  As artificial intelligence (AI) continues to permeate various domains,
concerns surrounding trust and transparency in AI-driven inference and training
processes have emerged, particularly with respect to potential biases and
traceability challenges. Decentralized solutions such as blockchain have been
proposed to tackle these issues, but they often struggle when dealing with
large-scale models, leading to time-consuming inference and inefficient
training verification. To overcome these limitations, we introduce BRAIN, a
Blockchain-based Reliable AI Network, a novel platform specifically designed to
ensure reliable inference and training of large models. BRAIN harnesses a
unique two-phase transaction mechanism, allowing real-time processing via
pipelining by separating request and response transactions. Each
randomly-selected inference committee commits and reveals the inference
results, and upon reaching an agreement through a smart contract, then the
requested operation is executed using the consensus result. Additionally, BRAIN
carries out training by employing a randomly-selected training committee. They
submit commit and reveal transactions along with their respective scores,
enabling local model aggregation based on the median value of the scores.
Experimental results demonstrate that BRAIN delivers considerably higher
inference throughput at reasonable gas fees. In particular, BRAIN's
tasks-per-second performance is 454.4293 times greater than that of a naive
single-phase implementation.
