Attention Is All You Need

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Attention Is All You Need,[1] a groundbreaking 2017[2][3] research paper by eight Google scientists, revolutionised machine learning by introducing the transformer architecture. Building upon Bahdanau et al.'s 2014 attention mechanism concept, this work laid the foundation for modern Artificial intelligence systems.[4][5] Though initially aimed at enhancing Seq2seq models for machine translation, the paper remarkably predicted its broader applications, including question-answering systems and today’s multimodal generative AI.[1] Transformers now dominate AI architectures, particularly in large language models, making this paper a key driver of the ongoing AI revolution.

References[edit]

  1. ↑ 1.0 1.1 Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Łukasz; Polosukhin, Illia (Dec 2017). "Attention is All you Need" (PDF). In I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett (ed.). 31st Conference on Neural Information Processing Systems (NIPS). Advances in Neural Information Processing Systems. Vol. 30. Curran Associates, Inc. arXiv:1706.03762.
  2. ↑ Love, Julia (2023-07-10). "AI Researcher Who Helped Write Landmark Paper Is Leaving Google". Bloomberg News. Retrieved 2024-04-01.
  3. ↑ Goldman, Sharon (2024-03-20). "'Attention is All You Need' creators look beyond Transformers for AI at Nvidia GTC: 'The world needs something better'". VentureBeat. Retrieved 2024-04-01.
  4. ↑ Bahdanau, Dzmitry; Cho, Kyunghyun; Bengio, Yoshua (2016-05-19). "Neural Machine Translation by Jointly Learning to Align and Translate". arXiv:1409.0473 [cs.CL].
  5. ↑ Shinde, Gitanjali; Wasatkar, Namrata; Mahalle, Parikshit (2024-06-06). Data-Centric Artificial Intelligence for Multidisciplinary Applications. CRC Press. p. 75. ISBN 9781040031131.
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