An artificial intelligence, AI, system adapted for data streaming applications, the AI system comprising: at least one processor; and memory having instructions stored thereon that, when executed by the at least one processor, cause the AI system to: process each input frame in a sequence of input frames through layers of a deep neural network, DNN, to produce a sequence of outputs, wherein the sequence of input frames denotes a sequence of ordered features describing information of an audio input, wherein the audio input is an acoustic signal or a speech utterance, and wherein at least some of the layers include a dual self-attention module having a dual causal and non-causal architecture attending to non-causal frames and causal frames; and render the sequence of outputs; wherein the causal frames attended to interpret a current frame are look-ahead frames, while the rest of the attended frames are the non-causal frames; wherein the dual self-attention module includes dual self-attention outputting a sequence of causal output frames derived without look-ahead and a sequence of non-causal output frames derived with the look-ahead of a predetermined size with respect to an input frame representing the present time; and wherein the sequence of outputs includes one or a combination of the causal output frames and the non-causal output frames; wherein the causal architecture of the dual self-attention of a current layer of the DNN transforms previous causal outputs determined by the causal architecture of the dual self-attention of a previous layer of the DNN into a sequence of causal keyframes, causal value frames, and causal query frames, wherein the non-causal architecture of the dual self-attention of the current layer of the DNN transforms previous non-causal outputs determined by the non-causal architecture of the dual self-attention of the previous layer of the DNN into a sequence of non-causal keyframes, non-causal value frames, and non-causal query frames, characterized in that the causal architecture produces current causal outputs by attending the causal query frames to causal keyframes combined with non-causal keyframes that are copied from the non-causal architecture to weight the causal value frames combined with non-causal value frames that are copied from the non-causal architecture; and the non-causal architecture produces current non-causal outputs by attending the non-causal query frames to the past and present non-causal keyframes combined with the causal look-ahead keyframes copied from the causal architecture to weight the non-causal past and present value frames combined with the causal look-ahead value frames copied from the causal architecture. process each input frame in a sequence of input frames through layers of a deep neural network, DNN, to produce a sequence of outputs, wherein the sequence of input frames denotes a sequence of ordered features describing information of an audio input, wherein the audio input is an acoustic signal or a speech utterance, and wherein at least some of the layers include a dual self-attention module having a dual causal and non-causal architecture attending to non-causal frames and causal frames; and render the sequence of outputs; wherein the causal frames attended to interpret a current frame are look-ahead frames, while the rest of the attended frames are the non-causal frames; wherein the dual self-attention module includes dual self-attention outputting a sequence of causal output frames derived without look-ahead and a sequence of non-causal output frames derived with the look-ahead of a predetermined size with respect to an input frame representing the present time; and wherein the sequence of outputs includes one or a combination of the causal output frames and the non-causal output frames; wherein the causal architecture of the dual self-attention of a current layer of the DNN transforms previous causal outputs determined by the causal architecture of the dual self-attention of a previous layer of the DNN into a sequence of causal keyframes, causal value frames, and causal query frames, wherein the non-causal architecture of the dual self-attention of the current layer of the DNN transforms previous non-causal outputs determined by the non-causal architecture of the dual self-attention of the previous layer of the DNN into a sequence of non-causal keyframes, non-causal value frames, and non-causal query frames, characterized in that the causal architecture produces current causal outputs by attending the causal query frames to causal keyframes combined with non-causal keyframes that are copied from the non-causal architecture to weight the causal value frames combined with non-causal value frames that are copied from the non-causal architecture; and the non-causal architecture produces current non-causal outputs by attending the non-causal query frames to the past and present non-causal keyframes combined with the causal look-ahead keyframes copied from the causal architecture to weight the non-causal past and present value frames combined with the causal look-ahead value frames copied from the causal architecture.