Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Preface
Preface
Preface
Introduction: human-machine entanglement
Machine learning: origins
Useful tools
Decision trees
Introduction to neural networks
Back-propagation
Regularisation
Optimisation
Building neural networks
Early applications of machine learning
Interpreting neural network decisions
Predicting corporate bond returns
Deep learning networks
Applications of deep learning networks
Machine intelligence
Consciousness
The future and its challenges
Artificial intelligence and the military
Final thoughts
Appendix
Epilogue
Acknowledgements
When I proposed this book to my publisher at Risk Books, it was as a manual for constructing artificial intelligence (AI) systems and as a mechanism for describing some of my applications. Given my experience building neural networks since the 1980s, I felt that I could provide a mathematically rigorous historical treatment of the rise and development of AI. However, two things became clear to me as I began writing the book in late 2022: rather than beginning immediately with a specific technical discussion, I wanted to find a way to introduce AI as a topic in itself; and news articles describing AI applications were appearing daily, along with reports on the emergence of ChatGPT.
From media reports of the performance of AI applications and responses from the public, it became apparent that there is considerable misunderstanding of AI’s advantages and limitations. The emergence of AI will present many new challenges and it is critical that these are addressed from an informed perspective. Accordingly, I decided to write an initial chapter focusing on issues of public concern such as control of machines in the context of the rapid societal changes created by AI. Chapter 1 raises many
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