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Neuro-Symbolic AI

Neuro-Symbolic AI

By : Alexiei Dingli, Farrugia
3.7 (6)
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Neuro-Symbolic AI

Neuro-Symbolic AI

3.7 (6)
By: Alexiei Dingli, 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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A Marriage of Neurons and Symbols – Opportunities and Obstacles

Research interest in neuro-symbolic artificial intelligence (NSAI) is on the rise. Currently, most researchers and artificial intelligence (AI) practitioners rely on deep learning (DL) algorithms as solutions to their tasks. The drawbacks and limitations have been widely documented, ranging from extremely resource-hungry processes to ambiguous and complex inner mechanics rendering a certain lack of interpretability. The modern generation acknowledges the importance of producing systems that we can fully understand. We also understand the significance of developing efficient processes that can achieve high-performance levels with fewer resources. For example, the craze of cryptocurrency (especially Bitcoin) mining saw a huge spike in computing electricity consumption worldwide. Forbes estimates that Bitcoin mining consumes roughly 127 terawatt-hours of electricity annually. To put this into perspective, that is greater...

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