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Risk modeling for advanced AI systems is hampered by a lack of rigorous quantitative methods, leaving critical societal assessments in the dark.
Governance gaps in AI systems can render control measures ineffective, highlighting the urgent need for integrated risk management strategies.
Covertly exfiltrating AI workload results is rendered infeasible, thanks to a new architecture that eliminates the need for mutually trusted processors.
