Introduction

The internet has transformed our world for the better, revolutionizing the way we communicate, work, and access information. However, as Chris Dixon points out in "Read Write Own," the internet has also undergone a troubling shift. What was once an open, democratic platform where anyone could participate equally has increasingly become dominated by a few major players. This centralization of power threatens the very principles of openness and collaboration that made the internet such a powerful force for positive change in the first place.

AI's journey has mirrored this trajectory. Initially collaborative and open, AI development has become more centralized as its immense potential has attracted major corporations. The Sentient protocol just recently launched their white paper "OML, Unleashing the Era of AI Entrepreneurship" which highlights the need for open Ai protocols that can compete against closed Ai networks.

"The development and deployment of AI are almost entirely controlled by a few powerful organizations, led by a handful of individuals, who are feverishly racing to create Artificial General Intelligence (AGI). Their decisions – made with little public oversight – will shape the future of humanity, often with unforeseen consequences."

To ensure that AI's transformative power benefits everyone, it's crucial to maintain its openness and accessibility. This is where the Open, Monetizable, Loyal (OML) framework comes in, designed to democratize AI and ensure its advantages are widely shared. 

"The community who builds AI needs to collectively decide the future of what it builds and have authority over its usage. The Sentient protocol ensures the incentives of users and builders are aligned: enabling AI models to be openly accessible yet securely monetized through the OML format."

What is OML?

OML is a groundbreaking framework aimed at reshaping how AI models are developed, shared, and monetized. It revolves around three core principles:

1. Open: OML champions accessibility, ensuring AI models are available to all—whether you're an independent developer, a startup, or a large corporation. This openness promotes collaboration, drives innovation, and prevents a few entities from monopolizing AI.

2. Monetizable: OML recognizes the value of AI creators' work and provides mechanisms for them to earn from their models without compromising accessibility. By using a decentralized system, creators can receive payments whenever their models are used, bypassing restrictive licensing and intermediaries. This incentivizes ongoing innovation and the development of cutting-edge technologies.

3. Loyal: OML upholds the principle of loyalty to the AI's original purpose and values. Advanced cryptographic techniques ensure the AI's behavior aligns with its creator’s vision, even as it’s used by others. Loyalty also extends to users, safeguarding their data and interactions with the model. This fosters trust and protects against misuse.

Together, these principles—openness, monetizability, and loyalty—form the foundation of OML, aiming to create a more equitable and sustainable AI ecosystem.

Empowering AI Builders

Currently, AI builders often remain unrecognized and unrewarded for their contributions. Much like digital artists before NFTs, AI creators face challenges in securing fair compensation. The OML framework addresses this by enabling builders to monetize their work effectively.

OML supports a community-driven approach where participants can develop, refine, and contribute to AI models. Builders can introduce new models, enhance existing ones, or provide data for training. Users can then utilize these models for various applications and contribute to their development by supplying usage data.

Aligning incentives with the growth of the AI economy is crucial. By rewarding contributors as more users engage with AI models, OML aims to close the loop and ensure fair compensation for all involved. This approach fosters innovation and drives the growth of the AI ecosystem.

Verifiable Ownership embedded in AI Models

Fingerprinting is a key technique in the OML framework for tracking model ownership and usage. When an AI model is formatted for OML, unique "fingerprints" are embedded into the model through supervised fine-tuning. These fingerprints serve as a permanent mark of ownership, allowing the platform to monitor usage and ensure creators are fairly compensated.

Various methods—such as mixing benign data, weight averaging, and regularization—are employed to maintain the model's utility during fingerprinting. The resulting OMLized model is then shared with users, who need specific keys to access its features. Meanwhile, interactions are securely recorded on the decentralized Sentient Protocol.

Tracking Usage and Restricting Access with the Sentient Protocol

The Sentient Protocol, integral to OML, tracks model usage and ensures fair compensation for AI builders. It involves three parties: model owners, model hosts, and provers, all interacting through the Sentient platform. When a model host requests access, the owner provides an OMLized model that includes cryptographic features for tracking and protection.

