The emerging AI Revolution is transforming every data-rich human endeavor and discipline. This spectacular image of the Carina Nebula by NASA’s James Web Space Telescope is from data captured from light that could have travelled for 13.6 B years. The JWST dataset grows at 86 terabytes per year. It is dwarfed by ten larger astrophysical datasets, the largest captures in 2023 by the Dark Energy Place Survey of the Milky Way and contains 3.32 billion celestial objects. The scope, scale, complexity, and power of these massive dataset and massive AI-based computational methods are enabling astrophysicists, worldwide, to discover properties of astrophysical phenomena not possible before AI-based data science, thus transforming the discipline of astrophysics. Every data-rich discipline is being similarly transformed. Yet AI-based data science that is at the heart of the AI Revolution is inscrutable.
My research provides a framework with which the data science community can understand, define, and develop AI-based data science as a field of inquiry. The framework is based on the classical reference framework (axiology, ontology, epistemology, methodology) used for 200 years to define knowledge discovery paradigms and disciplines in the humanities, sciences, algorithms, and now data science. Augmented for automated problem-solving with (methods, technology, community), it is used to define the data science knowledge discovery paradigm in terms of the philosophy of data science, the data science problem-solving paradigm (the data science method), and its data science problem-solving workflow.
AI-based data science is inherently multidisciplinary. I welcome enquiries and collaboration.
