We all know that WHAT machines like LLMs reflect the quality and security of everything in their WHAT pile (that is, their training set). We invent cutesy names like “hallucinate” to cover up being dangerously wrong. However, ignoring or soft pedaling risk is often not the best way forward. Real risk management is about understanding risk and adjusting strategy and tactics accordingly.

In order to do better risk management in MLsec, we need to understand what’s going on inside the network. Which nodes (and node groups) do what, what is the nature of representation inside the network, can we spot wrongness before it comes out? Better yet, can we compare networks and adjust networks from the inside before we adopt them?

These are the sorts of things that Starseer is looking into. At BIML we are bullish on this technical approach.