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Neuro-Symbolic AI
Neuro-Symbolic AI

Neuro-Symbolic AI: Design transparent and trustworthy systems that understand the world as you do

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Profile Icon Alexiei Dingli Profile Icon Farrugia
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R$80 R$178.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (6 Ratings)
eBook May 2023 196 pages 1st Edition
eBook
R$80 R$178.99
Paperback
R$222.99
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Renews at R$50p/m
Arrow left icon
Profile Icon Alexiei Dingli Profile Icon Farrugia
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R$80 R$178.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (6 Ratings)
eBook May 2023 196 pages 1st Edition
eBook
R$80 R$178.99
Paperback
R$222.99
Subscription
Free Trial
Renews at R$50p/m
eBook
R$80 R$178.99
Paperback
R$222.99
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Free Trial
Renews at R$50p/m

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Neuro-Symbolic AI

The Rise and Fall of Symbolic AI

The Second World War saw massive scientific contributions and technological advancements. Innovations such as radar technology, the mass production of penicillin, and the jet engine were all a by-product of the war. More importantly, the first electronic computer (Colossus) was also developed to decipher encrypted Nazi communications during the war. After the war, the desire to achieve machine intelligence continued to grow.

As humans, we wanted to enable computers with human-like abilities. The primary motivation behind Artificial Intelligence (AI) systems has always been to allow computers to mimic our behavior, to enable machines to think like us and act like us, to be like us. Over the years, this motivation has remained virtually unchanged. However, the methodology and the mindset of how we approach AI has gone through several phases throughout the years.

While today, we rely on deep Neural Networks (NNs) (also known as Connectionism AI) to allow a machine to automatically teach itself how to solve a particular task, early AI systems were heavily ruled, and logic-based methods were used to teach computers human behavior. These systems are referred to as Symbolic AI. Symbolic AI is one of the earliest forms of AI. In his 1985 book Artificial Intelligence: The Very Idea, John Haugeland coined Good Old-Fashioned AI (GOFAI) as a reference to Symbolic AI. It dominated the computer science and AI fields from the 1950s all the way through to the 1970s. One of the first successful implementations of Symbolic AI dates to 1951, when Christopher Strachey, a British computer scientist, wrote over 2,000 instructions to teach a computer how to play the game of checkers (draughts). Today, Symbolic AI has been leveraged to solve many problems, including Natural Language Processing (NLP) applications and Expert System (ES) implementations.

This chapter aims to understand the underlying mechanics of Symbolic AI, its key features, and its relevance to the next generation of AI systems.

The first objective of this chapter is to discuss the concept of Symbolic AI and provide a brief overview of its features. Symbolic AI is heavily influenced by human interaction and knowledge representation. We will discuss how this shaped Symbolic AI. We will then examine the key features of Symbolic AI, which allowed it to dominate the field during its time. After that, we will cover various paradigms of Symbolic AI and discuss some real-life use cases based on Symbolic AI. We will finally discuss the main challenges when developing Symbolic AI systems and understand their significant pitfalls.

This chapter will go through the following topics:

  • Defining Symbolic AI
  • Knowledge representation through symbols and signs
  • Critical features of Symbolic AI
  • The different paradigms and applications of Symbolic AI
  • Limitations and pitfalls of Symbolic AI

Let us get started with defining what we mean by Symbolic AI.

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Key benefits

  • Understand symbolic and statistical techniques through examples and detailed explanations
  • Explore the potential of neuro-symbolic AI for future developments using case studies
  • Discover the benefits of combining symbolic AI with modern neural networks to build transparent and high-performance AI solutions

Description

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.

Who is this book for?

This book is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to explore the emerging field of neuro-symbolic AI and discover how to build transparent and trustworthy AI solutions. A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.

What you will learn

  • Gain an understanding of the intuition behind neuro-symbolic AI
  • Determine the correct uses that can benefit from neuro-symbolic AI
  • Differentiate between types of explainable AI techniques
  • Think about, design, and implement neuro-symbolic AI solutions
  • Create and fine-tune your first neuro-symbolic AI system
  • Explore the advantages of fusing symbolic AI with modern neural networks in neuro-symbolic AI systems

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 31, 2023
Length: 196 pages
Edition : 1st
Language : English
ISBN-13 : 9781804616956
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Product Details

Publication date : May 31, 2023
Length: 196 pages
Edition : 1st
Language : English
ISBN-13 : 9781804616956
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Table of Contents

