Verification

Summary

Dynamic AI systems face a hidden failure mode: they can learn from their own mistakes. If every output is allowed into memory, stochastic errors do not stay local they accumulate.

In earlier posts, I argued that AI systems should not be trusted to enforce their own correctness.

Modern models are stochastic. They produce correct outputs, partially correct outputs, and completely incorrect outputs, but they do not reliably distinguish between them. That means a system that stores everything it generates will eventually learn from its own mistakes.

🚀 Summary

When you can measure what you are speaking about… you know something about it; but when you cannot measure it… your knowledge is of a meagre and unsatisfactory kind. Lord Kelvin

Remember that time you spent an hour with an AI, and in one perfect response, it solved a problem you’d been stuck on for weeks? Where is that answer now? Lost in a scroll of chat history, a fleeting moment of brilliance that vanished as quickly as it appeared. This post is about how to make that moment permanent, and turn it into an intelligence that amplifies everything you do.