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  2. 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.

  3. Attention Is All You Need - Wikipedia

    en.wikipedia.org/wiki/Attention_Is_All_You_Need

    The paper introduced a new deep learning architecture known as the transformer, based on the attention mechanism proposed in 2014 by Bahdanau et al. [4] It is considered a foundational [5] paper in modern artificial intelligence, as the transformer approach has become the main architecture of large language models like those based on GPT.

  4. Attention - Wikipedia

    en.wikipedia.org/wiki/Attention

    Attention is best described as the sustained focus of cognitive resources on information while filtering or ignoring extraneous information. Attention is a very basic function that often is a precursor to all other neurological/cognitive functions. As is frequently the case, clinical models of attention differ from investigation models.

  5. Attention AI experts: The White House wants you - AOL

    www.aol.com/finance/attention-ai-experts-white...

    In a move reminiscent of a wartime recruitment drive, the U.S. government is putting out the call for AI experts and taking steps to fast-track the hiring process. Attention AI experts: The White ...

  6. Pre-attentive processing - Wikipedia

    en.wikipedia.org/wiki/Pre-attentive_processing

    The ability to adequately filter information from pre-attentive processing to attentive processing is necessary for the normal development of social skills. [14] For acoustic pre-attentive processing, the temporal cortex was believed to be the main site of activation; however, recent evidence has indicated involvement of the frontal cortex as well.

  7. Test of everyday attention - Wikipedia

    en.wikipedia.org/wiki/Test_of_everyday_attention

    The Test of Everyday Attention (TEA) is designed to measure attention in adults age 18 through 80 years. The test comprises 8 subsets that represent everyday tasks and has three parallel forms. [ 1 ] It assess three aspects of attentional functioning: selective attention , sustained attention , and mental shifting .

  8. Attentional control - Wikipedia

    en.wikipedia.org/wiki/Attentional_control

    However, an active randomized controlled trial showed that a mobile-based mindfulness app with extensive self-assessment features may have long-term benefits for attentional control in healthy participants. [44] Mindfulness influences non-directed attention and other things like emotional well-being. [43]

  9. Trail Making Test - Wikipedia

    en.wikipedia.org/wiki/Trail_Making_Test

    The Trail Making Test is a neuropsychological test of visual attention and task switching. It has two parts, in which the subject is instructed to connect a set of 25 dots as quickly as possible while maintaining accuracy. [ 1 ]