ISBN: 9780262328661 | 504 pp. | June 2015

Intelligence Emerging

Adaptivity and Search in Evolving Neural Systems
Overview

Emergence—the formation of global patterns from solely local interactions—is a frequent and fascinating theme in the scientific literature both popular and academic. In this book, Keith Downing undertakes a systematic investigation of the widespread (if often vague) claim that intelligence is an emergent phenomenon. Downing focuses on neural networks, both natural and artificial, and how their adaptability in three time frames—phylogenetic (evolutionary), ontogenetic (developmental), and epigenetic (lifetime learning)—underlie the emergence of cognition. Integrating the perspectives of evolutionary biology, neuroscience, and artificial intelligence, Downing provides a series of concrete examples of neurocognitive emergence. Doing so, he offers a new motivation for the expanded use of bio-inspired concepts in artificial intelligence (AI), in the subfield known as Bio-AI.

One of Downing’s central claims is that two key concepts from traditional AI, search and representation, are key to understanding emergent intelligence as well. He first offers introductory chapters on five core concepts: emergent phenomena, formal search processes, representational issues in Bio-AI, artificial neural networks (ANNs), and evolutionary algorithms (EAs). Intermediate chapters delve deeper into search, representation, and emergence in ANNs, EAs, and evolving brains. Finally, advanced chapters on evolving artificial neural networks and information-theoretic approaches to assessing emergence in neural systems synthesize earlier topics to provide some perspective, predictions, and pointers for the future of Bio-AI.

Table of Contents

  1. Preface
  2. Acknowledgments
  3. 1. Introduction
  4. 2. Emergence
  5. 3. Search: The Core of AI
  6. 4. Representations for Search and Emergence
  7. 5. Evolutionary Algorithms
  8. 6. Artificial Neural Networks
  9. 7. Knowledge Representation in Neural Networks
  10. 8. Search and Representation in Evolutionary Algorithms
  11. 9. Evolution and Development of the Brain
  12. 10. Learning via Synaptic Tuning
  13. 11. Trial-and-Error Learning in Neural Networks
  14. 12. Evolving Artificial Neural Networks
  15. 13. Recognizing Emergent Intelligence
  16. 14. Conclusion
  17. Appendix A: Evolutionary Algorithm Sidelights
  18. Appendix B: Neural Network Sidelights
  19. Notes
  20. References
  21. Index