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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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Exploring different architectures of NSAI

Although NSAI is still a relatively niche and emerging field of study, researchers from the MIT-IBM collaboration and Google’s DeepMind have already contributed interesting research concerning NSAI architectures. In this section of the chapter, we will further explore and discuss some of these main NSAI architectures that have been proven effective.

Neuro-Symbolic Concept Learner

The main objective of the Neuro-Symbolic Concept Learner (NSCL) architecture is to produce a model capable of learning to identify objects in an image and parsing and understanding their semantics and linking relationships [3]. NSCL is based on the concept that humans can understand visual concepts through their ability to bridge between vision and language. For example, let us assume we are shown a photo of a blue elephant. We immediately identify that the “object” captured in the picture is an elephant. We also understand that the color...

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