Zero-Shot Learning
A model's ability to perform tasks it was never explicitly trained on, using its general knowledge and reasoning capabilities to handle entirely novel situations without any task-specific examples.
Zero-shot learning has become one of the most impressive capabilities of large language models. GPT-4 can perform tasks like sentiment analysis, translation, and code generation without any task-specific training examples. CLIP demonstrated zero-shot image classification by learning from 400 million image-text pairs. Zero-shot capability generally improves with model scale and is considered an emergent property of sufficiently large models. It has dramatically reduced the barrier to deploying AI for new applications.
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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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