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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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Summary

With the current technological advancements, NSAI has high expectations to reach. So far, researchers are hinting at rather promising results achieved by NSAI. The field is still emerging and relatively niche, but its properties and inherent understanding present interesting opportunities for NSAI.

Throughout this chapter, we discussed some of the main benefits of NSAI, as highlighted by research so far. We saw how NSAI can achieve high performance with significantly few data points while being highly interpretable and transparent. Some challenges and limitations of its parent techniques, mainly those of symbolic AI, are somewhat evident in NSAI. Mainly, challenges revolve around the knowledge base and the symbolic integration with NN. We also discussed the future research direction of NSAI.

In the next chapter, we will discuss some real-life applications of NSAI in various businesses and industries.

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