Trustless AI refers to artificial intelligence systems that operate based on verifiable logic and protocols, minimizing the need for reliance on a central authority or trusted intermediary. These systems leverage cryptographic principles and transparent execution environments to ensure that their operations and outputs are predictable and verifiable. This approach is foundational for AI applications within decentralized systems.
Context
The integration of trustless AI principles into blockchain and decentralized applications is a subject of ongoing exploration, particularly concerning its application in areas requiring automated decision-making without central control. Discussions often revolve around the mechanisms for achieving verifiable AI outputs, the challenges of ensuring AI robustness against adversarial attacks, and the potential for such systems to automate complex processes in a verifiable and decentralized manner.
Applying BFT-secure Hashgraph to LLM ensembles creates a novel, iterative consensus protocol that formally verifies model outputs, dramatically boosting AI reliability.
This research introduces zkFL, a novel framework leveraging zero-knowledge proofs and blockchain to secure federated learning against malicious aggregators, fostering trust in collaborative AI systems.
This research pioneers decentralized, verifiable multiparty computation for generative AI, safeguarding user privacy and model integrity against centralized control.
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