Neuro-Symbolic AI

Neuro-Symbolic AI

by Alexiei Dingli, David Farrugia
Released May 2023
Publisher(s): Packt Publishing
ISBN: 9781804617625

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Book description

Explore the inner workings of AI along with its limitations and future developments and create your first transparent and trustworthy neuro-symbolic AI system

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

  • 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

Book 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.

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

Who this book is 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.

Table of contents

  1. Neuro-Symbolic AI
  2. Contributors
  3. About the authors
  4. About the reviewers
  5. Preface
    1. Who this book is for
    2. What this book covers
    3. To get the most out of this book
    4. Download the example code files
    5. Download the color images
    6. Conventions used
    7. Get in touch
    8. Share Your Thoughts
    9. Download a free PDF copy of this book
  6. Chapter 1: The Evolution and Pitfalls of AI
    1. The basic idea behind AI
    2. The evolution of AI
      1. Philosophy
      2. Logic
      3. Mathematics
      4. Cognitive science
      5. A short history of AI
    3. Subfields of AI
      1. ML
      2. Computer vision
      3. Natural language processing
      4. Robotics
      5. Knowledge representation
      6. Problem-solving and reasoning
      7. Planning
      8. Evolutionary computing
    4. The pitfalls of AI
      1. Is AI limitless?
      2. How important is the data?
      3. Can we get training data?
      4. Have we got good data?
      5. Can a high-performance AI still fail?
    5. Summary
  7. Chapter 2: The Rise and Fall of Symbolic AI
    1. Defining Symbolic AI
      1. Humans, symbols, and signs
    2. Enabling machine intelligence through symbols
      1. The concept of intelligence
      2. Towards Symbolic AI
      3. From symbols and relations to logic rules
    3. The fall of Symbolic AI
    4. Symbolic AI today
      1. Expert systems
      2. Natural language processing
      3. Constraint satisfaction
      4. Explainable AI
    5. The sub-symbolic paradigm
    6. Summary
    7. Further reading
  8. Chapter 3: The Neural Networks Revolution
    1. Artificial neural networks modeling the human brain
      1. A simple artificial neural network
    2. Introducing popular neural network architectures
      1. Recurrent neural networks
      2. Competitive networks
      3. Hopfield networks
    3. Delving into deep neural networks
      1. Convolutional neural networks
      2. Long short-term memory networks
      3. Autoencoders
      4. Deep belief networks
      5. Generative networks
      6. Transformers
    4. The rise of data
    5. The complexities and limitations of neural networks
    6. Summary
  9. Chapter 4: The Need for Explainable AI
    1. What is XAI?
    2. Why do we need XAI?
      1. XAI case studies
    3. The state-of-the-art models in XAI
      1. Accumulated Local Effects
      2. Anchors
      3. Contrastive Explanation Method
      4. Counterfactual instances
      5. Explainable Boosting Machine
      6. Global Interpretation via Recursive Partitioning
      7. Integrated gradients
      8. Local interpretable model-agnostic explanations
      9. Morris Sensitivity Analysis
      10. Partial dependence plot
      11. Permutation importance
      12. Protodash
      13. SHapley Additive exPlanations
    4. Summary
  10. Chapter 5: Introducing Neuro-Symbolic AI – the Next Level of AI
    1. The idea behind NSAI
      1. Modeling human intelligence – insights from child psychology
    2. The ingredients of an NSAI system
      1. The symbolic ingredient
      2. The neural ingredient
      3. The neuro-symbolic blend
    3. Exploring different architectures of NSAI
      1. Neuro-Symbolic Concept Learner
      2. Neuro-symbolic dynamic reasoning
      3. Dissecting the NLM architecture
    4. Summary
    5. Further reading
  11. Chapter 6: A Marriage of Neurons and Symbols – Opportunities and Obstacles
    1. The benefits of combining neurons and symbols
      1. Data efficiency
      2. High accuracy
      3. Transparency and interpretability
    2. The challenges of combining neurons and symbols
      1. Knowledge and symbolic representation
      2. Multi-source knowledge reasoning
      3. Dynamic reasoning
      4. Query understanding for knowledge reasoning
    3. Research gaps in neuro-symbolic computing
    4. Summary
  12. Chapter 7: Applications of Neuro-Symbolic AI
    1. Application 1 – health – computational drug repurposing
      1. Application details
      2. Problem statement
      3. The role of NSAI
    2. Application 2 – education – student strategy prediction
      1. Application details
      2. Problem statement
      3. The role of NSAI
    3. Application 3 – finance – bank loan risk assessment
      1. Application details
      2. Problem statement
      3. The role of NSAI
    4. Summary
    5. Further reading
  13. Chapter 8: Neuro-Symbolic Programming in Python
    1. Environment and data setup
    2. Solution 1 – logic tensor networks
      1. Loading the dataset
      2. Modifying the dataset
      3. Creating train and test datasets
      4. Defining our knowledge base and NN architecture
      5. Defining our predicate, connectives, and quantifiers
      6. Setting up evaluation parameters
      7. Training the LTN model
      8. Analyzing the results
    3. Solution 2 – prediction stacking
      1. Experiment setup and loading the data
      2. Data preparation
      3. Training our NSAI model
      4. Analyzing the results
      5. Prediction interpretability and logic tracing
    4. Summary
    5. Further reading
  14. Chapter 9: The Future of AI
    1. Looking at fringe AI research
      1. Small data
      2. Novel network architectures
      3. New ways of learning
      4. Evolution of attention mechanisms
      5. World model
      6. Hybrid models
    2. Exploring future AI developments
      1. Quantum computing
      2. Neuromorphic engineering
      3. Brain-computer interaction
    3. Bracing for the rise of AGI
    4. Preparing for singularity
      1. Popular media
      2. Exploring the expert views
      3. Singularity challenges
    5. Summary
    6. Further reading
  15. Index
    1. Why subscribe?
  16. Other Books You May Enjoy
    1. Packt is searching for authors like you
    2. Share Your Thoughts
    3. Download a free PDF copy of this book

Product information

  • Title: Neuro-Symbolic AI
  • Author(s): Alexiei Dingli, David Farrugia
  • Release date: May 2023
  • Publisher(s): Packt Publishing
  • ISBN: 9781804617625