With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightlyย outย ofย date from time to time
With the amount of new subnets being added it can be hard to get up to date information across all subnets, so data may be slightlyย outย ofย date from time to time
Bittensor’s Subnet 2, known as Omron, represents a significant advancement in the integration of blockchain technology with artificial intelligence (AI). Developed by Inference Labs, Omron is designed to establish a decentralized network where AI computations are not only performed but also verified using Zero-Knowledge Machine Learning (zkML) techniques. This innovative approach ensures that AI operations are conducted with enhanced privacy, security, and verifiability, addressing some of the most pressing challenges in the current AI landscape. By leveraging zkML, Omron enables the validation of AI model inferences without exposing sensitive data or proprietary model details, thus fostering a trustless environment for AI computations.
The primary objective of Omron is to democratize access to verifiable AI computations within a decentralized framework. In traditional AI systems, users often have to trust centralized entities with their data, raising concerns about privacy and potential misuse. Omron addresses these issues by implementing zkML, which allows for the verification of AI computations without revealing the underlying data or model specifics. This ensures that users can trust the outcomes of AI processes without compromising their sensitive information. Furthermore, Omron aims to optimize strategies in liquid staking and restaking protocols by utilizing AI models that can analyze vast amounts of data to provide efficient and effective recommendations. This is particularly relevant in decentralized finance (DeFi), where maximizing returns while minimizing risks is paramount.
Bittensor’s Subnet 2, known as Omron, represents a significant advancement in the integration of blockchain technology with artificial intelligence (AI). Developed by Inference Labs, Omron is designed to establish a decentralized network where AI computations are not only performed but also verified using Zero-Knowledge Machine Learning (zkML) techniques. This innovative approach ensures that AI operations are conducted with enhanced privacy, security, and verifiability, addressing some of the most pressing challenges in the current AI landscape. By leveraging zkML, Omron enables the validation of AI model inferences without exposing sensitive data or proprietary model details, thus fostering a trustless environment for AI computations.
The primary objective of Omron is to democratize access to verifiable AI computations within a decentralized framework. In traditional AI systems, users often have to trust centralized entities with their data, raising concerns about privacy and potential misuse. Omron addresses these issues by implementing zkML, which allows for the verification of AI computations without revealing the underlying data or model specifics. This ensures that users can trust the outcomes of AI processes without compromising their sensitive information. Furthermore, Omron aims to optimize strategies in liquid staking and restaking protocols by utilizing AI models that can analyze vast amounts of data to provide efficient and effective recommendations. This is particularly relevant in decentralized finance (DeFi), where maximizing returns while minimizing risks is paramount.
Omron’s architecture is meticulously designed to facilitate decentralized, verifiable AI computations. At its core, the network comprises two primary participants: Miners and Validators.โ
Miners are responsible for executing AI model inferences based on the requests they receive. Upon processing these requests, miners generate Zero-Knowledge Proofs (ZKPs) that attest to the correctness of their computations without disclosing any sensitive data. This process involves converting the inference request into a format compatible with the zkML circuit, executing the computation, and producing both the result and the accompanying ZKP. The miner then sends these outputs back to the validator for verification. โ
Intro to Subnet 2 | Subnet 2
Validators, on the other hand, play a crucial role in maintaining the integrity of the network. They distribute inference tasks to miners and are responsible for verifying the results returned. This verification process involves checking the validity of the ZKPs provided by the miners to ensure that the computations were performed correctly. Validators assess miners based on several criteria, including the validity of the proof, the size of the proof, and the response time. These assessments directly influence the rewards that miners receive, creating an incentive structure that promotes efficient and accurate computations. โ
The interaction between miners and validators is facilitated through Bittensor’s decentralized protocol. Validators send inference queries to miners, who process these queries using their AI models and generate ZKPs. The results and proofs are then returned to the validators for verification. This process ensures that all computations within the network are both accurate and verifiable, fostering a trustless environment where users can rely on the integrity of the AI outputs without needing to trust any single entity. โ
Product Implementation and Features
Omron has been implemented with a focus on providing practical and verifiable AI solutions, particularly in the realm of liquid staking and restaking strategies. Currently, the network has deployed a Long Short-Term Memory (LSTM) model that specializes in predicting liquid staking durations. This model operates within the decentralized framework of Omron, utilizing zkML to ensure that all inferences are verifiable without exposing sensitive data. This implementation showcases the network’s capability to handle complex AI tasks while maintaining privacy and security. โ
In addition to its AI capabilities, Omron has integrated with the Ethereum blockchain to enhance its functionality. The network has deployed a smart contract on the Ethereum mainnet that accepts deposits of liquid restaking tokens and wrapped Ether (wETH). Users who deposit these assets accrue points over time, reflecting their participation in the network. This integration not only facilitates community involvement but also underscores Omron’s commitment to interoperability with existing blockchain ecosystems, thereby expanding its reach and utility.
Significance of zkML in Omron
Concept
Zero-Knowledge Machine Learning (zkML) combines zero-knowledge proofs with machine learning, enabling the verification of model computations without revealing underlying data or model specifics. โ
Advantages
By integrating zkML, Omron addresses critical challenges in AI, including data privacy, trust in computational results, and the need for decentralized processing.
