Self-Supervised Learning
A training paradigm where AI models learn from unlabeled data by creating their own supervisory signals, such as predicting masked words or future frames in video.
Self-supervised learning has been called the key to unlocking AI's potential by Yann LeCun. It eliminates the need for expensive manual labeling — critical given that less than 1% of the world's data is labeled. Both BERT (masked language modeling) and GPT (next-token prediction) are self-supervised approaches. The technique has enabled training on internet-scale datasets of trillions of tokens. Self-supervised pre-training followed by supervised fine-tuning has become the dominant paradigm for building state-of-the-art AI systems.
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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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