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Decentralized computing power for Automated Machine Learning

An open-sourced network for self-improving AI models

AutoMLs already outperform the best human researches in creating deep neural networks. But there’s a catch.

To autonomously produce effective AI solutions, AutoMLs need vast amounts of computing power. This makes the technology viable mostly to enterprises with significant access to the required resources, making smaller AI-based ventures dependable on a single service.

We are building efficient access to the resources the modern approach to AI so desperately requires. 

ScyNet is a blockchain protocol and open economic system. It rewards computing nodes for sharing resources in a decentralized network. AI ventures that configure their own AutoML nodes on the system are able to engage with the GPU power to test and improve their models.

The autonomous cycle of improving AI models

ScyNet allows private ventures to setup AutoML nodes and train their models on available trainer nodes on the network.

 

The network is governed by a Zero-Knowledge Proof Protocol, which ensures a trustless method of validation where AutoML nodes remain the effective owner of each model, while at the same time Trainers are able to prove the work they’ve put in improving the model without actually revealing the model to the validating third-party. 

Making use of

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Winner at

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Project by

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Partner of

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The Internet of AI?

A truly decentralized source of collective AI.  

Private AutoMLs usually obfuscate their training jobs so that trainers cannot reutilize the AI knowledge themselves and hence the AutoML holds exclusivity of the final result. An alternative approach that suits parties such as open-source organizations or foundations is public AutoMLs.

 

These participants help jumpstart the ecosystem by revealing the meaning of their data and models, in turn allowing the trainers to reutilize the models for their own purposes.

Progress

2017

2018

Overall conceptualization of the Scynet Project

Gattakka genetic framework released

First experiments with blockchain data analysis

Blockchain analysis paper submitted for peer-review and uploaded to Cornell University's ArXiv database.

2019

Controller architecture

Controller core implementation

Harvester blockchain features and distributions

Blockchain economics refinements

Validator behavioral logic Whitepaper published Public testnet release

YES, THESE SNAKES ARE SMART. EACH ONE OF THEM IS A GENETICALLY EVOLVING NEURAL NETWORK. THE WAY THEY LEARN HOW TO AVOID OBSTACLES AND EAT THE "FOOD" PIECES ILLUSTRATES THE SELF-IMPROVING ASPECT OF SCYNET. LEARN MORE HERE

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Let's chat. 

Applications can emerge on many fields of supervised, reinforcement and generative AI. We are open to discuss future implementations. 

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