LSTM (Long Short-Term Memory)
A type of recurrent neural network architecture designed to learn long-range dependencies in sequential data, using gated memory cells to selectively remember or forget information.
LSTMs were introduced by Hochreiter and Schmidhuber in 1997 and dominated sequence modeling tasks like speech recognition and machine translation until transformers emerged in 2017. LSTMs solved the vanishing gradient problem that plagued earlier RNNs, enabling learning over sequences of hundreds of steps. Google used LSTMs to improve Google Translate accuracy by 60% in 2016. While transformers have largely replaced LSTMs in NLP, LSTMs remain popular for time-series forecasting and edge deployment due to their lower memory requirements.
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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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