One-Shot Learning
A machine learning approach where models can learn to recognize new categories or perform new tasks from just a single example, mimicking humans' ability to generalize from minimal experience.
One-shot learning is particularly important for applications where collecting large datasets is impractical, such as facial recognition for access control or identifying rare medical conditions. Siamese networks, which compare pairs of examples, were among the first successful one-shot architectures. Modern LLMs have brought one-shot learning to language tasks, where a single example in the prompt can guide the model's behavior. The approach is closely related to few-shot and zero-shot learning along the spectrum of data efficiency.
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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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