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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 idea behind NSAI

Bridging the intelligence gap between man and machine is our destination—the holy grail. The challenge of solving the physical brain’s complexity has intrigued researchers for years. Over the years, we raised and answered numerous questions; nonetheless, one question still prevails. Scientists know that we cannot understand the brain’s function to its entirety, especially its ability to develop common-sense knowledge—which essentially boils down to multiple complex associations that the brain can extract.

Defining common-sense knowledge

Common-sense knowledge is any knowledge that becomes almost second nature or apparent to us. For example, if we see a chair, we can practically assume we can sit on it. If we see a bed, we know that we can lie on it without falling through the mattress. The director of the MIT-IBM Watson AI Lab, David Cox, defines common-sense knowledge as “all of the implicit knowledge that we have that...

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