What's so hard about continuous learning?

seangoedecke.com · 2 months ago · 14

AI models freeze their weights after deployment and cannot improve over time on user data, unlike human employees who grow more skilled. While the technical process of continuous learning is straightforward—running new inputs through existing training pipelines—the hard part is preventing models from degrading rather than improving, since training requires careful human supervision and succeeds only with careful hyperparameter tuning. Deploying continuous learning at scale remains impractical due to safety risks from weight poisoning attacks, inability to transfer learned knowledge to newer model versions, and the organizational burden of managing perpetually diverging model instances.