Description
On Wednesday’s show, the DAS crew focused on why measuring AI performance is becoming harder as systems move into real-time, multi-modal, and physical environments. The discussion centered on the limits of traditional benchmarks, why aggregate metrics fail to capture real behavior, and how AI evaluation breaks down once models operate continuously instead of in test snapshots. The crew also talked through real-world sensing, instrumentation, and why perception, context, and interpretation matter more than raw scores. The back half of the show explored how this affects trust, accountability, and how organizations should rethink validation as AI systems scale. Key Points Discussed Traditional AI benchmarks fail in real-time and continuous environments Aggregate metrics hide edge cases and failure modes Measuring perception and interpretation is harder than measuring output Physical and sensor-driven AI exposes new evaluation gaps Real-world context matters more than static test performance AI systems behave differently under live conditions Trust requires observability, not just scores Organizations need new measurement frameworks for deployed AI Timestamps and Topics 00:00:17 👋 Opening and framing the measurement problem 00:05:10 📊 Why benchmarks worked before and why they fail now 00:11:45 ⏱️ Real-time measurement and continuous systems 00:18:30 🌍 Context, sensing, and physical world complexity 00:26:05 🔍 Aggregate metrics vs individual behavior 00:33:40 ⚠️ Hidden failures and edge cases 00:41:15 🧠 Interpretation, perception, and meaning 00:48:50 🔁 Observability and system instrumentation 00:56:10 📉 Why scores don’t equal trust 01:03:20 🔮 Rethinking validation as AI scales 01:07:40 🏁 Closing and what didn’t make the agenda