By eliminating the friction of the traditional translation process, devices like Translate One2One will not only remove one of the biggest challenges for professionals when meeting and collaborating between cultures, but also offers enormous potential for communities around the world,

Machine learning (ML) may sound like a daunting concept to anyone unfamiliar with it, some may believe it to lead to outlandish ideas about machines poised to enslave mankind. Fortunately this isn't what ML is, it's basically a major advancement in the development of Information Technology (IT). For ML to benefit an organisation it first has to understand the full benefit and limitations it offers. While the principles of ML are rather simple and intuitive to grasp, it does require the use of specific statistical and IT skills that few people currently possess. To understand the idea think of a common and rather mundane language translation service – like Google Translate – this helped me realise the transformative potential of ML.

We present a recurrent encoder-decoder deep neural network architecture that directly translates speech in one language into text in another. The model does not explicitly transcribe the speech into text in the source language, nor does it require supervision from the ground truth source language transcription during training. We apply a slightly modified sequence-to-sequence with attention architecture that has previously been used for speech recognition and show that it can be repurposed for this more complex task, illustrating the power of attention-based models. A single model trained end-to-end obtains state-of-the-art performance on the Fisher Callhome Spanish-English speech translation task, outperforming a cascade of independently trained sequence-to-sequence speech recognition and machine translation models by 1.8 BLEU points on the Fisher test set. In addition, we find that making use of the training data in both languages by multi-task training sequence-to-sequence speech translation and recognition models with a shared encoder network can improve performance by a further 1.4 BLEU points.

In an increasingly technological world, it is essential for companies to be at the forefront of innovation as they strive to stay ahead of the competition. This is certainly the case in the e-gaming industry. Inherently driven by data, dominance in the sector is a case of who can crunch its data at real-time speeds to provide the best possible customer experience. Those leading the way in sportsbook and e-gaming are now beginning to understand the importance of harnessing machine learning and predictive data analytics to stay competitive. In the next few years, more machine learning will be integrated into these systems, with a growing focus on deep learning or artificial intelligence, and the commercial value it can add to the business.

Mumbai: Researchers are increasingly striving to help machines translate words from one language to another the way professional translators would. This implies that machines must understand the context of words and sentences, and make sense of idioms, phrases and jokes. However, despite the fact that billions of words are being translated daily by multilingual machine translation services like Google Translate, Microsoft Translator, Systran's Pure Neural Machine Translator, WordLingo, SDL FreeTranslation, China's Baidu, Russia's Yandex or Babel Fish, machines have a long way to go before they can function as fluently as humans do when speaking in, and translating, different tongues. Barak Turovsky, product lead at Google Translate--a free multilingual machine translation service from Google Inc.--understands this dilemma well. "Today, translation by machines can be likened to my five-year-old son speaking Russian. Since I speak fluent Russian, I know the mistakes he makes and how he forms words," he says.

Recently, the attention mechanism plays a key role to achieve high performance for Neural Machine Translation models. However, as it computes a score function for the encoder states in all positions at each decoding step, the attention model greatly increases the computational complexity. In this paper, we investigate the adequate vision span of attention models in the context of machine translation, by proposing a novel attention framework that is capable of reducing redundant score computation dynamically. The term "vision span" means a window of the encoder states considered by the attention model in one step. In our experiments, we found that the average window size of vision span can be reduced by over 50% with modest loss in accuracy on English-Japanese and German-English translation tasks.% This results indicate that the conventional attention mechanism performs a significant amount of redundant computation.

The UK election this Thursday will be shaped by artificial intelligence. Artificial intelligence is being used to fake vocal political support on social media in the run up to the UK election. Then there's social media targeting. Huge swathes of marginalised people could be empowered by automated translation tools.

What if you could talk to anyone, regardless of the language they spoke? The personal universal translator has long been a dream of science fiction, but that dream is now a reality: Microsoft Translator translates in-person conversations in real time with up to 100 speakers using their own smartphone, tablet, or PC.

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Machine translation systems that convert sign language into text and back again are helping people who are deaf or have difficulty hearing to communicate with those who cannot sign. KinTrans, a start-up based in Dallas, Texas, is trialling its technology in a bank and government offices in the United Arab Emirates, and plans to install it in more places over the next couple of months. SignAll, a company based in Budapest, Hungary, will begin its own trials next year. KinTrans uses a 3D camera to track the movement of a person's hands as they sign words. A sign language user can approach a bank teller and sign to the KinTrans camera that they'd like assistance, for example.