AI Governance: Rethinking Compute Thresholds In the rapidly advancing field of AI, it's crucial to ensure safe and ethical deployment. Risks associated with powerful AI systems are a key concern for policymakers and researchers. One common approach has been to set computational power thresholds for training AI models, assuming that higher compute power means greater risk. However, this approach is limited and ineffective. A better strategy involves a dynamic and comprehensive evaluation of AI systems, considering multiple factors that influence their risk profile. This includes redefining metrics like FLOP, considering additional dimensions of AI performance and risk, and implementing adaptive thresholds that can adjust to the evolving AI landscape. Research shows that fixed compute thresholds often overlook significant risks associated with smaller, highly optimized AI models. Smaller models, when optimized, can match the performance of much larger ones, highlighting the limitations of using compute thresholds as the sole governance tool for AI. To embrace AI in your company, use Cohere AI's paper to transform your operations. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually. For AI KPI management advice, connect with us at hello@itinai.com. Discover how AI can redefine your sales processes and customer engagement at itinai.com. Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom
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