The interesting promise of zkML is not simply privacy. It is proving that a computation was performed correctly without exposing the sensitive input or proprietary model details behind it. That is a powerful primitive for enterprise AI.

Inference Labs @inference_labs ·

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  1. #1

    From fuel leaks to smoke and fire, many airport risks are visual before they become operational problems. @hudsongrae_me is showing how Sertn can train models to detect these anom…

    Inference Labs @inference_labs ·
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  2. #2

    1/ Airports already have cameras covering stands, baggage halls, terminals, and airfield movement. The opportunity is not always adding more hardware. It is turning existing foota…

    Inference Labs @inference_labs ·
    1/ Airports already have cameras covering stands, baggage halls, terminals, and…
  3. #3

    Long-horizon AI is making outputs much larger and workflows much harder to inspect. The challenge is no longer only whether a model can finish the task, but whether the path there…

    Inference Labs @inference_labs ·
  4. #4

    In a factory, airport, or logistics site, the physical event may last seconds. The model output may trigger a workflow immediately. Sertn is useful because evidence can be created…

    Inference Labs @inference_labs ·
  5. #5

    NVIDIA now describes physical AI safety as something that has to span hardware, software, models, sensors, and deployment. That is important. A safe robot is not just a good model…

    Inference Labs @inference_labs ·
  6. #6

    Confidential computing protects workloads while they run. Verification answers a different question: did the expected workload actually run, and can we establish that continuously…

    Inference Labs @inference_labs ·

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