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AI in Continual Learning

Learning without forgetting

AI adapts to new tasks while retaining knowledge from previous tasks.

Encoding information for reasoning

AI represents facts and rules to enable logical inference and problem-solving.

Structured learning approach

AI trains models using a sequence of tasks from easy to hard for better performance.

Structured domain knowledge

AI uses ontologies to organize concepts and relationships for semantic understanding.

Learning without explicit labels

AI generates supervisory signals from data itself to improve representation learning.

Rule-based decision-making

Expert Systems apply predefined rules to solve domain-specific problems.

Competing networks for generation

GANs consist of generator and discriminator networks that create realistic synthetic data.

Simulating human thought processes

Cognitive Computing combines AI and cognitive science to mimic human reasoning.

Probabilistic generative models

VAEs learn latent representations for generating new data samples.