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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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The Need for Explainable AI

In the past few years, artificial intelligence (AI) has achieved impressive feats, much more than it ever did in the preceding decades. If we consider what happened in the last pandemic, AI was instrumental in helping the pharmaceutical industry develop a vaccine in record time. But AI’s extensive use is widespread in detecting all sorts of cancers, developing new drugs, and rehabilitating patients. On another front, we now have self-driving cars taking us from one place to the other, our airplanes are flown by an auto-pilot 90% of the time, and the level of safety we’ve reached is unheard of. If we look at basic human tasks, the ImageNet competition showed that even when it comes to simply labeling images (something we learned to do when we were still infants), an AI is capable of achieving much more accurate results. The list of examples can keep on going forever since AI systems can be found in most applications. The truth is that AI can...

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Neuro-Symbolic AI
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