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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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Looking at fringe AI research

Fringe AI research refers to all research that is actively being conducted in labs. It’s still not mainstream but a lot of work and effort is being spent on solving some of the most challenging and impactful problems in various domains, such as healthcare, transportation, education, and security. Some of the goals of such research include the following:

  • Developing more explainable and trustworthy AI systems that can provide transparent and reliable reasoning for their decisions and actions
  • Leveraging multimodal data and cross-domain knowledge to enable AI systems to understand complex situations and contexts better
  • Integrating human-AI collaboration and feedback to enhance the performance and usability of AI systems for different users and tasks
  • Exploring new paradigms and architectures for AI, to make it more efficient and effective

In the following sections, we’ll explore the most important topics that will probably...

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