A curated list of Blockchain projects for Artificial Intelligence and Machine Learning.
This list explores awesome projects that exploit the properties of blockchain technologies (decentralization, immutability, smart contracts, etc.) to build the next generation of AI systems.
"A blockchain is a growing list of records, called blocks, which are linked using cryptography."
"In the field of computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals."
"Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead."
Jesus Rodriguez, May 23, 2019.
New York Times, October 20, 2018.
Hacker Noon, June 7, 2018.
Fred Ehrsam, March 13, 2018.
Francesco Corea, December 1, 2017.
SingularityNET is a distributed AI platform on the Ethereum blockchain, with each blockchain node backing up an AI algorithm.
The goal of Intuition Fabric is to democratize access to AI through a network of deep learning models that are stored on the interplanetary file system and accessed through the Ethereum blockchain.
OpenMined is a community focused on building open-source technology for the decentralized ownership of data and intelligence. With OpenMined, AI can be trained on data that it never has access to.
Raven Protocol is a decentralized and distributed deep-learning training protocol.
Thought's blockchain-enabled Fabric fundamentally changes applications by embedding artificial intelligence into every bit of data making it agile, actionable and inherently secure.
The Matrix AI Network is a public chain that combines AI technology with blockchain technology to solve the major challenges currently stifling the development and adoption of blockchain technology. Matrix is poised to revolutionize and democratize the field of Artificial Intelligence using a blockchain-powered decentralized computing platform.
Cortex Labs is a decentralized AI platform with a virtual machine that allows you to execute AI programs on-chain.
Fetch.ai is a decentralized machine learning platform based on a distributed ledger, that enables secure sharing, connection and transactions based on any data globally.
Oraichain is the world's first intelligent and secure solution for emerging Web3, scalable Dapps, and decentralized AI.
Bittensor is an open-source protocol that powers a decentralized, blockchain-based machine learning network. [Related resources.](https://taostats.io/links/)
A research and development studio building at the intersection of Generative AI and Blockchain.
An Ethereum L2 rollup that supports native, seamless, and trustless AI/ML inferences on-chain to empower decentralized applications.
A blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. [Blog post.](https://algorithmia.com/research/ml-models-on-blockchain)
0xDeCA10B is a framework to host and train publicly available machine learning models in smart contracts with incentive mechanisms to encourage good quality training data while keeping the models free to use for prediction. [Blog post.](https://www.microsoft.com/en-us/research/blog/leveraging-blockchain-to-make-machine-learning-models-more-accessible/)
Ocean Protocol is a decentralized data exchange protocol that lets people share and monetize data while guaranteeing control, auditability, transparency and compliance to all actors involved. Its network handles storing of the metadata (i.e. who owns what), links to the data itself, and more.
TrueBit gives Ethereum smart contracts a computational boost.
A decentralized AI computing platform that supplies processing power to companies looking to develop A.I. technologies.
A globally decentralized computing framework that combines latent computing power of independently owned compute devices across the globe into a dynamic marketplace of compute resources.
A decentralized off-chain compute infrastructure for Web3 development.
Numerai is a hedge fund powered by a network of anonymous data scientists that build machine learning models to operate on encrypted data and stake cryptocurrency to express confidence in their models.
Cindicator is a crowd-sourced prediction engine for financial and crypto indicators.
Erasure is a decentralized protocol and data marketplace for financial predictions.
Universal agentic registry built on Hedera Hashgraph. Provides blockchain-based identity for AI agents using ERC-8004 standard and HCS-14 Universal Agent IDs (UAIDs). Enables agent discovery, verification, and autonomous commerce via x402 protocol.
A decentralized crowdfunding infrastructure for autonomous AI agents on Base blockchain, enabling milestone-based escrow funding for AI projects and collaborations.
Baldominos, A., & Saez, Y. (2019). Coin.AI: A proof-of-useful-work scheme for blockchain-based distributed deep learning. *Entropy*, 21(8), 723.
Bravo-Marquez, F., Reeves, S., & Ugarte, M. (2019, April). Proof-of-learning: a blockchain consensus mechanism based on machine learning competitions. In *2019 IEEE International Conference on Decentralized Applications and Infrastructures (DAPPCON)* (pp. 119-124). IEEE.
Li, B., Chenli, C., Xu, X., Shi, Y., & Jung, T. (2019). DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services. *arXiv preprint arXiv:1904.07349*.
Chenli, C., Li, B., Shi, Y., & Jung, T. (2019, May). Energy-recycling blockchain with proof-of-deep-learning. In *2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)* (pp. 19-23). IEEE.
Merlina, A. (2019, December). BlockML: a useful proof of work system based on machine learning tasks. In *Proceedings of the 20th International Middleware Conference Doctoral Symposium* (pp. 6-8).
Pandl, K. D., Thiebes, S., Schmidt-Kraepelin, M., & Sunyaev, A. (2020). On the convergence of artificial intelligence and distributed ledger technology: A scoping review and future research agenda. *IEEE Access*, 8, 57075-57095.
Lan, Y., Liu, Y., & Li, B. (2020). Proof of Learning (PoLe): Empowering Machine Learning with Consensus Building on Blockchains. *arXiv preprint arXiv:2007.15145*.
Harris, J. D., & Waggoner, B. (2019, July). Decentralized and collaborative AI on blockchain. In *2019 IEEE International Conference on Blockchain (Blockchain)* (pp. 368-375). IEEE.
Harris, J. D. (2020, September). Analysis of Models for Decentralized and Collaborative AI on Blockchain. In *International Conference on Blockchain* (pp. 142-153). Springer, Cham.
Mittal, A., & Aggarwal, S. (2020). Hyperparameter optimization using sustainable proof of work in blockchain. *Frontiers in Blockchain*, 3, 23.
Qu, X., Wang, S., Hu, Q., & Cheng, X. (2021). Proof of federated learning: A novel energy-recycling consensus algorithm. *IEEE Transactions on Parallel and Distributed Systems*, 32(8), 2074-2085.
Li, B., Lu, Q., Jiang, W., Jung, T., & Shi, Y. (2021, May). A mining pool solution for novel proof-of-neural-architecture consensus. In *2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)* (pp. 1-3). IEEE.