Sequence-to-Sequence Model
A neural network architecture that transforms one sequence into another, used for tasks like machine translation, text summarization, and speech-to-text where input and output lengths differ.
Sequence-to-sequence (Seq2Seq) models, introduced in 2014, use an encoder to process the input sequence and a decoder to generate the output sequence. This architecture powered Google Translate's neural upgrade in 2016, which improved translation quality by 60% for some language pairs. The original transformer paper was a Seq2Seq model. Modern variants like T5 frame all NLP tasks as sequence-to-sequence problems, enabling a single architecture to perform translation, summarization, question answering, and classification.
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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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