Decoding Google Gemini with Jeff Dean
Google DeepMind: The Podcast
Revolutionizing Language Processing: From Recurrence to Attention
Traditional recurrent models relied on an internal state that updated sequentially with each word processed, causing slow performance due to dependencies between steps. In contrast, transformer architectures introduced a simultaneous processing approach, preserving the state for each word and employing a learned attention mechanism to determine the relevance of previous words. This innovation allows for parallel processing of large volumes of text, significantly enhancing efficiency and scalability, making it 10 to 100 times faster than previous models.
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