this book

Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You’ll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you’ll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You’ll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions.

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Table of Contents (12 chapters)
Preface
Preface
Who this book is for
What this book covers
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Conventions used
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1
Chapter 1: The Evolution and Pitfalls of AI
Chapter 1: The Evolution and Pitfalls of AI
The basic idea behind AI
The evolution of AI
Subfields of AI
The pitfalls of AI
Summary
Free Chapter
2
Chapter 2: The Rise and Fall of Symbolic AI
Chapter 2: The Rise and Fall of Symbolic AI
Defining Symbolic AI
Enabling machine intelligence through symbols
The fall of Symbolic AI
Symbolic AI today
The sub-symbolic paradigm
Summary
Further reading
3
Chapter 3: The Neural Networks Revolution
Chapter 3: The Neural Networks Revolution
Artificial neural networks modeling the human brain
Introducing popular neural network architectures
Delving into deep neural networks
The rise of data
The complexities and limitations of neural networks
Summary
4
Chapter 4: The Need for Explainable AI
Chapter 4: The Need for Explainable AI
What is XAI?
Why do we need XAI?
The state-of-the-art models in XAI
Summary
5
Chapter 5: Introducing Neuro-Symbolic AI – the Next Level of AI
Chapter 5: Introducing Neuro-Symbolic AI – the Next Level of AI
The idea behind NSAI
The ingredients of an NSAI system
Exploring different architectures of NSAI
Summary
Further reading
6
Chapter 6: A Marriage of Neurons and Symbols – Opportunities and Obstacles
Chapter 6: A Marriage of Neurons and Symbols – Opportunities and Obstacles
The benefits of combining neurons and symbols
The challenges of combining neurons and symbols
Research gaps in neuro-symbolic computing
Summary
7
Chapter 7: Applications of Neuro-Symbolic AI
Chapter 7: Applications of Neuro-Symbolic AI
Application 1 – health – computational drug repurposing
Application 2 – education – student strategy prediction
Application 3 – finance – bank loan risk assessment
Summary
Further reading
8
Chapter 8: Neuro-Symbolic Programming in Python
Chapter 8: Neuro-Symbolic Programming in Python
Environment and data setup
Solution 1 – logic tensor networks
Solution 2 – prediction stacking
Summary
Further reading
9
Chapter 9: The Future of AI
Chapter 9: The Future of AI
Looking at fringe AI research
Exploring future AI developments
Bracing for the rise of AGI
Preparing for singularity
Summary
Further reading
10
Index
Index
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11
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What this book covers

Chapter 1, The Evolution and Pitfalls of AI, provides an introduction to the fundamentals of AI, its various types, uses, benefits, and limitations, as well as the mechanics of building AI systems.

Chapter 2, The Rise and Fall of Symbolic AI, discusses the concept of symbolic learning and its history, inner mechanics, and limitations.

Chapter 3, The Neural Networks Revolution, introduces neural networks, their types, potential use cases, and limitations.

Chapter 4, The Need for Explainable AI, highlights the motivation for explainable AI (XAI), its importance, and the current state-of-the-art techniques.

Chapter 5, Introducing Neuro-Symbolic AI: The Next Level of AI, introduces the composite AI topic of neuro-symbolic AI, its mechanics, and its emergence as a way forward for AI development.

Chapter 6, A Marriage of Neurons and Symbols: Opportunities and Obstacles, explores the trade-offs between reasoning and learning and the benefits, challenges, and research gaps in neuro-symbolic computing.

Chapter 7, Applications of Neuro-Symbolic AI, showcases different neuro-symbolic AI applications based on different techniques, inspiring creativity in the adoption of this composite technology.

Chapter 8, Neuro-Symbolic Programming in Python, provides a basic programmatic outline to design and implement neuro-symbolic systems in Python.

Chapter 9, The Future of AI, discusses future developments of AI, the rise of artificial general intelligence (AGI), and the ethical issues associated with the creation of singularity.

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