Currently Numerai/Numeraire (NMR) is on Ethereum, but this is something I think would be an interesting usecase on Algorand:
Numerai is a hedge fund that uses a unique approach to investment management by harnessing the power of crowdsourced artificial intelligence (AI) models. Here is an overview of how Numerai works:
- Data anonymization: Numerai anonymizes financial data and sends it to its community of data scientists, who then create AI models to make predictions on this data.
- Model submissions: Data scientists submit their AI models to Numerai, which are trained and evaluated on the anonymized data.
- Staking: Data scientists must stake a certain amount of Numerai’s cryptocurrency, Numeraire (NMR), on their model in order to participate in the tournament. This helps to incentivize data scientists to submit high-quality models.
- Selection and weighting: Numerai selects the best models based on their performance on new, previously unseen data. Numerai then combines these models into an ensemble that is used to make trades in financial markets.
- Rewards: Data scientists whose models are included in the ensemble are rewarded with NMR tokens, while those whose models perform poorly lose their stake.
- Trading: Numerai uses the ensemble of AI models to make trades in financial markets. These trades are made using Numerai’s own capital, rather than investor funds.
- Performance monitoring: Numerai monitors the performance of its AI models and ensemble in real-time, making adjustments as necessary to optimize performance.
Overall, Numerai’s approach to investment management is unique in that it relies on a decentralized community of data scientists to create and improve AI models. This approach has proven to be highly effective, with Numerai’s hedge fund outperforming many traditional investment funds
Only thing I dont like, is investors losing their stake if their model doesnt perform. Dont see the point of that. I get stake to participate and be rewarded for a model that performs well but for someone to lose their investment just because their model didnt perform well isn’t a great approach. Fork it and make it better