Recurrent Neural Network (RNN)
A neural network architecture designed for sequential data that maintains a hidden state across time steps, allowing it to process inputs of variable length and capture temporal patterns.
RNNs were the dominant architecture for sequence tasks like language modeling, speech recognition, and time-series prediction before transformers. They process data one step at a time, maintaining a memory of previous inputs through their hidden state. Variants like LSTM and GRU addressed the vanishing gradient problem that limited basic RNNs. While transformers have largely replaced RNNs for NLP tasks, recurrent architectures remain competitive for streaming applications and are experiencing a revival through state-space models like Mamba.
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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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