Deep learning models, including language models like ChatGPT, have a knowledge cut-off date which limits their access to up-to-date information, affecting their effectiveness.
Shibhansh Dohare emphasizes that loss of plasticity indicates a crucial failure in AI systems—without the capability to learn new information, these systems cannot adapt.
In order to maintain both stability and plasticity in AI models, a delicate balance must be struck as models that prioritize stability risk losing their learning efficacy.
Continual learning in AI systems hinges on maintaining plasticity; without it, models cannot adapt to evolving datasets and situations, impacting their overall utility.
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