To monitor usage, the protocol requires hosts to obtain a signed permission string from Sentient for each user query. Provers periodically send special queries, or "keys," to verify compliance. If a host fails to secure the necessary permission, the prover reports it to Sentient, which can penalize the host accordingly. This system prevents hosts from circumventing usage tracking and allows model owners to control access based on the embedded key-response pairs.

Protecting Ownership and Ensuring Monetization 

The combination of fingerprinting and the Sentient Protocol's usage tracking system creates a robust framework for safeguarding AI builders' intellectual property and ensuring they receive fair compensation for their work. By embedding unique fingerprints into AI models, the OML framework establishes an immutable record of ownership that persists even as the models are shared and used by others.

Meanwhile, the Sentient Protocol's system of model hosts, provers, and permission strings ensures that all usage is accurately tracked and reported. Any attempt to circumvent this system is quickly detected by the provers, who serve as impartial auditors. This combination of techniques makes it virtually impossible for bad actors to use OML models without proper authorization and compensation to the rightful owners.

Moreover, the decentralized nature of the Sentient Protocol ensures that no single entity can manipulate or control the usage tracking system. This creates a trustless environment where AI builders can be confident that their work is protected and that they will be fairly rewarded for their contributions. By providing this level of security and accountability, the OML framework empowers AI builders to focus on innovation and collaboration, knowing that their efforts will be recognized and valued appropriately.

Conclusion 

AI has the potential to revolutionize various sectors, from healthcare and education to business and entertainment. However, as AI's economic impact grows, there's a risk of its development and control becoming concentrated in the hands of a few powerful corporations. This concentration could stifle innovation and limit the benefits of AI, raising concerns about fairness and transparency.

The OML framework offers a promising solution. By allowing AI creators to monetize their work without resorting to closed, proprietary models, OML promotes a more open, collaborative, and inclusive AI ecosystem. Through a combination of fingerprinting and the Sentient Protocol's usage tracking system, the framework protects AI builders' ownership rights and ensures they receive fair compensation for their contributions. As we navigate the future of AI, the OML framework provides a path forward that prioritizes openness, collaboration, and equitable rewards for all participants in the AI ecosystem

To read the full white paper - click here!

Introduction

The internet has transformed our world for the better, revolutionizing the way we communicate, work, and access information. However, as Chris Dixon points out in "Read Write Own," the internet has also undergone a troubling shift. What was once an open, democratic platform where anyone could participate equally has increasingly become dominated by a few major players. This centralization of power threatens the very principles of openness and collaboration that made the internet such a powerful force for positive change in the first place.

AI's journey has mirrored this trajectory. Initially collaborative and open, AI development has become more centralized as its immense potential has attracted major corporations. The Sentient protocol just recently launched their white paper "OML, Unleashing the Era of AI Entrepreneurship" which highlights the need for open Ai protocols that can compete against closed Ai networks.

"The development and deployment of AI are almost entirely controlled by a few powerful organizations, led by a handful of individuals, who are feverishly racing to create Artificial General Intelligence (AGI). Their decisions – made with little public oversight – will shape the future of humanity, often with unforeseen consequences."

To ensure that AI's transformative power benefits everyone, it's crucial to maintain its openness and accessibility. This is where the Open, Monetizable, Loyal (OML) framework comes in, designed to democratize AI and ensure its advantages are widely shared. 

"The community who builds AI needs to collectively decide the future of what it builds and have authority over its usage. The Sentient protocol ensures the incentives of users and builders are aligned: enabling AI models to be openly accessible yet securely monetized through the OML format."

What is OML?

OML is a groundbreaking framework aimed at reshaping how AI models are developed, shared, and monetized. It revolves around three core principles:

1. Open: OML champions accessibility, ensuring AI models are available to all—whether you're an independent developer, a startup, or a large corporation. This openness promotes collaboration, drives innovation, and prevents a few entities from monopolizing AI.

2. Monetizable: OML recognizes the value of AI creators' work and provides mechanisms for them to earn from their models without compromising accessibility. By using a decentralized system, creators can receive payments whenever their models are used, bypassing restrictive licensing and intermediaries. This incentivizes ongoing innovation and the development of cutting-edge technologies.

