Processing Information in the Clouds†
1
Dept. of Math., University of California Los Angeles, Los Angeles, CA 90095, USA
2
Dept. of Eng. and Sci., Rensselaer Polytechnic Institute, Hartford, CT 01609, USA
3
School of Business, Golden Gate University, San Francisco, CA 94105, USA
*
Author to whom correspondence should be addressed.
†
Conference Morphological, Natural, Analog and Other Unconventional Forms of Computing for Cognition and Intelligence (MORCOM), Berkeley, CA, USA, 2–6 June 2019.
Proceedings 2020, 47(1), 25; https://doi.org/10.3390/proceedings47010025
Published: 7 May 2020
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.
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MDPI and ACS Style
Burgin, M.; Eberbach, E.; Mikkilineni, R. Processing Information in the Clouds. Proceedings 2020, 47, 25.
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