Machine Learning: Origins, Developments and Implications
First published:
ISBN: 978-1-78272-445-2
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Final thoughts
Final thoughts
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
Some have argued that the world is worse off for the emergence of artificial intelligence (AI) and machine learning. But is that true? Advances in human science and technology, notwithstanding the occasional disaster, have enabled over 8 billion humans to populate the Earth. World War II taught us that nuclear devices can end human existence, but advancements in science and technology in the 70 years since the use of automation in the NC3 system was first introduced have taken place despite the possibility of nuclear destruction. The paradigm of world peace has failed us and AI is not at fault.11 My generation is the first that has never known a time devoid of nuclear weapons.
In addition to war, human existence is threatened by climate change, increasing wealth disparity, overpopulation, distrust of governments and institutions (see Figure 19.1), uncertain news credibility and a documented global increase in anxiety. Sheppard (2020) foresees disaster unless these problems are solved within the next 10 years. If this prophesy is true, critics of AI should consider how humans might deal with these problems in a world without it.
Despite the potential of AI to advance the economy and
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