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Creating efficient deep neural networks involves repetitive manualoptimization of the topology and the hyperparameters. This human interventionsignificantly inhibits the process. Recent publications propose various NeuralArchitecture Search (NAS) algorithms that automate this work. We have applied acustomized NAS algorithm with network morphism and Bayesian optimization to theproblem of cryptocurrency predictions, where it achieved results on par withour best manually designed models. This is consistent with the findings ofother teams, while several known experiments suggest that given enoughcomputing power, NAS algorithms can surpass state-of-the-art neural networkmodels designed by humans. In this paper, we propose a blockchain networkprotocol that incentivises independent computing nodes to run NAS algorithmsand compete in finding better neural network models for a particular task. Ifimplemented, such network can be an autonomous and self-improving source ofmachine learning models, significantly boosting and democratizing the access toAI capabilities for many industries.

Distributed creation of Machine learning agents for Blockchain analysis