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)

Research gaps in neuro-symbolic computing

Although the field of NSAI has grown significantly in research interest and popularity, it is still maturing. NSAI is proving to be rather promising and is seeing major financial investment from strong research and technical institutions such as MIT and IBM. In this section, we will briefly highlight the future NSAI research direction and study avenues of interest:

  • Standardized datasets and comprehensibility tests for benchmarking:

Like the existing CLEVR and CLEVRER datasets, the domain of NSAI would benefit from common datasets that can be used to benchmark existing and new techniques. These standardized resources are critical for comparative research.

  • Strategies for NN inference using symbolic propositions.
  • More comparative evaluations on the NSAI paradigm to determine the best NN architecture to use.
  • Context-aware semantics:

Further investigation on how semantics can be constructed and influenced by...