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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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Solution 2 – prediction stacking

We started this chapter by stating that NSAI is not constrained by design, development rules, or principles. It is simply the marriage of symbolic learning and NNs. What does this mean for us? First, we can still leverage the power of NSAI without using complex algorithms or spending too much time figuring out the best way to extract the knowledge base. NSAI is highly creative. Following, we will go through the process of implementing a much simpler NSAI system using the same dataset.

In our previous example, we focused on representing knowledge as axioms. We feed this representation to the NN to map the relationships between the various dimensions to learn knowledge. Another way to extract knowledge in the form of symbolic statements would be to use decision trees (DTs). DTs use logical rules to make decisions and map the training data in a tree-like structure. Every node in the tree represents some logical condition, and the subsequent nodes...

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