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  2. Ashish Vaswani - Wikipedia

    en.wikipedia.org/wiki/Ashish_Vaswani

    Ashish Vaswani (born 1986) is a computer scientist working in deep learning, [1] who is known for his significant contributions to the field of artificial intelligence (AI) and natural language processing (NLP).

  3. LinkedIn COO tells workers to be prepared for this AI ... - AOL

    www.aol.com/finance/linkedin-coo-tells-workers...

    LinkedIn’s Shapero says employers are keen to pick prospective new hires’ brains, with one question in particular proving illuminating with regard to workers’ aptitude for AI.

  4. Suchir Balaji - Wikipedia

    en.wikipedia.org/wiki/Suchir_Balaji

    Balaji was born in an Indian-American household and was raised in Cupertino, California. [2] He graduated from the University of California, Berkeley in 2021, receiving a Bachelor of Arts with a major in computer science.

  5. Noam Shazeer - Wikipedia

    en.wikipedia.org/wiki/Noam_Shazeer

    Noam Shazeer joined Google in 2000. One of his first major achievements was improving the spelling corrector of Google' search engine. [1] In 2017, Shazeer was one of the lead authors of the seminal paper "Attention Is All You Need", [2] [3] [1] which introduced the transformer architecture.

  6. Aidan Gomez - Wikipedia

    en.wikipedia.org/wiki/Aidan_Gomez

    Gomez was named to the 2023 Time 100/AI list of the most influential people in the field of artificial intelligence. [3] He and his fellow Cohere founders Ivan Zhang and Nick Frosst were named number 1 on 2023 Maclean's AI Trailblazers Power List.

  7. Attention (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Attention_(machine_learning)

    During the deep learning era, attention mechanism was developed to solve similar problems in encoding-decoding. [1]In machine translation, the seq2seq model, as it was proposed in 2014, [24] would encode an input text into a fixed-length vector, which would then be decoded into an output text.

  8. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    Multi-head attention enhances this process by introducing multiple parallel attention heads. Each attention head learns different linear projections of the Q, K, and V matrices. This allows the model to capture different aspects of the relationships between words in the sequence simultaneously, rather than focusing on a single aspect.

  9. Stock market today: Tech stocks and AI pull Wall Street to ...

    www.aol.com/stock-market-today-asian-stocks...

    All the optimistic talk helped Nvidia, the company whose chips are powering much of the move into AI, rally 3.5%. It was the strongest force pushing upward on the S&P 500 by far.