As the CPU computing power required for Bitcoin Generation due to the "proof of work"concept is totally wasted (and ultimately converted to CO2), I devised a method to save this computational power and use it in the form of trained AI experts (neural network instances), the difficulty variation of the puzzle in Bitcoin is repaced with the variable Kolmogorov Complexity of any of the training instances of the AI experts, and this instance is evaluated by the client for the "proof of work" generation, and to distributedly train the experts using distributed power.
You can spend a lot of GPU power to train a AI expert (several instances, several iterations), but to use the trained version, you only use the best neural network generated and little CPU resources so the advanced neuroevolution techniques are out of reach for the Arduino Uno or Raspberry Pi.
A novel modified fractal turing machine is used to comunicate neuroevolution commands and signals to field machines and internet of things minimizing transmision cost of the experts to the final user and to replicate, modify and use the best trained AI expert available in the network at low CPU cost.
This resourse is invaluable for research as interconectable, configurable connection-based parametrization inside a taxonómical distribution of AI experts can deliver advanced image analisys and data minning thad due to lack of computing power are out of the reach for some people, aditionally the modular, interconectable, nature of neural networks make possible the creation of very complex experts and the usage of the final trained expert in a prototype or mobil device.
The name comes from the fact that as all the AI experts in the taxonomy reach efficiences superior the the human counterparts by definition the machine is in average more "Inteligent" than one human, and this concept is called Technological Singularity, and i think if done correctly will bastly improve all aspects of life.
Comments, sugestions, help welcome.