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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 1 – logic tensor networks

In our first Python NSAI example, we will implement a system based on the Logic Tensor Network (LTN) framework.

In short, LTNs are a sub-class of neural networks that leverage logical propositions (i.e., symbolic logic). LTNs use logical propositions to represent the knowledge base as formulas and deep learning to learn the different weights of these formulas. These logical propositions act as soft constraints on the neural network’s inference. If the neural network’s output violates the logical propositions, then it is penalized. As a result, an LTN during training has two main objectives: 1) satisfy the logical propositions, and 2) improve its predictive performance on the target objective. As such, the logical propositions as model constraints act as a way to directly integrate prior domain knowledge into the neural network.

For the more interested reader, you can read the full LTN paper at https://arxiv.org/pdf/1606...

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