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Policy Primer - The Limits of Thresholds

Cohere

Prominent AI governance frameworks around the world have specified thresholds based on the amount of computing power used to train an AI model, measured in floating-point operations (FLOPs).

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Research Cohere

ResearchPolicy Primer - Efficient AICohere ResearchContrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashionCohere ResearchOn the Limitations of Compute Thresholds as a Governance StrategyCohere ResearchUnderstanding and Mitigating Language Confusion in LLMsCohere ResearchFAIR-Ensemble: When Fairness Naturally Emerges From Deep EnsemblingCohere ResearchMetadata Archaeology: Unearthing Data Subsets by Leveraging Training DynamicsCohere BlogsEmpower Your Team with Policy as CodePulumi BlogsNew Policy as Code Capabilities with CrossGuardPulumi ResourcesGo Wiki: PortingPolicy - The Go Programming LanguageGo BlogsPolicy as Code for Any Cloud ProviderPulumi BlogsAchieve security and compliance goals with policy guardrails in Docker ScoutDocker BlogsIntroducing Origin Shield Concurrency Limits and QueueingBunny ResourcesDEPLOYMENT_DELETEDVercel BlogsPolicy Comes to Team and Enterprise, with New Out-of-the-box Policies and Management ExperiencePulumi BlogsIntroducing Usage Limits for Pulumi NeoPulumi ResourcesFetch directive - GlossaryDeveloper Mozilla ResourcesNavigation directive - GlossaryDeveloper Mozilla ResourcesReporting directive - GlossaryDeveloper Mozilla BlogsPolicy-as-Code: Flexible Kubernetes governance with KyvernoCncf ResourcesMigrate to AI Gateway Using Your Coding AgentVercel