Every Measurement Has a Scale
A physics principle reveals a critical flaw in how we measure and define things.
Deep Dive
A blog post argues that no measurement is meaningful unless it remains stable under small, unobservable changes. The core idea is to replace yes/no questions, like 'is this a minimum?', with quantitative ones stated at a specific scale. This thinking tool is applied to concepts like loss landscapes in AI and modularity. It emphasizes that precision requires explicitly stating the scale of observation to avoid misleading conclusions.
Why It Matters
This reframes how we define success and failure in complex systems like AI models.