White Paper
SeaChain introduces a groundbreaking solution to enhance supply chain transparency and certification. Powered by Non-Fungible Tokens (NFTs) and IoT chips, SeaChain enables comprehensive tracking throughout the entire supply chain. This innovative protocol leverages NFTs linked to IoT chips, ensuring unique identification and traceability at every stage of the supply chain process.
Abstract:
In today's world, markets play a vital role in efficiently producing various commodities, including machine intelligence. SeaChain proposes a novel market model where intelligence is priced and exchanged by peer-to-peer intelligence systems across the internet. Peers rank each other based on their ability to train neural networks, which in turn learn the value of their peers. This system incentivizes honest contributions and fosters a collectively run intelligence market that continually generates valuable insights.
Introduction:
The conventional approach to producing machine intelligence relies heavily on benchmarking and supervised learning. However, this approach has limitations, especially in rewarding the true value of intelligence and encouraging diversity in the field. SeaChain introduces a paradigm shift by measuring intelligence based on its applicability across a wide range of objectives, allowing legacy systems to be monetized for their unique value. By creating a network of interconnected computers sharing representations peer-to-peer (P2P) over the internet, SeaChain establishes a decentralized market for machine intelligence, enabling direct monetization for researchers and accessible purchasing for consumers.
Model:
SeaChain's model defines intelligence as a parameterized function trained over a dataset to minimize loss. Peers in the network, represented as functions with unique datasets, collaborate to minimize the collective loss objective. Through a process of peer-ranking and weight adjustment, SeaChain ensures fair rewards for honest contributions while mitigating the risk of collusion.
Incentive:
To prevent collusion and incentivize honest behavior, SeaChain introduces an incentive function that rewards peers based on their trustworthiness and contribution to the network. By dynamically adjusting rewards and penalties, SeaChain maintains network integrity and encourages participants to act in the best interest of the collective.
Bonds:
SeaChain further enhances the incentive mechanism by introducing bonds, which represent the proportional ownership of one peer in another. Bonds accumulate based on peer rankings and contribute to the redistribution of rewards within the network, fostering a system where peers speculate on each other's future value.
Reaching Consensus:
Ensuring consensus among peers is crucial for maintaining network integrity and preventing collusion. SeaChain employs a consensus mechanism that rewards highly trusted peers while penalizing those who deviate from the consensus. By continuously adjusting incentives based on network dynamics, SeaChain promotes honest behavior and discourages malicious actors.
Running the Network:
Operating a peer in the SeaChain network involves defining datasets, broadcasting examples, training models, and updating weights based on peer interactions. By standardizing input dimensions, employing conditional computation, and facilitating knowledge extraction, SeaChain enables efficient collaboration among peers while minimizing network bandwidth and computational overhead.
Tensor Standardization:
SeaChain utilizes tensor modalities to partition the network and standardize inputs and outputs across different model types. This approach promotes interoperability and allows for the integration of diverse data sources and processing techniques within the network.
Conditional Computation:
To address potential bandwidth limitations, SeaChain implements conditional computation, where peers learn to selectively query and interact with a subset of their peers based on input data. This reduces network transfer and computational overhead while maintaining robust connectivity and collaboration among peers.
Knowledge Extraction:
SeaChain enables knowledge extraction through distillation, allowing smaller models to mimic the behavior of the larger network. By distilling network knowledge into standalone models, SeaChain facilitates offline validation and inference, ensuring privacy and scalability without compromising network integrity.
Learning Weights:
Peers in the SeaChain network learn weights based on their contribution to the collective intelligence. By analyzing the impact of removing each peer from the network, SeaChain assigns informational significance to each peer, encouraging honest participation and discouraging collusion.
Collusion:
SeaChain’s incentive mechanism and consensus protocol effectively deter collusion by ensuring that honest peers are consistently rewarded and malicious actors are penalized. By continuously adjusting incentives and monitoring network dynamics, SeaChain maintains network integrity and promotes fair collaboration among participants.
Conclusion:
SeaChain represents a groundbreaking approach to supply chain transparency and certification, powered by NFTs and IoT technology. By leveraging peer-to-peer intelligence markets and incentivizing honest participation, SeaChain fosters a transparent and efficient supply chain ecosystem that benefits all stakeholders. Through innovative protocols and decentralized governance, SeaChain is poised to revolutionize the way we track, certify, and transact in the global supply chain landscape
