A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder. The encoder/decoder architecture has obvious promise for machine translation, and has been successfully applied this way. Encoding an input to a small number of hidden nodes which can effectively be decoded to a matching string requires machine learning to learn an efficient representation of the essence of the strings. In addition to translation, seq2seq models have been used in a number of other NLP tasks such as summarization and image captioning. Related Links <ul> <li> <a href="https://google.github.io/seq2seq/">tf-seq2seq</a> </li> <li> <a href="https://arxiv.org/abs/1507.01053v1">Describing Multimedia Content using Attention-based Encoder--Decoder Networks</a> </li> <li> <a href= "https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Vinyals_Show_and_Tell_2015_CVPR_paper.pdf"> Show and Tell: A Neural Image Caption Generator</a> </li> <li> <a href="https://arxiv.org/pdf/1704.06485.pdf">Attend to You: Personalized Image Captioning with Context Sequence Memory Networks</a> </li> </ul>

Human vs Machine Transcription

The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.