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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 state-of-the-art models in XAI

XAI is a research area that has been gaining popularity in the past few years. In this section, we will look at a synthesis of the most critical XAI models in use today.

Accumulated Local Effects

The Accumulated Local Effects (ALE) method computes the effects of features globally. It is mainly used with tabular data, where different variables can be compared. The idea behind ALE is that if we have a small enough window, we can create an accurate estimate of the changes within a specific period. So, if we have a variable and we can sample its values across different periods, we can create an accurate estimate of how that variable is changing over time. The process is then repeated across all the accumulated data and is used to augment the global prediction. The algorithm focuses on the changes between one sampling point and the other, thus making the data relatively easy to interpret for any analyst.

Anchors

Anchors try to explain the behavior...

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