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 rise of data

Data refers to the raw facts and figures that are collected and processed to provide useful information. In this context, data is essential for training models to recognize patterns and make predictions. The rise of data has been closely linked with the growth of the internet. As more people have come online and generated more information, the amount of data available has grown exponentially. This has had a profound impact on deep learning, enabling researchers to develop increasingly sophisticated models that can learn from vast amounts of data. As more people came online, companies began to realize the value of this data. They started collecting it en masse, using it to gain insights into customer behavior and preferences. This led to the emergence of big data – large datasets that were too complex to be processed using traditional methods. Big data presented a challenge for machine learning researchers. Traditional machine learning algorithms were not designed...