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Bridge Atlas - Episode 5: Verifiability | Shreya Shankar & Justin Drake

InterviewShreya Shankar, Justin Drake
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Bridge Atlas - Episode 5: Verifiability | Shreya Shankar & Justin Drake
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Shreya Shankar (Berkeley PhD, creator of the DocETL declarative data-processing system) and Justin Drake (Ethereum Foundation, lean Ethereum project) compare verifiability across AI data pipelines and blockchain consensus. Drake explains SNARKs, trust-minimization, and recursive proof composition for Ethereum's deterministic and non-deterministic components, while Shankar details how DocETL handles LLM-powered query pipelines, human-AI misalignment, and eval design for unstructured data like court transcripts, with both drawing parallels between 'hints' in SNARK proving and decomposition techniques for AI reasoning verification.

Topics

ai-eval-design-and-human-ai-misalignmentbridge-atlasdocetl-declarative-data-pipelinesformal-verification-and-economic-penalties-for-malicious-actorsgovernanceoptimistic-vs-zk-rollups-and-trust-spectrumprotocolssnarks-and-recursive-proof-compositiontechnology

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