Decentralized computing power for Automated Machine Learning
An open-sourced network for
self-improving AI models
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.
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.
An AutoML node creates a stream of Training Jobs.
Each job consists of a Model, Obfuscated data, and a performance metric.
Jobs are picked by computing nodes, also known as Trainers.
Models are trained on the provided dataset until an optimal configuration is achieved.
As the training job is complete, the trained model is returned to the AutoML node.
Validators test the accuracy of the model via the Zero-Knowledge Protocol.
AI models are ranked and trainers are rewarded.
The AutoML learns from the best models and creates better configurations for the next cycle of jobs.
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.
1.
An AutoML node creates a stream of Training Jobs.
8.
The AutoML learns from the best models and creates better configurations for the next cycle of jobs.
2.
Each job consists of a Model, Obfuscated data, and a performance metric.
3.
Jobs are picked by computing nodes, also known as Trainers.
7.
AI models are ranked and trainers are rewarded.
6.
Validators test the accuracy of the model via the Zero-Knowledge Protocol.
4.
Models are trained on the provided dataset until an optimal configuration is achieved.
5.
As the training job is complete, the trained model is returned to the AutoML node.
Making use of
Winner of
Project by
Partner of
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.
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Applications can emerge on many fields of supervised, reinforcement and generative AI. We are open to discuss future implementations.