👉 Read computing is an advanced approach to natural language processing (NLP) that aims to bridge the gap between human reading and machine understanding. It involves training models not just on raw text data but also on the cognitive processes of reading, such as comprehension, inference, and contextual understanding. This method uses large datasets of text paired with annotations that reflect how humans interpret and derive meaning from written content, including the relationships between sentences, the flow of ideas, and the overall coherence of a passage. By mimicking human reading behavior, read computing models can generate more contextually relevant and coherent text responses, making them particularly useful for applications like chatbots, content summarization, and automated writing assistance. This approach leverages deep learning techniques, often involving transformer architectures like BERT or GPT, to capture the nuances of human language and improve the quality of machine-generated text.
