The latest scare story around the rise of robots is that within 120 years all human jobs will be automated. If that study from Oxford University is to be believed, we're just 3 to 4 generations away from perpetual holiday. The report goes on to predict when AI will outperform humans and -- more interestingly -- how. Some aspects will be of genuine concern to certain industries: AI will be a better driver than human heavy goods vehicles drivers by 2027, AI will write better novels than we can by 2049, and, closest to today, AI will be better at translation by 2024. AI has the potential to significantly reshape the translation sector, as it's doing to many other industries already. However, given that the last time human translators were pitted against machine translation (in February) that 90 percent of the automated translation was judged "grammatically awkward," that is a bold prediction.

Chinese professional Go player Ke Jie preparing to make a move during the second game of a match against Google's AlphaGo in May 2017. Artificial intelligence (AI), once described as a technology with permanent potential, has come of age in the past decade. Propelled by massively parallel computer systems, huge datasets, and better algorithms, AI has brought a number of important applications, such as image- and speech-recognition and autonomous vehicle navigation, to near-human levels of performance. Now, AI experts say, a wave of even newer technology may enable systems to understand and react to the world in ways that traditionally have been seen as the sole province of human beings. These technologies include algorithms that model human intuition and make predictions in the face of incomplete knowledge, systems that learn without being pre-trained with labeled data, systems that transfer knowledge gained in one domain to another, hybrid systems that combine two or more approaches, and more powerful and energy-efficient hardware specialized for AI.

In this paper, we study a new learning paradigm for Neural Machine Translation (NMT). Instead of maximizing the likelihood of the human translation as in previous works, we minimize the distinction between human translation and the translation given by an NMT model. To achieve this goal, inspired by the recent success of generative adversarial networks (GANs), we employ an adversarial training architecture and name it as Adversarial-NMT. In Adversarial-NMT, the training of the NMT model is assisted by an adversary, which is an elaborately designed Convolutional Neural Network (CNN). The goal of the adversary is to differentiate the translation result generated by the NMT model from that by human. The goal of the NMT model is to produce high quality translations so as to cheat the adversary. A policy gradient method is leveraged to co-train the NMT model and the adversary. Experimental results on English$\rightarrow$French and German$\rightarrow$English translation tasks show that Adversarial-NMT can achieve significantly better translation quality than several strong baselines.

The Institute of Formal and Applied Linguistics (UFAL) is seeking a candidate for a one-year post-doc position in the area of neural machine translation (NMT). The exact topic will be determined based on the candidate's interests, e.g. A PhD degree in computational linguistic, artificial intelligence or a related field is required. Experience with neural MT, Linux and cluster environment (SGE), and/or general deep learning and GPU computation is a bonus.

Deep learning yields great results across many fields, from speech recognition, image classification, to translation. But for each problem, getting a deep model to work well involves research into the architecture and a long period of tuning. We present a single model that yields good results on a number of problems spanning multiple domains. In particular, this single model is trained concurrently on ImageNet, multiple translation tasks, image captioning (COCO dataset), a speech recognition corpus, and an English parsing task. Our model architecture incorporates building blocks from multiple domains. It contains convolutional layers, an attention mechanism, and sparsely-gated layers. Each of these computational blocks is crucial for a subset of the tasks we train on. Interestingly, even if a block is not crucial for a task, we observe that adding it never hurts performance and in most cases improves it on all tasks. We also show that tasks with less data benefit largely from joint training with other tasks, while performance on large tasks degrades only slightly if at all.

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.