11 Chapters
Chapter 1: The Evolution and Pitfalls of AI Chevron down icon Chevron up icon
Chapter 2: The Rise and Fall of Symbolic AI Chevron down icon Chevron up icon
Chapter 3: The Neural Networks Revolution Chevron down icon Chevron up icon
Chapter 4: The Need for Explainable AI Chevron down icon Chevron up icon
Chapter 5: Introducing Neuro-Symbolic AI – the Next Level of AI Chevron down icon Chevron up icon
Chapter 6: A Marriage of Neurons and Symbols – Opportunities and Obstacles Chevron down icon Chevron up icon
Chapter 7: Applications of Neuro-Symbolic AI Chevron down icon Chevron up icon
Chapter 8: Neuro-Symbolic Programming in Python Chevron down icon Chevron up icon
Chapter 9: The Future of AI Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(6 Ratings)
5 star 33.3%
4 star 33.3%
3 star 16.7%
2 star 0%
1 star 16.7%
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Om S Jun 28, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book insightful exploration of neuro-symbolic AI, showcasing the potential of combining symbolic and neural network approaches to create transparent and high-performance AI solutions. With detailed explanations and practical examples, the book equips data scientists, machine learning engineers, and AI enthusiasts with the knowledge to design and implement neuro-symbolic AI solutions. It emphasizes the importance of trust and transparency in AI, offering insights into explainable AI techniques. From understanding the intuition behind neuro-symbolic AI to creating and fine-tuning systems, this book covers a wide range of topics. By fusing symbolic AI with modern neural networks, it opens doors to a new level of AI capabilities.
Amazon Verified review Amazon
Steven Fernandes Aug 11, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Diving into the intricate intersection of neural networks and symbolic AI, this book meticulously unravels the intuition behind neuro-symbolic AI. It guides readers through its potential applications, helping them discern its ideal uses. The author meticulously delineates the various strands of explainable AI, setting a clear foundation for enthusiasts and practitioners alike. Readers are not only invited to reflect upon neuro-symbolic AI designs but are also equipped with hands-on guidance to implement and fine-tune their own systems. A standout highlight is the book's exploration of the synergistic advantages when fusing symbolic AI with contemporary neural networks. A comprehensive primer for anyone looking to delve deep into the future of AI.
Amazon Verified review Amazon
Matthew Emerick Jun 08, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
I was very excited to see this book come out and I picked it up as quickly as I could. There are very few books about neuro-symbolic AI and I was hoping that this could be a leader in the field that brings a new wave of AI developers and applications. After reading it, I'm a little less certain.This book is aimed at "data scientists, ML engineers, and AI enthusiasts who are looking to expandtheir knowledge of neuro-symbolic AI and stay up to date with the latest advancements in the field." It then spends the first hundred pages, the first half of the book, talking about the history of AI and where neuro-symbolic AI fits in. Yes, it's needed, but is it worth spending just over half the book on it?This is where it starts to get more interesting, though it starts reading a bit like a research paper. There is definitely a difference between the first and second halves of the book. It looks at a single paradigm for neuro-symbolic AI instead of comparing and contrasting a few different prominent models.Chapter 8 gets to the only hands-on portion using Python and Pytorch. You will want a decent knowledge of both to work through the examples and understand them. A general knowledge of programming and machine learning might be enough as well.After this chapter, the book dives into a lot of speculation. There is some welcome information on non-mainstream research that may or may not yield useful results. It also goes into the singularity, a very hypothetical event where AI blows past our capabilities and something either very good or very bad happens. The book lists some fiction and non-fiction books as well as movies, TV series, and video games that talk about the singularity. Then it gives a "balanced" discussion about whether the singularity is good or bad, instead of if it could even occur. This was a very odd section.Overall, this book does add to the discussion on neuro-symbolic AI, but in a haphazard way. I would give it 3.5 stars, but rounded it up hoping it will lead to other books on the subject coming out. If you're interested in this topic, it might be worth a read. If you know the basics, you'll probably have to go to research papers to learn more.
Amazon Verified review Amazon
Pavel Jul 07, 2023
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
An approachable intro to Neuro-Symbolic AI. However, it includes excessive information not directly related to neuro-symbolic AI. If you are a newcomer to the field and/or want a refresher on AI, its history, and a slow and gradual build-up to neuro-symbolic AI, this is a great start. If you directly want to jump into neuro-symbolic, prepare to skip 1/2 of the book.Personally enjoyed the book, but wished the material had more mathematical rigor.
Amazon Verified review Amazon
A. Fraser Jul 06, 2023
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
If you are buying this book, you should be aware that it is very much academic and conceptual, and spends a lot of time discussing history, background context and what has led to NSAI. It does touch on two implementations in 15 pages at the back of the book, one using Logic Tensor Networks and one using Decision Trees.I would have rated the book 4 stars based on the content, but it (my copy anyway) is also let down by the print quality. A good proportion (maybe 50%) of the diagrams have been printed in a way that when they have laid down the colours they don't align which creates a lot of blurring. It's not bad enough that I am going to send it back, but enough that it makes me feel slightly seasick when I'm looking at those diagrams. I expect better from a quality publisher like packt.In summary, if you want a broad overview to give you context in this emerging area this is good. If you want deeper implementation level detail or "how to" then look elsewhere.
Amazon Verified review Amazon
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