Gombilla

Project owner

SMART WASTE: Decentralized Waste Management

A revolutionary decentralized waste management platform that connects waste producers directly with waste management companies and recyclers. Powered by AI algorithms, it analyzes historical & real-time data to predict waste generation, optimize collection routes, and match producers with the most suitable solutions based on location, waste type, & recycling capabilities. Smart contracts ensure transparent & automated transactions, fostering trust between parties. It also empowers communities through Community-Led Recycling Initiatives, allowing members to earn tokens for participating in recycling programs. These tokens can be redeemed for rewards or reinvested in environmental initiatives

Alignment-Optimized Fundraising Tool for AI Research and Entrepreneurship

RnDAO and ReLab, under the Regenerative Organizations Laboratory, aim to address the problem of stakeholder misalignment in AI development, particularly AGI, by developing the Preferential Return on Investment Soft-Capped Multistakeholder Model (PRISM). PRISM is designed to provide an alternative fundraising model for AI projects, ensuring alignment between entrepreneurs, researchers, investors, and collaborators, prioritizing humanity's wellbeing and safety. The grant application seeks funding to further research and apply PRISM to AI development and alignment, potentially offering a solution to the risks posed by competitive dynamics in the AI field. The project involves research planning, user interviews, design adaptations, presentation, validation, and publishing of findings. It will be conducted in collaboration with SingularityNET, leveraging both organizations' social channels and networks for promotion. The findings will be made publicly available under a creative commons license. Mitigation strategies include a collaborative alliance and the possibility of disproving the hypothesis.

DeFiGraph – Knowledge Graph (KG) for DeFi

The proposal outlines the need for a DeFi Knowledge Graph (KG) to simplify and democratize access to decentralized finance (DeFi) data. It highlights the complexity of DeFi and the lack of user-friendly interfaces, making it difficult for users and developers to engage effectively. The project, called DeFiGraph, aims to create a KG that maps relationships between various DeFi entities, backed by Language Learning Models (LLMs) for natural language queries.

Key components include data collection, KG design, semantic mapping, graph database implementation, and an interactive user interface. The project's innovation lies in combining a KG with a user-friendly interface. It envisions a range of applications, from personalized DeFi bots to on-chain yield products and real-time credit scoring systems.

Stakeholders include L3A, Openmesh, MIT, and Neo4j consultants. The estimated budget is $40,000, allocated mainly to external consultants. Success metrics include milestone completion, data quality, user engagement, technical accuracy, and industry impact.

Milestone 1 focuses on data gathering, preprocessing, and infrastructure setup, involving data source identification, API integration, data normalization, and xNode deployment. Milestone 2 covers KG and model development over ten weeks.

Juana Attieh

Project owner

Requirements for a Deep Funding Contribution Token

Photrek and NFT Guild's proposal aims to develop requirements for a DeepFunding Token that goes beyond the limitations of AGIX, creating a multifaceted governance system within Deep Funding and SingularityNET. This initiative seeks to represent social incentives such as engagement and participation through the token, enhancing community-driven dynamics. The proposal includes workshops, both open community and expert discussions, to ideate and design the token. The outcome will be a recommendation document presented to the SingularityNET Foundation and DeepFunding Team. With a budget of $5,000 in AGIX, the proposal aims to establish a resource pool and expertise to guide the token's design beyond the initial project phase.

Robert Haas

Project owner

Evolutionary search for robust trading strategies

This project uses grammar-guided genetic programming to automatically search for robust and creative trading strategies on user-defined markets, indicators, metrics and external timeseries data from predictive models, fundamental analysis or elsewhere. The main challenge in the design of trading strategies is not the automated search process, although an advanced one will be used here, but rather the design and implemention of a proper backtesting approach to evaluate candidate strategies that avoids common pitfalls such as overfitting to the provided data or optimizing the wrong goal. This challenge will be tackled by in-depth literature research about state-of-the-art statistical evaluation techniques and review of existing backtesting libraries. Consequently, the search itself will be tuned by initially developing simple and then increasingly advanced grammars that define different search spaces of candidate trading strategies.

Both 1) the evaluation method for candidate strategies and 2) the grammars for defining suitable spaces of candidate strategies will be integrated into an open-source software package and distributed via GitHub and PyPI. Based on this package, a service will be developed for SingularityNET's AI platform, where users can provide data plus various preferences to the algorithm and it will return evolved trading strategies that perform well on historical data and have a reasonably high chance to actually perform similarly well on live markets. These strategies can then be used either for manual trading or as part of an automated trading system, which involves further aspects such as portfolio management and position sizing for multiple strategies. When the markets undergo a regime change after some time, the strategies can easily be adapted by making use of the service again.