From few‐shot learning to data‐efficient intelligence

Abstract Modern artificial intelligence (AI) has achieved remarkable progress by scaling data, model size, and computation. Yet scaling alone does not resolve one of the most fundamental gaps between machine and human intelligence: the ability to learn, adapt, and act from limited experience. This limitation is especially visible when labeled examples, expert feedback, user interactions, or real‐world trials are scarce, costly, or dynamically changing. Humans can often grasp a new concept, adapt to a new goal, or personalize behavior after only a few examples or interactions, but many AI systems remain fragile under the same conditions. My research studies data‐efficient generalization: how learning systems can acquire useful priors, adapt to new tasks, and reuse limited experience under bounded supervision. In this article, I trace a continuous research path from few‐shot learning to meta‐learning, in‐context learning, and data‐efficient agentic learning. I first revisit few‐shot learning as a foundational problem and explain how data‐, model‐, and algorithm‐level priors make learning possible when empirical risk minimization is unreliable. I then describe our work on meta‐learning, where task‐level information and parameter‐efficient adaptation enable fast generalization. Next, I discuss in‐context learning in large language models (LLMs) as an implicit form of learning to learn, and show how its connection to classical meta‐learning provides both theoretical insight and practical guidance. Finally, I turn to large language model (LLM)‐based agents, where data efficiency expands beyond labels to include demonstrations, reasoning traces, tool‐use records, preferences, memory, and feedback. Across AI for science, biomedical knowledge discovery, recommendation, search, and personalization, the common thread is simple: more capable AI should extract more learning signal from less experience.