Trustless parallel local search for effective distributed algorithm discovery
The protocol introduces publicly verifiable performance evaluation of the local optima reported by each node, creating a competitive environment between the local searches. That is strengthened with economical stimuli for producing good solutions, that provide coordination between the nodes, as every node tries to explore different sections of the search space to beat their competition.
06 FEB 2019
Distributed creation of Machine learning agents for Blockchain analysis
We propose a blockchain network protocol that incentivizes independent computing nodes to run NAS algorithms and compete in finding better neural network models for a particular task.
If implemented, such a network can be an autonomous and self-improving source of machine learning models, significantly boosting and democratizing access to AI capabilities for many industries.
15 OCT 2018
Predicting the digital asset market based on blockchain activity data
We explore whether analyzing blockchain data with modern Deep Leaning techniques results in higher accuracies than the standard approach.
During a series of experiments on the Ethereum blockchain, we achieved a four-time error reduction with blockchain data than an LSTM approach with trade volume data.