3. Loyal: OML upholds the principle of loyalty to the AI's original purpose and values. Advanced cryptographic techniques ensure the AI's behavior aligns with its creator’s vision, even as it’s used by others. Loyalty also extends to users, safeguarding their data and interactions with the model. This fosters trust and protects against misuse.

Together, these principles—openness, monetizability, and loyalty—form the foundation of OML, aiming to create a more equitable and sustainable AI ecosystem.

Empowering AI Builders

Currently, AI builders often remain unrecognized and unrewarded for their contributions. Much like digital artists before NFTs, AI creators face challenges in securing fair compensation. The OML framework addresses this by enabling builders to monetize their work effectively.

OML supports a community-driven approach where participants can develop, refine, and contribute to AI models. Builders can introduce new models, enhance existing ones, or provide data for training. Users can then utilize these models for various applications and contribute to their development by supplying usage data.

Aligning incentives with the growth of the AI economy is crucial. By rewarding contributors as more users engage with AI models, OML aims to close the loop and ensure fair compensation for all involved. This approach fosters innovation and drives the growth of the AI ecosystem.

Verifiable Ownership embedded in AI Models

Fingerprinting is a key technique in the OML framework for tracking model ownership and usage. When an AI model is formatted for OML, unique "fingerprints" are embedded into the model through supervised fine-tuning. These fingerprints serve as a permanent mark of ownership, allowing the platform to monitor usage and ensure creators are fairly compensated.

Various methods—such as mixing benign data, weight averaging, and regularization—are employed to maintain the model's utility during fingerprinting. The resulting OMLized model is then shared with users, who need specific keys to access its features. Meanwhile, interactions are securely recorded on the decentralized Sentient Protocol.

Tracking Usage and Restricting Access with the Sentient Protocol

The Sentient Protocol, integral to OML, tracks model usage and ensures fair compensation for AI builders. It involves three parties: model owners, model hosts, and provers, all interacting through the Sentient platform. When a model host requests access, the owner provides an OMLized model that includes cryptographic features for tracking and protection.

To monitor usage, the protocol requires hosts to obtain a signed permission string from Sentient for each user query. Provers periodically send special queries, or "keys," to verify compliance. If a host fails to secure the necessary permission, the prover reports it to Sentient, which can penalize the host accordingly. This system prevents hosts from circumventing usage tracking and allows model owners to control access based on the embedded key-response pairs.

Protecting Ownership and Ensuring Monetization 

The combination of fingerprinting and the Sentient Protocol's usage tracking system creates a robust framework for safeguarding AI builders' intellectual property and ensuring they receive fair compensation for their work. By embedding unique fingerprints into AI models, the OML framework establishes an immutable record of ownership that persists even as the models are shared and used by others.

Meanwhile, the Sentient Protocol's system of model hosts, provers, and permission strings ensures that all usage is accurately tracked and reported. Any attempt to circumvent this system is quickly detected by the provers, who serve as impartial auditors. This combination of techniques makes it virtually impossible for bad actors to use OML models without proper authorization and compensation to the rightful owners.

Moreover, the decentralized nature of the Sentient Protocol ensures that no single entity can manipulate or control the usage tracking system. This creates a trustless environment where AI builders can be confident that their work is protected and that they will be fairly rewarded for their contributions. By providing this level of security and accountability, the OML framework empowers AI builders to focus on innovation and collaboration, knowing that their efforts will be recognized and valued appropriately.

Conclusion 

AI has the potential to revolutionize various sectors, from healthcare and education to business and entertainment. However, as AI's economic impact grows, there's a risk of its development and control becoming concentrated in the hands of a few powerful corporations. This concentration could stifle innovation and limit the benefits of AI, raising concerns about fairness and transparency.

The OML framework offers a promising solution. By allowing AI creators to monetize their work without resorting to closed, proprietary models, OML promotes a more open, collaborative, and inclusive AI ecosystem. Through a combination of fingerprinting and the Sentient Protocol's usage tracking system, the framework protects AI builders' ownership rights and ensures they receive fair compensation for their contributions. As we navigate the future of AI, the OML framework provides a path forward that prioritizes openness, collaboration, and equitable rewards for all participants in the AI ecosystem

To read the full white paper - click here!