Abstract
Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.
Highlights
When Eugene Wigner discussed the role of mathematics in physical theories, he emphasized the unreasonable effectiveness of mathematics in the natural sciences
They often permit an unexpectedly close and accurate description of the phenomena in these connections. Just because of this circumstance, and because we do not understand the reasons of their usefulness, we cannot know whether a theory formulated in terms of mathematical concepts is uniquely appropriate.” [1]
We examine a few new theoretical insights based on advanced mathematical theories, and their application to understanding the information processing structures in cloud computing
Summary
When Eugene Wigner discussed the role of mathematics in physical theories, he emphasized the unreasonable effectiveness of mathematics in the natural sciences. We examine a few new theoretical insights based on advanced mathematical theories, and their application to understanding the information processing structures in cloud computing. We discuss the ability of the cloud automata model [2] to harness the power of distributed computing in clouds These results are new and original, and by stressing the emergence of cloud computing as a new computational paradigm, it is possible to use cloud computing for problems that either cannot be solved at all or not to be solved effectively by traditional computers. We use the Oracle AI agents as hierarchical cognizing agents These assist in modeling, monitoring and executing computational structures that process information, and manage their evolution even in the face of non-deterministic fluctuations in the availability or demand of resource
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