Zero shot machine learning

Zero shot machine learning is a paradigm where models perform tasks on classes or domains they have never encountered during training, leveraging learned representations and semantic knowledge to generalize beyond their training distribution. This approach enables immediate adaptation to new categories without requiring additional training examples by exploiting semantic embeddings, attribute-based learning, and cross-modal knowledge transfer. Zero shot learning employs techniques like mapping textual descriptions to visual features, utilizing pre-trained embeddings, and exploiting compositional understanding of concepts. Common implementations include vision-language models classifying unseen object categories, language models following novel instructions, and recommendation systems handling new items. The capability emerges from models learning generalizable patterns and relationships that transfer across domains. For AI agents, zero shot machine learning provides immediate deployment capabilities, rapid adaptation to unforeseen scenarios, and cost-effective scaling.