Few-Shot Learning
A machine learning paradigm where models learn to perform tasks from just a few examples, rather than requiring thousands or millions of labeled training samples.
Few-shot learning became a breakthrough capability of large language models like GPT-3, which demonstrated strong task performance from as few as 2-5 examples provided in the prompt. This capability dramatically reduced the cost and effort of adapting AI to new tasks — previously requiring thousands of labeled examples for fine-tuning. Few-shot learning is closely related to meta-learning (learning to learn) and is critical for enterprise applications where labeled data is scarce or expensive to obtain.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
ChatGPT
OpenAI's conversational AI assistant, launched in November 2022, which catalyzed the current generative AI boom by demonstrating the capabilities of large language models to a mainstream audience.
Fine-Tuning
The process of further training a pre-trained AI model on a specific, smaller dataset to specialize it for a particular task or domain, requiring far less compute than training from scratch.
Foundation Model
A large AI model trained on broad data that can be adapted to a wide range of downstream tasks — examples include GPT-4, Claude, Gemini, and Llama.
Frontier Model
The most capable and advanced AI models at any given time, typically trained with the largest compute budgets and achieving state-of-the-art performance on benchmarks.
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