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Neuro-Symbolic AI

Neuro-Symbolic AI

By : Alexiei Dingli, David Farrugia
3.7 (6)
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Neuro-Symbolic AI

Neuro-Symbolic AI

3.7 (6)
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)
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The ingredients of an NSAI system

With AI gaining more traction, in August of 2019, strong research efforts began to enable common-sense and reasoning abilities in AI systems by reverse engineering the brain of human babies. As the name implies, the recipe of neuro-symbolic programming involves two main ingredients: NNs and symbolic programming. We will explore these two ingredients using the Compositional Language and Elementary Visual Reasoning (CLEVR) example case. CLEVR is a dataset of 100,000 computer-generated scenes portraying 3D shapes (https://cs.stanford.edu/people/jcjohns/clevr/). The objective of this dataset is for AI to reason about these images and be able to answer questions regarding the said images—for example: How many spheres are in the image?

The symbolic ingredient

Motivated by their observations, the researchers highlighted one key aspect of the reasoning abilities of humans (and other organisms, for that matter): world knowledge. We can reason about...

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