Attention Mechanism
A neural network component that allows a model to focus on the most relevant parts of an input sequence when producing an output, enabling context-aware processing of data.
The attention mechanism was introduced in 2014 for machine translation and became the core of the transformer architecture in 2017. Self-attention allows every token in a sequence to attend to every other token, capturing long-range dependencies that earlier architectures like RNNs struggled with. Multi-head attention runs several attention functions in parallel, enabling the model to capture different types of relationships simultaneously. Attention is the key innovation behind all modern LLMs.
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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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