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)

Subfields of AI

Now that we’ve explored the origins of AI and understood how it evolved over the years, let’s look at the different subfields that constitute this vast field of study. At this stage, it’s important to note that there isn’t a single subdivision acceptable to everyone. There might be other ways of dividing these areas, and some might also overlap. However, this is an attempt at logically organizing the subfields of AI.

Figure 1.2 – Subfields of AI

Figure 1.2 – Subfields of AI

ML

ML tries to create algorithms capable of learning. It typically starts by using data found in a training set and then generates predictions out of that data. DL is one of the various subfields of ML, which tries to create learning algorithms while gaining inspiration from the brain’s inner workings. Today, this subfield is considered the superstar of AI, and in fact, it is used in almost all the other subfields. ML has various applications such as...