fastText_multilingual

ntic 'thought vector' layer of an Imagenet trained Alexnet, plug in a RNN sentence generator using word2vec and ( some over simplification ... ) you get a convincing image captioner [6]. The thought vectors are akin to high level representations of the world and can cross modalities . Text to Images using thought Vectors ( from hnnews discussion [7] ) So the vectors of though are in some way a an AI mentalese or encoding of a symbolic representation of the world derived from the data and can ( again drastic over simplification ) transfer modalities and even between previously unlinked languages [8] Also see Anything2Vec https://gab41.lab41.org/anything2vec-e99ec0dc186 [1] https://youtu.be/izrG86jycck?t=25m58s [2] The paper Geoff Hinton is reffering to : Sequence to Sequence Learning with Neural Networks by Ilya Sutskever, Oriol Vinyals, Quoc V. Le https://arxiv.org/abs/1409.3215 [3] Efficient Estimation of Word Representations in Vector Space by Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean https://arxiv.org/abs/1301.3781 [4] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala https://arxiv.org/abs/1511.06434 [5] https://code.facebook.com/posts/1587249151575490/a-path-to-u... [6] SF Machine Learning: Automated Image Captioning with ConvNets and Recurrent Nets by Karpathy https://youtu.be/ZkY7fAoaNcg?t=38m31s [7] https://news.ycombinator.com/item?id=12366684 [8] https://github.com/Babylonpartners/fastText_multilingual