Summary of Oml: Open, Monetizable, and Loyal Ai, by Zerui Cheng et al.
OML: Open, Monetizable, and Loyal AI
by Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi, Anshul Nasery, Sandeep Nailwal, Sewoong Oh, Himanshu Tyagi, Pramod Viswanath
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Open, Monetizable, and Loyal (OML) framework is designed to democratize AI development by creating a decentralized platform for AI model creation, sharing, and monetization. This interdisciplinary approach combines AI, blockchain, and cryptography technologies to enable transparency, accountability, and community-driven innovation. The OML 1.0 prototype uses model fingerprinting to protect the integrity and ownership of AI models, transforming AI attack methods into security tools. This innovative framework has the potential to revolutionize the development and deployment of AI, ensuring that power is not concentrated in the hands of a few powerful organizations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are working on a new way to make artificial intelligence (AI) fairer and more open. They want to create a platform where anyone can contribute to, share, and earn money from their own AI models. This would help prevent a small group of people or companies from controlling all the AI technology. The team is using a mix of AI, blockchain, and cryptography to make this happen. They’re calling it Open, Monetizable, and Loyal (OML). One part of OML is called model fingerprinting, which helps keep AI models safe and honest. |
