The Continual Learning Problem
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Continual LearningMachine LearningArtificial Intelligence
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Continual Learning
Machine Learning
Artificial Intelligence
The article discusses the challenges of continual learning in machine learning, where models struggle to learn new information without forgetting previous knowledge, a problem that is also relevant to human learning.
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I especially like this taxonomy
> I think of continual learning as two subproblems:
> Generalization: given a piece of data (user feedback, a piece of experience, etc.), what update should we do to learn the “important bits” from that data?
> Forgetting/Integration: given a piece of data, how do we integrate it with what we already know?
My personal feeling is that generalization is a data issue: given a datapoint x, what are all the examples in the distribution of things that can be inferred from x? Maybe we can solve this with synthetic datagen. And forgetting might be solvable architecturally, e.g. with Cartridges (https://arxiv.org/abs/2506.06266) or something of that nature.