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The 2017 paper 'Attention Is All You Need' by Vaswani et al. marked a major paradigm shift in AI. Rather than clinging to tired methods like recurrence or convolutions, its Transformer design boldly flipped the script melding self-attention into a fresh take that not only remade natural language processing but also trickled over into computer vision, robotics, quirky multi-modal setups, and more 1. At its very core lies a refreshingly simple idea self-attention, which lets models dynamically figure out which bits of the input deserve extra focus instead of being bound by strict, linear routines. This clever tweak kicked the old, rigid rules to the curb and opened up levels of parallel processing that we hadn’t seen before. In many cases, this shift not only ramped up the training speed dramatically but also proved essential in our data-swamped world where speed and nimbleness really do make all the difference.
Ladu et al. (Tue,) studied this question.