A brain-like computer made of time crystal: Could a metric of prime alone replace a user and alleviate programming forever?

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Abstract

Big data or data overflow problem is due to century-old wrong information theory practiced even today that believes every single event that has happened, is happening, and will happen in the universe could be recreated as sum of simple events. We say, events are connected by geometric shapes, e.g., eight points of a cube represent eight events; to integrate events, either one places new geometric shapes inside any of the eight points, or consider entire cube as a single point located in a new giant geometric shape. Brain has no algorithm running, how does it make decision? Making a decision by building a scientific theory means predicting future from a given set of events; here, we propose to do that by using a pattern of all possible choices a given set of events could be grouped. This pattern is like space–time metric of astrophysics. When we convert any event into a 3D topological shape changing with time, those shapes are fed into this pattern of event-groups namely, phase prime metric. The output is another changing shape that links all possible unknown patterns that could happen in future; so, without any algorithm we can analyze big data and run a robot. We reject Turing’s machine. For frequency fractal computing using time crystal, information is not “bit,” but a unique time crystal, which is life-like. These crystals holding geometric shape self-assemble to create a clocking topology of phase. To convert information into a 3D geometric structure it follows our phase prime metric. Phase structure changes continuously, sometimes it is manifested as dynamics of mass, spin foam, string, tube etc. Thus, time crystal based brain-like computer of makes a decision using phase prime metric.

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Reddy, S., Sonker, D., Singh, P., Saxena, K., Singh, S., Chhajed, R., … Bandyopadhyay, A. (2018). A brain-like computer made of time crystal: Could a metric of prime alone replace a user and alleviate programming forever? In Studies in Computational Intelligence (Vol. 761, pp. 1–43). Springer Verlag. https://doi.org/10.1007/978-981-10-8049-4_1

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