Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Knowledge Graphs for Explainability and Interpretability
- 3 Knowledge-Infused Learning: The Subsumer to NeuroSymbolic AI
- 4 Shallow Infusion of Knowledge
- 5 Semi-deep Infusion Learning
- 6 Deep Knowledge-Infused Learning
- 7 Process Knowledge-Infused Learning
- 8 Knowledge-Infused Conversational NLP
- 9 Neurosymbolic Large Language Models
- References
- Index
1 - Introduction
Published online by Cambridge University Press: 22 April 2026
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Knowledge Graphs for Explainability and Interpretability
- 3 Knowledge-Infused Learning: The Subsumer to NeuroSymbolic AI
- 4 Shallow Infusion of Knowledge
- 5 Semi-deep Infusion Learning
- 6 Deep Knowledge-Infused Learning
- 7 Process Knowledge-Infused Learning
- 8 Knowledge-Infused Conversational NLP
- 9 Neurosymbolic Large Language Models
- References
- Index
Summary
To create genuinely effective artificial intelligence (AI), we need to build systems that think, explain, and reason like humans. This perspective from Gary Marcus aligns with Andrew Ng’s view that the hype around big data is overblown and that AI must advance in intelligence. Early on in the heyday of machine learning (ML), Pedro Domingos (2012) observed that “Data alone is not enough.” These experts agree that merely scaling up AI models to billions of parameters has led to fundamental challenges such as “hallucinations” (Thoppilan et al., 2022a).
Information
- Type
- Chapter
- Information
- Knowledge-Infused LearningNeurosymbolic AI for Explainability, Interpretability, and Safety, pp. 1 - 10Publisher: Cambridge University PressPrint publication year: 2026
Accessibility standard: WCAG 2.1 A
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The PDF of this chapter
complies with version 2.1 of the
Web Content Accessibility Guidelines
(WCAG), covering newer accessibility requirements and improved
user experiences and meets the basic
(A)
level of WCAG compliance, addressing essential accessibility barriers.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.