Difference between revisions of "Meta University/Context"
| Line 1: | Line 1: | ||
As [[Moore's Law]]<ref name="Cramming">{{:Paper/Cramming more components onto integrated circuits}}</ref> shifted the paradigm of data processing by claiming that data processing capabilities will grow at an exponential pace. This exponential growth assumption has already transformed economic activities around the world, and most importantly it must be assimilated into knowledge dissemination strategy. | As [[Moore's Law]]<ref name="Cramming">{{:Paper/Cramming more components onto integrated circuits}}</ref> shifted the paradigm of data processing by claiming that data processing capabilities will grow at an exponential pace. This exponential growth assumption has already transformed economic activities around the world, and most importantly it must be assimilated into knowledge dissemination strategy. The essential thesis of [[Moore's Law]] is that it articulated the causal relations between data processing capabilities with an observable physical metrics of data processing instrumentations. This causal revelation of assigning data processing capability with physical scales may provide an testable thesis to position [[Data Science]] as the foundation of modern knowledge management approaches. Based on public contribution, it will evolve to incrementally approximate a curriculum to help anyone who wish to benefit from [[intuitive data presentation]] on pervasive computing devices, and [[highly-available]] data content. | ||
<noinclude> | <noinclude> | ||
= Moore's Law as the central thesis = | = Moore's Law as the central thesis = | ||
Revision as of 12:38, 12 February 2022
As Moore's Law[1] shifted the paradigm of data processing by claiming that data processing capabilities will grow at an exponential pace. This exponential growth assumption has already transformed economic activities around the world, and most importantly it must be assimilated into knowledge dissemination strategy. The essential thesis of Moore's Law is that it articulated the causal relations between data processing capabilities with an observable physical metrics of data processing instrumentations. This causal revelation of assigning data processing capability with physical scales may provide an testable thesis to position Data Science as the foundation of modern knowledge management approaches. Based on public contribution, it will evolve to incrementally approximate a curriculum to help anyone who wish to benefit from intuitive data presentation on pervasive computing devices, and highly-available data content.
Moore's Law as the central thesis
Moore's Law is instrumental for shifting the paradigms of institutional and individual learning, because it provides an testable theory to relate the spatial and temporal dimensions of computational/decision-making activities. Before Moore's Law, computational tasks are considered to be a pure mental exercise for humans, the mechanical devices for computation are usually clumsy and rigid, so that they can hardly display any kind of intelligence. Given Moore's Law's revelation, the speed and capabilities of computation, can be extrapolated to various possibilities, therefore, it creates a societal rhythm in planning on how to cram[1] more decision-making, or accountable activities in certain unit spacetime and energy. This revelation creates a tangible way to plan for technology and business process deployment. Therefore, we must be reminded of this intellectual revelation for planning MU. However, it is important to note that Moore's Law has been tighly associated with the idea of exponential growth, while the precise ratio of exponential growth may change from industry to industry.
References
- ↑ 1.0 1.1 Gordon, Moore E. (Apr 19, 1965). Cramming more components onto integrated circuits (PDF). local page: Electronics Magazine.