Article source: LXDAO
In the blockchain and open-source field, efficient fund allocation has always been a challenge. Today, an innovative project called Deep Funding is attempting to solve this problem with artificial intelligence and decentralized review. This project, supported by an initial funding of $250,000 from Vitalik Buterin, not only aims to address the resource allocation issues within the current Ethereum ecosystem but also to pioneer new models for future public goods funding.
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 decentralized review mechanisms, aiming to solve the inefficiency of resource allocation within the Ethereum ecosystem. The project's goal is to build a fair, transparent, and efficient fund allocation system that supports critical open-source projects related to Ethereum for long-term sustainable development.
Official website: https://deepfunding.org/
What problem do you want to solve?
Currently, the following issues exist in the allocation of public goods funding in Ethereum:
Irrationality of Human Decision-Making: When faced with complex and abstract problems, humans often find it difficult to make reasonable judgments.
Preference for Surface-Level Projects: Election-based funding mechanisms tend to favor funding projects that are obviously apparent, while neglecting deeper technical dependencies and complex contributions.
This leads to insufficient support for some crucial but 'hidden' infrastructure within the Ethereum ecosystem, 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 used to solve the problem?
The solutions proposed by Deep Funding include:
1. Construct Deep Graph
Deep Graph is a dynamic dependency graph that displays the dependencies between projects and allocates weights to each dependency. In this way, the contributions and actual value of public goods are visualized, solving the problem 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, last update time, etc.). This requires you to leverage your imagination and understanding of the value of open-source projects.
Weight Allocation: AI models allocate weights based on the importance and actual impact of dependencies, dynamically adjusting fund distribution.
Verification and Optimization: Conduct sampling of models through the jury to ensure the reasonableness of weights.
3. Review Panel Review Mechanism
The review panel is composed of experts who provide training data for the models by answering questions like 'Which is more important, project A or B?'. This type of question is chosen because it is relatively easy for humans to discern and answer.
Collaboration mode between humans and AI: humans are responsible for direction and value judgment, while AI provides data analysis support. Subsequently, multiple models that closely align with human consensus will be selected for application.
4. Fair Allocation of Funds
Funds are allocated based on the contribution ratio of the projects, and there will also be incentives for the winning models.
Deep Funding will not only be used for the weighting and allocation of open-source software; this model can be applied to any scenario with dependencies and allocations. For example: papers, music, film works, etc. Open-source software is just an initial attempt; 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, constructing a weighted graph based on the dependencies of open-source projects to determine the donation amounts each repository should receive. Then, it focuses on open-source projects under the Ethereum tag, especially clients.
Current progress of the Deep Funding project includes:
Sponsorship and Funding: Vitalik Buterin provided initial sponsorship of $250,000.
Data Preparation: Collecting Ethereum dependency graphs, involving data from approximately 40,000+ edges. It is currently ready.
Mechanism Design: Conducting AI model competitions (to be held on Kaggle), currently recruiting AI models.
Sample Evaluation: Validate model effectiveness through jury sampling; apply the dependency weight model to Ethereum-related projects and observe actual effects.
Of the $250K prize, $170k will be allocated to projects based on the weight of the dependency graph, $40k will reward the best-performing models in review sampling, and $40k will reward models submitted as open source. The innovativeness of these models will be evaluated and decided by an expert review panel.
There are still many challenges to address
Fairness and Incentive Mechanism of Review: How to ensure neutrality and long-term participation enthusiasm of the review panel? How to build a fair and effective review panel?
Effectiveness of AI Models: How to accurately weight deep dependencies and prevent models from being misused or gamified?
Dynamic Adjustment Mechanism: How to balance self-assessment and external review to avoid bias?
Funding Sources and Incentive Methods: How to attract more funds to participate in allocation, especially for non-code contributions?
We will gradually discuss and explore.
