Seems like the term Machine Learning is popping up in mainstream media as the next big thing. The fact is, however, that Machine Learning went mainstream a long time ago. You don't think so? Check your mobile phone. Chances are you've been using and benefiting from Machine Learning algorithms all this time without even knowing it. In this blog post, I go through some of the many apps on your mobile phone that use Machine Learning algorithms to make recommendations, get you to your destination quickly and safely, improve your photos, tell you what song you're listening to and more.

Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks: Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model. On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.

Chances are you mostly need a translator app on your phone while you are traveling. But that's also when you are most likely to not have any connectivity. While most translation apps still work when they are offline, they can't use the sophisticated -- and computationally intense -- machine learning algorithms in the cloud that typically power them. Until now, that was also the case for the Microsoft Translator app on Amazon Fire, Android and iOS, but starting today, the app will actually run a slightly modified neural translation when offline (though iOS users may still have to wait a few days, as the update still has to be approved by Apple). What's interesting about this is that Microsoft is able to do this on virtually any modern phone and that there is no need for a custom AI chip in them.

Title: When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation? Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

Microsoft has announced that its Translator app for Android, iOS and Amazon Fire tablets will now support AI translations even when the device is offline. Translator comes really handy when you traveling to a foreign country and you are not familiar with the local language. But since the connectivity is mostly limited when you traveling, users are left with basic translations on their mobile applications. With the new udpate for Microsoft Translator, the Redmond-based software major wants to change that narrative altogether. Microsoft says its Translator app will be able to use sophisticated algorithms and computational power for translation even when the device is not connected to internet.

The Microsoft Translator app's most accurate translations, powered by the company's emphasis on artificial intelligence, are now available offline. Microsoft Translator first allowed users to download entire languages for offline translations starting in 2016. But this new update focuses on AI-powered "neural translation technology," which the company says produces translations that are 23 percent more accurate than the previously available offline packs. The technology is also open to third-party developers, allowing them to integrate AI translations into their apps. Offline capabilities are available now on Android devices and iOS devices by the end of the week.

Localization (also referred to as "l10n") is the process of adapting a product or content to a specific geographic locale or market with the aim of giving it the look and feel of having been created specifically for a target market, no matter their language, culture, or location. Language translation and cultural adaptation are obviously a big part of localization, and globally visible companies heavily rely on sophisticated technology and localization engineering to get the job done. Localization is a complex process--some of it is automated by tools, but much of it is still a human-driven, manual undertaking. So it's no wonder that recent AI advances in Machine Translation (MT), as well as the allure of automated one-click translation platforms have caused a stir in the translation and localization industry and some fear that this development might spell doom for language professionals and perhaps even be the end of language service providers (LSPs) altogether. So, is complete push-button localization imminent or hyped?

Like many translation apps, Microsoft Translator has only used AI to decipher phrases while you have an internet connection. That's not much help if you're on a vacation in a place where mobile data is just a distant memory. Well, you won't have to sacrifice quality for much longer -- Microsoft has released offline language packs for Translator (currently on Android, iOS and Amazon Fire devices) that use AI for translation when you're offline regardless of your hardware. The move not only provides higher quality translations, but shrinks the size of the language packs by half. If you're a jetsetter, you might not have to shuffle language packs whenever you visit a new country.

It's been known for some time that Moore's law is dying. Transistor densities don't quite rise at the same rates that they used to [1]. For this reason, the last decade of computer scientists have been trained to not expect their code to get faster without effort. Multicore systems for CPU remain hard to program and often require significant tuning on the part of a skilled programmer to achieve. At the same time, the growth of mobile computing has lead to a Cambrian explosion in the broad applications of deployed programs.

Often, there is no "perfect" answer. While much is logical, many elements are harder to explain, untethered as they are to any fixed set of rules. For instance, when is a thought expressed with an indicative vs a subjunctive mood? When to use polite vs casual phrasing in languages such as Korean or Japanese? How to articulate an expression that doesn't exist in a target language?