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Neuro-Symbolic AI

Neuro-Symbolic AI

By : Alexiei Dingli, David Farrugia
3.7 (6)
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Neuro-Symbolic AI

Neuro-Symbolic AI

3.7 (6)
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.
Table of Contents (12 chapters)
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Neuro-Symbolic Programming in Python

Neuro-symbolic artificial intelligence (NSAI) systems are not constrained by standardized principles or confined by special requirements. The only consideration in NSAI systems is the combination of symbolic artificial intelligence (AI) and neural networks (NNs). As reviewed in the previous chapter, NSAI implementations vary widely in scope and architecture. Although NSAI is a product of decades of research, the marriage between the two fields remains fresh. Some frameworks have gained more traction than others; however, none stand out as state of the art yet. Even evaluation strategies focused on NSAI algorithms are yet to solidify. Designing NSAI solutions requires solid creative direction by the developers, heavily influenced by domain knowledge and expertise. When working with NSAI, there are some considerations to keep in mind. How will deep learning interact (either directly or indirectly) with the symbolic learning component? How will we...

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