The Unreasonable Effectiveness of Recurrent Neural Networks (2015)
If this were written today, Karpathy would have to call it "The Unreasonable Effectiveness of Convolutions". Since 2015, convolutions, causal or dilated convolutions, and especially convolutions with attention like the Transformer, have made remarkable inroads onto RNN territory and are now SOTA for most (all?) sequence-related tasks. Apparently RNNs just don't make very good use of that recurrency & hidden memory, and the non-locality & easy optimization of convolutions allow for much better performance through faster training & bigger models. Who knew?