A neural network architecture that uses self-attention to process sequential data, forming the backbone of all modern large language models.
Introduced in the 2017 paper 'Attention Is All You Need,' transformers process entire input sequences in parallel rather than word-by-word. The self-attention mechanism allows each token to weigh the relevance of every other token, enabling nuanced understanding of context and relationships.
GPT-4, Claude, Gemini, and BERT are all transformer-based models used for text generation, classification, and semantic search.
Understanding transformers helps businesses evaluate AI capabilities realistically and choose the right model size/cost tradeoff for their use case.
The field focused on ensuring AI systems behave as intended, don't cause harm, and remain aligned wi...
A neural network component that allows models to focus on the most relevant parts of the input when ...
AI systems that can interpret and understand visual information from images and video.
Numerical representations of text that capture semantic meaning for AI processing.
An AI model trained on vast amounts of text data capable of understanding and generating human-like ...
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