How to Create Machine Superintelligence: A Quick Journey through Classical/Quantum Computing, Artificial Intelligence, Machine Learning, and Neural Networks
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Language: English
Published by Independently published, 2017
- Softcover
- New

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- Title
- How to Create Machine Superintelligence: A Quick Journey through Classical/Quantum Computing, Artificial Intelligence, Machine Learning, and Neural Networks
- Author
- Kovera, Artem
- Publisher
- Independently published
- Publication year
- 2017
- Condition
- New
- Binding
- Soft cover
- Language
- English
- ISBN 10
- 152103012X
- ISBN 13
- 9781521030127
A new edition of this book is also available on Amazon. Imagine what an astonishing effect could happen if we combined profound human natural creativity with computers' numerical and logical capabilities. Probably, individuals that will use this technology first will immensely benefit from it.
Many impressive achievements have recently emerged in machine learning and narrow AI. For example, modern deep neural networks can recognize and even generate some types of patterns even better than humans can. Some experts think that deep learning is a direct road to artificial general intelligence, but others think differently and propose alternative ideas such as evolutionary computations, cellular automata, probabilistic programming, or quantum computing.
This book will cover many approaches to machine learning and the main classes of artificial neural networks and how these computational approaches can potentially lead to the creation of artificial general intelligence.
Also, you will find a quick explanation of two main methodologies for computation: classical and quantum computing, and how the principles of operation of those can be applied to building artificial intelligence.
You will also learn about the fundamental limitations of computation and about the paradigms of uncertainty and unpredictability of the world. We will see why this is important for understanding the capabilities of natural and artificial intelligence.
Many impressive achievements have recently emerged in machine learning and narrow AI. For example, modern deep neural networks can recognize and even generate some types of patterns even better than humans can. Some experts think that deep learning is a direct road to artificial general intelligence, but others think differently and propose alternative ideas such as evolutionary computations, cellular automata, probabilistic programming, or quantum computing.
This book will cover many approaches to machine learning and the main classes of artificial neural networks and how these computational approaches can potentially lead to the creation of artificial general intelligence.
Also, you will find a quick explanation of two main methodologies for computation: classical and quantum computing, and how the principles of operation of those can be applied to building artificial intelligence.
You will also learn about the fundamental limitations of computation and about the paradigms of uncertainty and unpredictability of the world. We will see why this is important for understanding the capabilities of natural and artificial intelligence.
"Synopsis" may belong to another edition of this title.
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