Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- 11 Toward Certifiably Trustworthy Deep Learning at Scale
- 12 Metacognition with Neural Network Verification and Repair Using Veritex
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
11 - Toward Certifiably Trustworthy Deep Learning at Scale
from Part VI - Assured Machine Learning in High-Stakes Domains
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Book contents
- Frontmatter
- Contents
- Contributors
- Acknowledgments
- Part I Introduction
- Part II Taxonomy of Metacognitive Approaches
- Part III Neuro-Symbolic Models in AI
- Part IV Metacognition with LLMS
- Part V Metacognition in Learning Agents
- Part VI Assured Machine Learning in High-Stakes Domains
- 11 Toward Certifiably Trustworthy Deep Learning at Scale
- 12 Metacognition with Neural Network Verification and Repair Using Veritex
- Part VII Metacognition as a Solution to Handle Failure
- Part VIII Applications of Metacognitive AI
Summary
Metacognitive AI is closely connected to certifiable AI and trustworthy AI, the two areas focusing on equipping AI with trustworthy guarantees in high-stake domains. This chapter provides a systematic overview, tutorial, and discussion of the certified approaches in trustworthy deep learning. The chapter introduces essential terminologies, core methodologies, and representative applications of certified approaches. We believe that certified approaches, as a prerequisite for deploying AI in high-stake and safety-critical applications, would be an essential tool in metacognitive AI, and we hope that this chapter can inspire readers to further advance the field of certifiable trustworthiness for metacognitive AI.
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- Information
- Metacognitive Artificial Intelligence , pp. 185 - 211Publisher: Cambridge University PressPrint publication year: 2025
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