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)

The challenges of combining neurons and symbols

So far, we have discussed the inner dynamics of NSAI and how this new class of computing promises to take machine intelligence to the next level. There are, however, a few obstacles that we need to overcome when designing an NSAI system. In this section, we will delve into the most prominent challenges and limitations of NSAI.

Knowledge and symbolic representation

Perhaps one of the more prominent obstacles to face when designing NSAI systems is knowledge and symbol representation. As part of the symbolic component, we have two main challenges:

  • Robustly decomposing our problem space into symbols
  • Representing the extracted symbols in a way that a computer (more specifically, an NN) can understand

In the previous chapters, we discussed how existing NSAI algorithms mostly rely on a domain-specific language (DSL) to represent the symbols and their respective relations as first-order logic. The DSL acts as a descriptor...