Book Image

Neuro-Symbolic AI

By : Alexiei Dingli, David Farrugia
Book Image

Neuro-Symbolic AI

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)

Summary

NSAI is the new kid on the block in the field of AI. NSAI is considered by many as the next level of AI, or the new AI revolution, mainly due to its ability to equip machines with human-like reasoning capabilities.

This chapter started by venturing through human psychology’s vast and exciting realm. We briefly explored the human reasoning process and discussed how humans and other beings develop the ability to understand and reason about physical world objects early on. Common-sense knowledge seems to be an innate skill that humans possess—or at least one that we can learn autonomously to a certain degree. We then dissected the hybrid NSAI model into its constituent parents: Symbolic AI and NNs. NNs seem to have granted Symbolic AI a second chance to prove itself as a worthy contender for achieving the original motivation behind AI systems—reaching human levels of reasoning intelligence in computers. Further, we also discussed the NSCL, NSDR, and NLM...