In the 18th century home craft textile workers were displaced by mass production in organized factories. Some of these displaced workers destroyed knitting equipment in these new factories. In England, Ned Ludd, also known as Capitain Ludd or King Ludd, the mythical leader of the Luddites, was the inspiration of a widespread effort to destroy factory equipment and return to the cottage industry that had sustained local economies before the development of the steam engine and the industrial revolution. Industrialization destroyed early cottage economies, but today, advanced technology and the slowing of semiconductor scaling may bring about a renaissance of cottage industry. This is the result of minimizing the cost of technology development with new cloud-based tools and internet enabled supply chains.

Currently, when the Google Translate apps for iOS and Android has access to the internet, its translations are far superior to those it produces when it's offline. That's because the offline translations are phrase-based, meaning they use an older machine translation technique than the machine learning-powered systems in the cloud that the app has access to when it's online. Google is now rolling out offline Neural Machine Translation (NMT) support for 59 languages in the Translate apps. Today, only a small number of users will see the updated offline translations, but it will roll out to all users within the next few weeks. The list of supported languages consists of a wide range of languages.

Being tongue-tied on holiday could become a thing of the past thanks to a major update to Google's Translate feature. Two years ago it introduced translating AI called'neural machine translation (NMT)' to improve the accuracy of translations - and now this is available offline. Phones running on both iOS and Android will be able to take advantage of the new update and it will be available in 59 languages. Two years ago Google introduced translating AI called'neural machine translation (NMT)' to improve the accuracy of translations - and now this is available offline Google says that each language won't take up too much storage - just 35 to 45Mb. It also uses broader context to help determine the most relevant translation, which it then rearranges and adjusts to sound more like a real person speaking with proper grammar.

Google has gone a long way in making our lives easier and this is evident from the fact that we use some of its products everyday in our lives. Google Translate is one of those products from the company that is boon for travelers going to places where they don't speak the native language. Google Translate works just fine in terms of technicality, but there is always room for improvement. Google made sure that people can access its Translate feature even in areas with patchy, or no network with the offline feature. And in a recent blog post Google declared that the offline services is about to get better with the integration of neural machine translation or NMT.

Neural machine translations (NMT) in the Google Translate application now works offline on both iPhone and Android, product manager Julie Cattiau announced in a blog post on Tuesday. Prior to this update, translations in the app were phrase-based, meaning sentences would be translated in chunks. The update will roll out in the next few weeks, according to the post. The offline functionality could be useful for international business travelers, especially those who travel to regions with spotty Wi-Fi or poor signal. The NMT functionality could also ease communication due to its more accurate translations.

Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora show that we can achieve promising results against a competitive baseline by removing the need of specific feature engineering or additional external resources. To justify the utility of our approach, we extract sentence pairs from Wikipedia articles to train machine translation systems and show significant improvements in translation performance.

We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training.

Google isn't going to sit idly by while Microsoft brings AI-based offline translation to your phone. The company is rolling out internet-free neural machine translation to its Translate apps for Android and iOS, promising much more accurate language conversion when you don't have the luxury of data. The initial release covers 58 languages, including a slew of European and Indian languages as well as common translation targets like Arabic, Chinese and Japanese. Despite the improved accuracy, the app shouldn't chew up too much of your valuable device space. Each language takes about 30MB to 40MB, Google said.

Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy, but lacks consideration of computational resource use. We propose the Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA uses a policy network to process the network embeddings to generate new configurations. We demonstrate RENA on image recognition and keyword spotting (KWS) problems. RENA can find novel architectures that achieve high performance even with tight resource constraints. For CIFAR10, it achieves 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size is less than 3M parameters. For Google Speech Commands Dataset, RENA achieves the state-of-the-art accuracy without resource constraints, and it outperforms the optimized architectures with tight resource constraints.

Neural language models (NLMs) have recently gained a renewed interest by achieving state-of-the-art performance across many natural language processing (NLP) tasks. However, NLMs are very computationally demanding largely due to the computational cost of the softmax layer over a large vocabulary. We observe that, in decoding of many NLP tasks, only the probabilities of the top-K hypotheses need to be calculated preciously and K is often much smaller than the vocabulary size. This paper proposes a novel softmax layer approximation algorithm, called Fast Graph Decoder (FGD), which quickly identifies, for a given context, a set of K words that are most likely to occur according to a NLM. We demonstrate that FGD reduces the decoding time by an order of magnitude while attaining close to the full softmax baseline accuracy on neural machine translation and language modeling tasks. We also prove the theoretical guarantee on the softmax approximation quality.