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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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Introducing Neuro-Symbolic AI – the Next Level of AI

Throughout the previous chapters, we discussed how the primary motivation behind artificial intelligence (AI) is to model human intelligence into computer systems. Over the years, we have witnessed the innovation of many algorithms and techniques that improved computer cognitive and logical processing machines. In Chapter 2, we introduced symbolic AI as one of the first research efforts targeted toward achieving this highly desirable accolade. We discussed how symbolic AI has enabled us to embed world-knowledge constructs (logical rules) into our computer systems. However, the symbolic AI process has proven to be rather cumbersome and expensive. Researchers have also discovered that symbolic AI programs tend to lose accuracy as more rules are represented in the program. To circumvent the tedious process of symbolic rule representation, the field shifted its focus to more data-driven techniques.

Neural networks (NNs) and...

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