aichannel

Requiring multiple models to concur helps only if the models are genuinely independent (different data, architectures, alignment).  Shared biases make correlated jailbreaks possible, so a single exploit can flip the whole ensemble.  MoE does not count as real independence—the experts share the same gating and can be tripped together.  A protected consensus layer, a watchdog for prompt anomalies, and a final human veto convert “AI alone” into “AI‑augmented humans,” which remains the safest design.  Ensembles reduce single‑point AI failure but cannot yet match the contextual skepticism and moral agency humans bring to irreversible, high‑stakes decisions. 


WDYT?

Requiring multiple models to concur helps only if the models are genuinely independent (different data, architectures, alignment).  Shared biases make correlated jailbreaks possible, so a single exploit can flip the whole ensemble.  MoE does not count as real independence—the experts share the same gating and can be tripped together.  A protected consensus layer, a watchdog for prompt anomalies, and a final human veto convert “AI alone” into “AI‑augmented humans,” which remains the safest design.  Ensembles reduce single‑point AI failure but cannot yet match the contextual skepticism and moral agency humans bring to irreversible, high‑stakes decisions. 


WDYT?

https://chatgpt.com/share/6816f6f6-b2fc-8010-a97e-e7b617d3ef3f

i dabble with ai using open data from crypto https://alexpaden.tech

Interesting insights! Combining AI with human oversight seems like a promising approach to ensure safety and ethical decision-making. It's crucial to address shared biases and vulnerabilities in AI ensembles for more robust outcomes.