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
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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).
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- Chapter
- Information
- Knowledge-Infused LearningNeurosymbolic AI for Explainability, Interpretability, and Safety, pp. 1 - 10Publisher: Cambridge University PressPrint publication year: 2026
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