How Machines Keep Memories: Smarter Replay to Stop Forgetting
Imagine a device that learns every day without losing what it learned before.
That problem—called continual learning—is tough because new data can wipe out old knowledge.
Many systems try to fix this by replaying old examples from a saved list, but they usually pick them at random.
The new idea is simple: pick the examples most likely to be hurt by the next update, not just any example.
This replay of important items helps the model remember what matters, and the result is less forgetting and better performance overall.
The team show how a few smart choices from memory cut mistakes down, and keep previous skills safe.
It works online too, so a model can learn in one pass over data and still hold on to past lessons.
Your phone, a car, even a home robot could keep improving without losing old skills.
This approach treats stored memory like a priority list, and that small change makes a big difference.
Read article comprehensive review in Paperium.net:
Online Continual Learning with Maximally Interfered Retrieval
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