Trustless Machine Learning Contracts; Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain

Preprint English OPEN
Kurtulmus, A. Besir ; Daniel, Kenny (2018)
  • Subject: Computer Science - Cryptography and Security

Using blockchain technology, it is possible to create contracts that offer a reward in exchange for a trained machine learning model for a particular data set. This would allow users to train machine learning models for a reward in a trustless manner. The smart contract will use the blockchain to automatically validate the solution, so there would be no debate about whether the solution was correct or not. Users who submit the solutions won't have counterparty risk that they won't get paid for their work. Contracts can be created easily by anyone with a dataset, even programmatically by software agents. This creates a market where parties who are good at solving machine learning problems can directly monetize their skillset, and where any organization or software agent that has a problem to solve with AI can solicit solutions from all over the world. This will incentivize the creation of better machine learning models, and make AI more accessible to companies and software agents.
  • References (13)
    13 references, page 1 of 2

    Buterin, Vitalik. A next-generation smart contract and decentralized application platform. 2014.

    Chen, Ting, Li, Xiaoqi, Luo, Xiapu, and Zhang, Xiaosong. Under-optimized smart contracts devour your money. CoRR, abs/1703.03994, 2017. URL http://arxiv.org/abs/1703.03994.

    Chung, Joon Son, Senior, Andrew W., Vinyals, Oriol, and Zisserman, Andrew. Lip reading sentences in the wild. CoRR, abs/1611.05358, 2016. URL http: //arxiv.org/abs/1611.05358.

    Graepel, Thore, Lauter, Kristin, and Naehrig, Michael. Ml con dential: Machine learning on encrypted data. In Kwon, Taekyoung, Lee, MunKyu, and Kwon, Daesung (eds.), Information Security and Cryptology { ICISC 2012, pp. 1{21, Berlin, Heidelberg, 2013. Springer Berlin Heidelberg. ISBN 978-3-642-37682-5.

    He, Kaiming, Zhang, Xiangyu, Ren, Shaoqing, and Sun, Jian. Delving deep into recti ers: Surpassing human-level performance on imagenet classi cation. CoRR, abs/1502.01852, 2015. URL http://arxiv. org/abs/1502.01852.

    Hornik, Kurt. Approximation capabilities of multilayer feedforward networks. Neural Networks, 4(2):251 { 257, 1991. ISSN 0893- 6080. doi: https://doi.org/10.1016/0893-6080(91) 90009-T. URL http://www.sciencedirect.com/ science/article/pii/089360809190009T.

    Jouppi, Norman P., Young, Cli , Patil, Nishant, Patterson, David, Agrawal, Gaurav, Bajwa, Raminder, Bates, Sarah, Bhatia, Suresh, Boden, Nan, Borchers, Al, Boyle, Rick, Cantin, Pierreluc, Chao, Cli ord, Clark, Chris, Coriell, Jeremy, Daley, Mike, Dau, Matt, Dean, Je rey, Gelb, Ben, Ghaemmaghami, Tara Vazir, Gottipati, Rajendra, Gulland, William, Hagmann, Robert, Ho, Richard C., Hogberg, Doug, Hu, John, Hundt, Robert, Hurt, Dan, Ibarz, Julian, Ja ey, Aaron, Jaworski, Alek, Kaplan, Alexander, Khaitan, Harshit, Koch, Andy, Kumar, Naveen, Lacy, Steve, Laudon, James, Law, James, Le, Diemthu, Leary, Chris, Liu, Zhuyuan, Lucke, Kyle, Lundin, Alan, MacKean, Gordon, Maggiore, Adriana, Mahony, Maire, Miller, Kieran, Nagarajan, Rahul, Narayanaswami, Ravi, Ni, Ray, Nix, Kathy, Norrie, Thomas, Omernick, Mark, Penukonda, Narayana, Phelps, Andy, Ross, Jonathan, Salek, Amir, Samadiani, Emad, Severn, Chris, Sizikov, Gregory, Snelham, Matthew, Souter, Jed, Steinberg, Dan, Swing, Andy, Tan, Mercedes, Thorson, Gregory, Tian, Bo, Toma, Horia, Tuttle, Erick, Vasudevan, Vijay, Walter, Richard, Wang, Walter, Wilcox, Eric, and Yoon, Doe Hyun. Indatacenter performance analysis of a tensor processing unit. CoRR, abs/1704.04760, 2017. URL http://arxiv.org/abs/1704.04760.

    Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geo rey E. Imagenet classi cation with deep convolutional neural networks. In Pereira, F., Burges, C. J. C., Bottou, L., and Weinberger, K. Q. (eds.), Advances in Neural Information Processing Systems 25, pp. 1097{1105. Curran Associates, Inc., 2012. URL http://papers.nips.cc/paper/ 4824-imagenet-classification-with-deep-convolutional-neural-networks. pdf.

    Silver, David, Huang, Aja, Maddison, Christopher J., Guez, Arthur, Sifre, Laurent, van den Driessche, George, Schrittwieser, Julian, Antonoglou, Ioannis, Panneershelvam, Veda, Lanctot, Marc, Dieleman, Sander, Grewe, Dominik, Nham, John, Kalchbrenner, Nal, Sutskever, Ilya, Lillicrap, Timothy, Leach, Madeleine, Kavukcuoglu, Koray, Graepel, Thore, and Hassabis, Demis. Mastering the game of go with deep neural networks and tree search. Nature, 529:484{503, 2016. URL http://www.nature.com/nature/ journal/v529/n7587/full/nature16961.html.

    Trask, Andrew. Building safe ai. URL http:// iamtrask.github.io/2017/03/17/safe-ai/.

  • Metrics
    No metrics available
Share - Bookmark