English | ISBN: 3110697807 | 2021 | 340 pages | PDF | 8 MB
The amount of new information is constantly increasing, faster than our ability to fully interpret and utilize it to improve human experiences.
Addressing this asymmetry requires novel and revolutionary scientific methods and effective human and artificial intelligence interfaces.
By lifting the concept of from a positive real number to a 2D complex (kime), this book uncovers a connection between artificial intelligence (AI), data science, and quantum mechanics. It proposes a new mathematical foundation for data science based on raising the 4D space to a higher dimension where loudinal data (e.g., -series) are represented as manifolds (e.g., kime-surfaces). This new framework enables the development of innovative data science analytical methods for model-based and model-free scientific inference, derived computed phenotyping, and statistical forecasting. The book provides a transdisciplinary bridge and a pragmatic mechanism to translate quantum mechanical principles, such as particles and wavefunctions, into data science concepts, such as datum and inference-functions. It includes many that still need to be solved, technological challenges that need to be tackled, and computational statistics algorithms that have to be fully developed and validated.
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