Welcome to the resource topic for 2024/1573
Title:
OML: Open, Monetizable, and Loyal AI
Authors: Zerui Cheng, Edoardo Contente, Ben Finch, Oleg Golev, Jonathan Hayase, Andrew Miller, Niusha Moshrefi, Anshul Nasery, Sandeep Nailwal, Sewoong Oh, Himanshu Tyagi, Pramod Viswanath
Abstract:Artificial Intelligence (AI) has steadily improved across a wide range of tasks, and a significant breakthrough towards general intelligence was achieved with the rise of generative deep models, which have garnered worldwide attention. However, the development and deployment of AI are almost entirely controlled by a few powerful organizations and individuals who are racing to create Artificial General Intelligence (AGI). These centralized entities make decisions with little public oversight, shaping the future of humanity, often with unforeseen consequences.
In this paper, we propose OML, which stands for Open, Monetizable, and Loyal AI, an approach designed to democratize AI development and shift control away from these monopolistic actors. OML is realized through an interdisciplinary framework spanning AI, blockchain, and cryptography. We present several ideas for constructing OML systems using technologies such as Trusted Execution Environments (TEE), traditional cryptographic primitives like fully homomorphic encryption and functional encryption, obfuscation, and AI-native solutions rooted in the sample complexity and intrinsic hardness of AI tasks.
A key innovation of our work is the introduction of a new scientific field: AI-native cryptography, which leverages cryptographic primitives tailored to AI applications. Unlike conventional cryptography, which focuses on discrete data and binary security guarantees, AI-native cryptography exploits the continuous nature of AI data representations and their low-dimensional manifolds, focusing on improving approximate performance. One core idea is to transform AI attack methods, such as data poisoning, into security tools. This novel approach serves as a foundation for OML 1.0, an implemented system that demonstrates the practical viability of AI-native cryptographic techniques. At the heart of OML 1.0 is the concept of model fingerprinting, a novel AI-native cryptographic primitive that helps protect the integrity and ownership of AI models.
The spirit of OML is to establish a decentralized, open, and transparent platform for AI development, enabling the community to contribute, monetize, and take ownership of AI models. By decentralizing control and ensuring transparency through blockchain technology, OML prevents the concentration of power and provides accountability in AI development that has not been possible before.
To the best of our knowledge, this paper is the first to:
• Identify the monopolization and lack of transparency challenges in AI deployment today and formulate the challenge as OML (Open, Monetizable, Loyal).
• Provide an interdisciplinary approach to solving the OML challenge, incorporating ideas from AI, blockchain, and cryptography.
• Introduce and formally define the new scientific field of AI-native cryptography.
• Develop novel AI-native cryptographic primitives and implement them in OML 1.0, analyzing their security and effectiveness.
• Leverage blockchain technology to host OML solutions, ensuring transparency, decentralization, and alignment with the goals of democratized AI development.
Through OML, we aim to provide a decentralized framework for AI development that prioritizes open collaboration, ownership rights, and transparency, ultimately fostering a more inclusive AI ecosystem.
ePrint: https://eprint.iacr.org/2024/1573
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