Proof of Inference (PoI) & Proof of Useful Work (PoUW

Proof of Inference (PoI):

Purpose: To ensure that AI inference tasks are performed correctly and consistently across different nodes using the same model.
Mechanism:
- Nodes perform AI inference tasks and generate outputs.
- Outputs are compared using embeddings and vector distances to measure similarity.
- High similarity between outputs from different nodes indicates that the nodes have performed the task correctly, providing consensus on the inference results.
- This process ensures that nodes are generating accurate and reliable outputs based on the same AI model.

Proof of Useful Work (PoUW):

Purpose: To validate the correctness and usefulness of AI inference results, ensuring they are meaningful and can contribute to further knowledge.
Mechanism:
- Nodes generate AI inference results, which are then reviewed by other nodes or validators.
- Validators assess the usefulness of the results by checking if the information generated is correct, relevant, and can be extended as knowledge.
- This might involve additional checks, such as semantic consistency, logical coherence, or practical applicability of the generated information.
- Validators provide feedback or scores on the results, helping to determine their usefulness.
- This process ensures that AI inference outputs are not only accurate but also valuable and applicable in real-world scenarios.

Summary

Proof of Inference (PoI):

Focuses on ensuring consistency and correctness of inference tasks.
Uses embeddings and vector distances to measure similarity between outputs.

Proof of Useful Work (PoUW):

Focuses on validating the correctness and practical usefulness of inference results.
Involves peer review or validation to assess the relevance and applicability of the generated information.

Both mechanisms work together to maintain the integrity and reliability of AI inference tasks within the Cortensor network, ensuring that outputs are both accurate and valuable.

PreviousConsensus & Validation Nextaka Mining

Last updated 6 months ago

Proof of Inference (PoI):

Purpose: To ensure that AI inference tasks are performed correctly and consistently across different nodes using the same model.

Mechanism:

Nodes perform AI inference tasks and generate outputs.
Outputs are compared using embeddings and vector distances to measure similarity.
High similarity between outputs from different nodes indicates that the nodes have performed the task correctly, providing consensus on the inference results.
This process ensures that nodes are generating accurate and reliable outputs based on the same AI model.

Proof of Useful Work (PoUW):

Purpose: To validate the correctness and usefulness of AI inference results, ensuring they are meaningful and can contribute to further knowledge.

Mechanism:

Nodes generate AI inference results, which are then reviewed by other nodes or validators.
Validators assess the usefulness of the results by checking if the information generated is correct, relevant, and can be extended as knowledge.
This might involve additional checks, such as semantic consistency, logical coherence, or practical applicability of the generated information.
Validators provide feedback or scores on the results, helping to determine their usefulness.
This process ensures that AI inference outputs are not only accurate but also valuable and applicable in real-world scenarios.

Summary

Proof of Inference (PoI):

Focuses on ensuring consistency and correctness of inference tasks.

Uses embeddings and vector distances to measure similarity between outputs.

Proof of Useful Work (PoUW):

Focuses on validating the correctness and practical usefulness of inference results.

Involves peer review or validation to assess the relevance and applicability of the generated information.

Both mechanisms work together to maintain the integrity and reliability of AI inference tasks within the Cortensor network, ensuring that outputs are both accurate and valuable.