Omron’s architecture is meticulously designed to facilitate decentralized, verifiable AI computations. At its core, the network comprises two primary participants: Miners and Validators.โ
Miners are responsible for executing AI model inferences based on the requests they receive. Upon processing these requests, miners generate Zero-Knowledge Proofs (ZKPs) that attest to the correctness of their computations without disclosing any sensitive data. This process involves converting the inference request into a format compatible with the zkML circuit, executing the computation, and producing both the result and the accompanying ZKP. The miner then sends these outputs back to the validator for verification. โ
Intro to Subnet 2 | Subnet 2
Validators, on the other hand, play a crucial role in maintaining the integrity of the network. They distribute inference tasks to miners and are responsible for verifying the results returned. This verification process involves checking the validity of the ZKPs provided by the miners to ensure that the computations were performed correctly. Validators assess miners based on several criteria, including the validity of the proof, the size of the proof, and the response time. These assessments directly influence the rewards that miners receive, creating an incentive structure that promotes efficient and accurate computations. โ
The interaction between miners and validators is facilitated through Bittensor’s decentralized protocol. Validators send inference queries to miners, who process these queries using their AI models and generate ZKPs. The results and proofs are then returned to the validators for verification. This process ensures that all computations within the network are both accurate and verifiable, fostering a trustless environment where users can rely on the integrity of the AI outputs without needing to trust any single entity. โ
Product Implementation and Features
Omron has been implemented with a focus on providing practical and verifiable AI solutions, particularly in the realm of liquid staking and restaking strategies. Currently, the network has deployed a Long Short-Term Memory (LSTM) model that specializes in predicting liquid staking durations. This model operates within the decentralized framework of Omron, utilizing zkML to ensure that all inferences are verifiable without exposing sensitive data. This implementation showcases the network’s capability to handle complex AI tasks while maintaining privacy and security. โ
In addition to its AI capabilities, Omron has integrated with the Ethereum blockchain to enhance its functionality. The network has deployed a smart contract on the Ethereum mainnet that accepts deposits of liquid restaking tokens and wrapped Ether (wETH). Users who deposit these assets accrue points over time, reflecting their participation in the network. This integration not only facilitates community involvement but also underscores Omron’s commitment to interoperability with existing blockchain ecosystems, thereby expanding its reach and utility.
Significance of zkML in Omron
Concept
Zero-Knowledge Machine Learning (zkML) combines zero-knowledge proofs with machine learning, enabling the verification of model computations without revealing underlying data or model specifics. โ
Advantages
By integrating zkML, Omron addresses critical challenges in AI, including data privacy, trust in computational results, and the need for decentralized processing.
Inference Labs specializes in developing advanced infrastructure and products for Artificial Intelligence. The team have experience in civil aviation AI projects and social AI experiments which led up to their involvement with Bittensor. Delving into questions about AI model origins and royalties distribution, the team recognized the potential of blockchain for ensuring authenticity and fair compensation in AI collaborations. By identifying the need for proof of inference in the AI space, Inference Labs found a specific problem to solve with broad applications, including across web 2 and web 3 platforms.
Colin Gagich – Co-Founder
Ronald Chan – Co-Founder
Eric Lesiuta – Software Engineer
Spencer Graham – Software Developer
Will P – Software Developer
Ehsan Meamari – Researcher
Julia Thรฉberge – Executive Assistant
Shawn Knapczykย – Communities Manager
Ivan Anishchuk – Crypto Researcher
Jonathan Gold – Software Engineer
Inference Labs specializes in developing advanced infrastructure and products for Artificial Intelligence. The team have experience in civil aviation AI projects and social AI experiments which led up to their involvement with Bittensor. Delving into questions about AI model origins and royalties distribution, the team recognized the potential of blockchain for ensuring authenticity and fair compensation in AI collaborations. By identifying the need for proof of inference in the AI space, Inference Labs found a specific problem to solve with broad applications, including across web 2 and web 3 platforms.
Colin Gagich – Co-Founder
Ronald Chan – Co-Founder
Eric Lesiuta – Software Engineer
Spencer Graham – Software Developer
Will P – Software Developer
Ehsan Meamari – Researcher
Julia Thรฉberge – Executive Assistant
Shawn Knapczykย – Communities Manager
Ivan Anishchuk – Crypto Researcher
Jonathan Gold – Software Engineer
Omron’s development is structured into multiple phases, each aimed at enhancing the network’s capabilities and expanding its applications.โ
Version 1 focused on establishing the foundational infrastructure for verifiable AI computations. This phase saw the deployment of the initial LSTM model with zkML capabilities and the implementation of the Ethereum deposit contract, enabling users to participate in the network by depositing assets and accruing points. โ
Version 2 introduces several significant enhancements aimed at improving the network’s performance and utility:
These developments are designed to position Omron as a versatile and robust platform for decentralized, verifiable AI computations.
Omron’s development is structured into multiple phases, each aimed at enhancing the network’s capabilities and expanding its applications.โ
Version 1 focused on establishing the foundational infrastructure for verifiable AI computations. This phase saw the deployment of the initial LSTM model with zkML capabilities and the implementation of the Ethereum deposit contract, enabling users to participate in the network by depositing assets and accruing points. โ
Version 2 introduces several significant enhancements aimed at improving the network’s performance and utility:
These developments are designed to position Omron as a versatile and robust platform for decentralized, verifiable AI computations.
Huge thanks to Keith Singery (aka Bittensor Guru) for all of his fantastic work in the Bittensor community. Make sure to check out his other video/audio interviews by clicking HERE.
Inference Labs has developed Subnet 2 Omron to offer cryptographically verified proof-of-inference. Their initial focus is on Active Validation Service (AVS) and Liquid Restaking Tokens (LRT). Colin, cofounder of Inference Labs, guides us through the subnet and its essential role in ensuring authenticity for inference.
Keep ahead of the Bittensor exponential development curve…
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