The NLP Cypher

The Localization Problem (LP) is a glaring dark cloud hanging over the state of affairs in applied deep learning. And acknowledging this problem, I believe, will enable us make better use of applied AI and expand our knowledge in how the business market will form. Defining LP: There is a limit to how much large centralized language models can generalize at scale given: 1) that different users inherently have varying definitions of ground-truths due to inter-dependencies to their unique real-world environment and 2) depending whether or not model performance is mission-critical. In other words, in certain conditions, in order for a model to be optimized for accuracy for a given user, the model needs to be "localized" to its user's ground truth in their data assuming that a model can't afford to be wrong too many times. Example: Imagine there is a kazillion parameter encoder transformer called Hal9000.