In the blockchain and open-source fields, efficient fund distribution has always been a challenge. Today, an innovative project called Deep Funding is attempting to solve this issue with AI and decentralized reviews. Supported by an initial funding of $250,000 from Vitalik Buterin, this project not only aims to address current resource distribution problems in the Ethereum ecosystem but also to pioneer new models for the funding of future public goods.
01. Deep Funding
What is Deep Funding?
Deep Funding is an innovative project that optimizes the allocation of public goods funding through AI and decentralized review mechanisms, aiming to solve the inefficiencies in resource distribution within the Ethereum ecosystem. The project's goal is to build a fair, transparent, and efficient funding distribution system that supports Ethereum and its critical open-source projects, achieving long-term sustainable development.
Official Website: https://deepfunding.org/
What problem do you want to solve?
Currently, the allocation of public goods funding in Ethereum faces the following issues:
Irrationality of Human Decision-Making: When faced with complex and abstract issues, humans often find it difficult to make rational judgments.
Preference for Surface-Level Projects: Election-based funding mechanisms tend to favor projects that are superficially evident while ignoring deeper technical dependencies and complex contributions.
This has led to some critical yet 'hidden' infrastructure for the Ethereum ecosystem not receiving enough support, while also potentially wasting resources on projects that seem important in the short term but have limited long-term value.
What kind of thinking is being used to solve the problem?
The solutions proposed by Deep Funding include:
1. Build Deep Graph
Deep Graph is a dynamic dependency graph that shows the relationships between projects and allocates weights to each dependency. In this way, the contributions and actual value of public goods can be visualized, addressing the issue of 'invisible contributions' being difficult to measure.
2. AI Model Weighting and Evaluation
Data Input: Various information based on open-source projects (e.g., number of stars, contributor activity, update time, etc.). This requires you to exercise your imagination and understanding of the value of open-source projects.
Weight Allocation: The AI model allocates weights based on the importance and actual impact of dependencies, dynamically adjusting fund distribution.
Verification and Optimization: Random checks on the model by a jury to ensure the reasonableness of 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 was chosen because it is relatively easy for humans to discern and answer.
Collaboration Model between Humans and AI: Humans are responsible for direction and value judgments, while AI provides data analysis support. Subsequently, several models that align closely with human consensus will be selected for application.
4. Fair Distribution of Funds
Funds are allocated based on the contribution ratio of the project, and there will also be incentives for the awarded models.
Deep Funding will not only be used for weighting and distribution of open-source software; this model can be applied to any scenario involving dependencies and distribution. 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 repos and open-source projects, building a weighted graph based on the dependencies of open-source projects to determine the donation amount each repository should receive. Then it focuses on open-source projects under the Ethereum tag, especially clients.
The current progress of the Deep Funding project includes:
Sponsorship and Funding: Vitalik Buterin provided an initial sponsorship of $250,000.
Data Preparation: Collecting the Ethereum dependency graph, involving data on about 40,000+ edges. It has now been prepared.
Mechanism Design: Conducting AI model competitions (to be held on Kaggle), currently recruiting AI models.
Trial Evaluation: Validating model effectiveness through jury random checks; applying the dependency weight model to Ethereum-related projects to see the actual effect.
Of the $250K prize, $170K will be allocated to projects based on the weights of the dependency graph, $40K will be awarded to the best-performing models in the jury's random checks, and $40K will be awarded to models submitted as open-source, whose innovation will be evaluated by an expert jury.
There are still many challenges to address.
Jury Fairness and Incentive Mechanism: How to ensure the neutrality and long-term participation of the jury? How to create a fair and effective jury?
Effectiveness of AI Models: How to accurately weight deep dependencies and avoid misuse or gamification of the models?
Dynamic Adjustment Mechanism: How to balance self-assessment and external reviews to avoid bias?
Sources of Funds and Incentive Methods: How to attract more funds for distribution, especially for non-code contributions?
We will gradually engage in discussion and exploration.