Rethinking Catastrophic Forgetting: The Key Lies in Interface Drift

Catastrophic forgetting has long been seen as a formidable challenge AI. The prevailing belief is that as models sequentially train on new tasks, they lose the representational features key for past tasks. However, recent research is challenging this perspective, suggesting the real issue might be more about interface drift.

The Interface Drift Phenomenon

In a series of controlled continual-learning experiments, researchers have uncovered that much of the apparent forgetting isn't due to the erasure of task-relevant computations. Instead, it seems to be an interface problem between internal model stages. This revelation is significant. If forgetting isn't about losing features, we're looking at a convergence of different factors that could reshape how we approach model training.

Transport Keys: A New Hope?

Enter the concept of transport keys. These are compact, task-specific interface-alignment operators. By employing a stitched evaluation protocol, researchers combined computations from an updated network with those of its predecessor. This process, mediated by transport keys, helped recover task performance. On split CIFAR-100 with a ResNet-style network, transport keys managed to bring back the majority of the original Task A performance after the model had moved on to Task B. Even a compact vision transformer showed similar recovery results. The AI-AI Venn diagram is getting thicker, and transport keys might just be the tool to navigate this convergence.

Why It Matters

This isn't just a technical curiosity. for how we develop AI systems capable of learning continuously without forgetting. If interface drift and not weight changes are the culprits, then perhaps the future of AI involves better indexing and re-accessing of latent computations rather than solely focusing on preserving weights.

So, why should you care? Consider this: if we can solve the interface drift issue, we'll be one step closer to creating truly autonomous systems that can learn as humans do, without losing past knowledge. If agents have wallets, who holds the keys? Perhaps the answer lies in refining how these agentic systems interface internally.

We're building the financial plumbing for machines, and understanding the nuances of catastrophic forgetting could be the next key step in this journey. As AI continues to evolve, the significance of interface drift and solutions like transport keys shouldn't be underestimated. After all, the compute layer needs a payment rail, and in this case, it might just need a better roadmap for information flow.