Gensyn - the future of deep learning training

Gensyn – the future of deep learning training. Find out more about the project’s objectives and features, and go straight to their official website.

Gensyn brings together all the computers in the world in a global machine learning cluster that can be used by anyone, at any time.

The Gensyn protocol confidently drives large-scale, low-cost neural networks. The protocol enables lower prices.

If you keep up to date with the decentralized web or web3.0, you’ll certainly be hearing more and more about this project, as it meets specific needs and the solution was long overdue.

Gensyn – introduction:

Gensyn is an innovative platform dedicated to the collaborative training of decentralized artificial intelligence (AI) models. It relies on a distributed computing infrastructure, allowing various stakeholders to participate and contribute to the training processes of AI models. Here is a detailed presentation of the main aspects of the project:

Project Objectives

Democratization of AI:
- Gensyn aims to make AI model training capabilities accessible to a wider audience, including independent developers, researchers, and small businesses.
- By lowering the entry barriers, the project facilitates innovation and experimentation in the field of AI.
Decentralized Infrastructure:
- The project leverages a decentralized computing infrastructure, utilizing available resources worldwide. This reduces the costs associated with model training and optimizes the use of global computing resources.
Collaborative Economy:
- Users can contribute computing resources to the platform and be compensated for their participation.
- This incentive-based approach encourages an active and diverse community of contributors.

Key Features

Distributed Model Training:
- Users can submit their AI model training tasks to the platform, which distributes these tasks across the available computing resources in its network.
- The platform ensures efficient task distribution management and result retrieval.
Resource Marketplace:
- Gensyn offers a marketplace where computing resource contributors can offer their services, and users can choose the offers that best meet their needs and budget.
Security and Privacy:
- The platform implements advanced security protocols to ensure the confidentiality of data and models during distributed computing processes.
Ease of Integration:
- Gensyn provides APIs and development tools to facilitate integration with users’ existing systems and workflows.

Advantages

Cost Reduction:
- By using decentralized computing resources, the platform enables significant savings compared to traditional computing infrastructures.
Scalability:
- The distributed nature of the infrastructure allows for easy scaling of computing capabilities based on user needs.
Flexibility and Accessibility:
- The platform is designed to be accessible and usable by a wide variety of profiles, from novices to AI experts.

A project that offers a concrete solution for boosting the development of deep learning:

The Gensyn Protocol is a layer-1 trustless protocol for deep learning computation that directly and immediately rewards supply-side participants for pledging their compute time to the network and performing ML tasks. The protocol requires no administrative overseer or legal enforcement, rather facilitating task distribution and payments programmatically through smart contracts. As described above, the fundamental challenge in building this network is the verification of completed ML work. This is a highly complex problem that sits at the intersection of complexity theory, game theory, cryptography, and optimisation.

A simple solution is to check the honesty of workers by re-doing their work. At a bare minimum, this requires a doubling of the operations required (‘single replication’); however, even with replication, the issue of trust remains unless the verifying party is the actual requestor of the work (in which case, they wouldn’t request the work as they’d simply perform it themselves). Therefore, ensuring the honesty of the verifying party can generate an infinite chain of replication, where each new verifier is required to check the work of the previous verifier.

This project solves this verification problem by nesting appropriate concepts into a robust solution that is far more efficient than existing replication methods. In so doing, it solves the infinite chain problem.

Conclusion

Gensyn positions itself as a key player in the field of decentralized AI model training, offering an innovative and economical alternative to traditional computing solutions. By promoting a collaborative and open approach, Gensyn.ai contributes to the advancement and democratization of AI worldwide.