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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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Summary

Throughout this chapter, we highlighted the importance and potential of NSAI in various real-world applications. By combining the strengths of both neural networks and symbolic reasoning, NSAI can handle the complexity and uncertainty of real-world problems, providing valuable insights and accurate predictions. This chapter discussed using NSAI in the healthcare, education, and finance industries in separate applications.

This chapter showed how NSAI can help solve real-world problems. As AI continues to evolve, the use of NSAI will become increasingly important. The investigated applications show that NSAI is a powerful tool for solving complex problems and offers the added benefit of interpretability, which is essential for real-life applications. A common theme in these NSAI applications is that we obtain improved learning using fewer data points. Reusing the existing knowledge base also results in a more reliable and robust model. The different applications leverage...

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