  Creating efficient deep neural networks involves repetitive manual
optimization of the topology and the hyperparameters. This human intervention
significantly inhibits the process. Recent publications propose various Neural
Architecture Search (NAS) algorithms that automate this work. We have applied a
customized NAS algorithm with network morphism and Bayesian optimization to the
problem of cryptocurrency predictions, where it achieved results on par with
our best manually designed models. This is consistent with the findings of
other teams, while several known experiments suggest that given enough
computing power, NAS algorithms can surpass state-of-the-art neural network
models designed by humans. In this paper, we propose a blockchain network
protocol that incentivises independent computing nodes to run NAS algorithms
and compete in finding better neural network models for a particular task. If
implemented, such network can be an autonomous and self-improving source of
machine learning models, significantly boosting and democratizing the access to
AI capabilities for many industries.

Machine Learning;Cryptography and Security;Distributed, Parallel, and Cluster Computing;Machine Learning