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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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The benefits of combining neurons and symbols

In the previous chapter, we mentioned several key elements that make NSAI attractive to researchers. The idea of merging two parent AI techniques and trimming away all their limitations sounds rather promising. NSAI research has explored this new generation of AI workers under different lights – each highlighting various opportunities presented by NSAI. Having said that, as argued by the director of the MIT-IBM Watson AI research lab, Dr. Cox, we can group the benefits of NSAI into three main categories. Throughout this section, we will introduce each of these categories and delve further into how they are accomplished by NSAI.

Data efficiency

There is no denying that neural networks (NNs) have been the key to multiple state-of-the-art solutions. There is no denying the powerful capabilities offered by NNs to solve highly complex problems. However, NNs are incredibly data-hungry. As we discussed in the previous chapters, NNs...

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