In the blockchain and open-source fields, efficient fund allocation has always been a challenge. Today, an innovative project called Deep Funding is attempting to address this issue using artificial intelligence and decentralized reviews. This project, supported by an initial funding of $250,000 from Vitalik Buterin, aims not only to solve the current resource allocation problems within the Ethereum ecosystem but also to create new models for funding public goods in the future.

01. Deep Funding

What is Deep Funding?

Deep Funding is an innovative project that optimizes the allocation of funds for public goods through AI and a decentralized review mechanism, aiming to solve the inefficiencies in resource allocation within the Ethereum ecosystem. The project's goal is to build a fair, transparent, and efficient funding distribution system that supports Ethereum and its key open-source projects, ensuring long-term sustainable development.

Official website: https://deepfunding.org/

What problems does it aim to solve?

Currently, the allocation of funds for Ethereum public goods faces the following issues:

1. Irrational human decision-making: When faced with complex and abstract problems, humans often struggle to make reasonable judgments.
2. Preference for surface-level projects: Election-based funding mechanisms tend to favor projects that are superficially obvious, neglecting deeper technical dependencies and complex contributions.

This results in some critical but "hidden" infrastructure for the Ethereum ecosystem not receiving adequate support, while resources may be wasted on projects that seem important in the short term but have limited long-term value.

What approach is being taken to solve the problem?

The solutions proposed by Deep Funding include:

1. Building Deep Graph

Deep Graph is a dynamic dependency graph that illustrates the relationships between projects and assigns weights to each dependency. This way, the contributions and actual value of public goods can be visualized, addressing the issue of measuring "invisible contributions."

2. AI model weighting and evaluation

• Data input: Based on various information about open-source projects (e.g., number of stars, contributor activity, last update time, etc.). This requires leveraging your imagination and understanding of the value of open-source projects.
• Weight allocation: The AI model assigns weights based on the importance and actual impact of dependencies, dynamically adjusting fund distribution.
• Verification and optimization: The model is randomly checked by a jury to ensure the reasonableness of the weights.

3. Jury review mechanism

• The jury consists of experts who provide training data for the model by answering questions like "Which is more important, Project A or Project B?" This type of question is chosen because it is relatively easy for humans to discern and answer.
• Collaboration between humans and AI: Humans are responsible for direction and value judgments, while AI provides data analysis support. Multiple models that align well with human consensus will be selected for application.

4. Fair fund distribution

Funds will be allocated based on the contribution ratio of projects, with a portion also incentivizing the awarded models.

Deep Funding will not only be used for the weighting and distribution of open-source software; this model can be applied to any scenario involving dependencies and allocations. For example: papers, music, films, etc. Open-source software is just an initial attempt, and Deep Funding hopes to become a solution applicable to various scenarios.

02. Deep Funding Competition

Currently, the first competition of Deep Funding focuses on GitHub repositories and open-source projects, constructing a weighted graph based on the dependencies of open-source projects to determine the donation amounts each repository should receive. The focus is particularly on open-source projects under the Ethereum label, especially clients.

The current progress of the Deep Funding project includes:

1. Sponsorship and funding: Vitalik Buterin has provided an initial sponsorship of $250,000.
2. Data preparation: Collecting the Ethereum dependency graph, involving data from approximately 40,000+ edges. This has been prepared.
3. Mechanism design: Launching an AI model competition (to be held on Kaggle), currently recruiting AI models.
4. Pilot evaluation: Validating the effectiveness of the model through jury checks; applying the dependency weight model to Ethereum-related projects and observing the actual effects.

Among the $250K prize, $170K will be allocated to projects based on the weights of the dependency graph, $40K will reward the best-performing models in the jury checks, and $40K will reward models submitted to open-source contributions, with their innovation assessed by an expert jury.

There are still many challenges to address

1. Fairness of reviews and incentive mechanisms: How to ensure the neutrality of the jury and long-term participation? How to build a fair and effective jury?
2. Effectiveness of AI models: How to accurately weight deep dependencies and avoid misuse or gamification of the models?
3. Dynamic adjustment mechanisms: How to balance self-assessment and external reviews to avoid bias?
4. Sources of funding and incentive methods: How to attract more funds to participate in distribution, especially for non-code contributions?

We will gradually discuss and explore these issues.

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