Watch · narrated walkthroughs
A zero-knowledge proof lets one party convince another that a computation was performed correctly without revealing the inputs — which is exactly what verifiable AI inference needs. This series builds the idea from interactive proofs and the simulator argument through Fiat-Shamir, SNARKs, transparent STARKs, and closes with zkML: proving a model produced an output on a committed input. Grounded in Goldwasser-Micali-Rackoff, Groth16, and Ben-Sasson STARKs.