CrunchDAO is a Decentralised Autonomous Organization of over 2,500 data scientists and 500 PhDs utilizing collective intelligence to solve complex problems, powered by a tokenomics model.
Our DeSci framework is twofold:
CrunchDAO is about periodic competitions with supervised learning problems at their core. Our focus is on leveraging Web3 collective intelligence to identify patterns in data, particularly associated with complex systems, by crowdsourcing. It serves as a secure intermediary, enabling data scientists to keep control of their models while powering institutions.
The predictions submitted to the competition inform the ensemble output of the Decentralised Autonomous Organization (DAO). Members have access to an encrypted, quantized datasets, create their prediction model and submit predictions on the given test set (without sharing their Intellectual Property, i.e., their models).
We plan the growth in the use of ZK Tech both to move towards homomorphic tournaments and to make private-data-based research reproducible. Part of the finalists of the DeSci London 2023 Hackathon, our Lead Quant, Matteo Manzi, presented our work on the use of IPFS to foster the reproducibility of machine learning papers.
CrunchDeSci is CrunchDAO’s decentralized science platform: a community-led, Open Science research framework that allows users to build knowledge incrementally through the scientific research papers proposed on its platform.
Our DeSci platform, through the use of ZK Tech, will allow users to access and contribute to our library of scientific papers and knowledge. Our technical roadmap lays out our plans for advancement with ZK Tech. We are actively performing R&D on ZKML throughout our work, and plan to integrate in our pipeline homomorphic encryption and ZKML tools. We are equally collaborating with Zama, also based in Paris, and in future we plan to integrate concreteML into our work. This is relevant, not only for the field of finance, but many other potential applications, including healthcare.
Our progress can be monitored through our DeSci platform, our Twitter and our LinkedIn.
This grant will enable us to define positions to develop this framework and to actively use it both inside the DAO and with our research group, working on Dynamical Systems Theory, Machine Learning and Quantitative Finance.