Late one Friday night in early November, Jun Rekimoto, a distinguished professor of human-computer interaction at the University of Tokyo, was online preparing for a lecture when he began to notice some peculiar posts rolling in on social media. Apparently Google Translate, the company's popular machine-translation service, had suddenly and almost immeasurably improved. Rekimoto visited Translate himself and began to experiment with it. He had to go to sleep, but Translate refused to relax its grip on his imagination. Rekimoto wrote up his initial findings in a blog post.

Greece and Rome were, like many areas of the ancient world, multilingual, and so needed both translators and interpreters. My own thesis into English to Welsh translation – due to be published later this year – shows that a translator working to correct the output from machine translation makes for higher productivity and quicker translation. Well over 350,000 people speak Welsh every day, while local authorities across the UK are also translating into numerous other languages. Today, machine translation can create rough drafts of relatively simple language, and research shows that correcting this draft is usually more efficient than translation from scratch by a human.

"By far, the greatest danger of artificial intelligence is that people conclude too early that they understand it." Artificial intelligence (AI) is pushing the boundaries of human imagination. Machines today are capable of doing a lot of things that we could not imagine doing 20 years ago. AI has changed the way we look at learning and inventing. From drug discovery to sports analysis to protecting the oceans, AI has marked its presence everywhere.

"By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." Artificial Intelligence has been pushing the boundaries of human imagination. The machines today are capable of doing a lot of things that we could not imagine doing, 20 years back. Artificial Intelligence has changed the way we look at learning and inventing. From drug discovery to sports analysis to protecting the oceans, AI has marked its presence everywhere.

Imagine a world where everyone can perfectly understand each other. Language is translated as we speak, and awkward moments of trying to be understood are a thing of the past. This elusive idea is something that developers have been chasing for years. Free tools like Google Translate – which is used to translate over 100 billion words a day – along with other apps and hardware that claim to translate foreign languages as they are spoken are now available, but something is still missing. Yes, you can now buy earpiece technology reminiscent of the Hitchiker's Guide to the Galaxy babel fish – a bit of kit which claims to so a similar job to that a university-trained, professionally-experienced, multilingual translator – but it's really not that simple.

HPE and NVIDIA are delivering IT solutions with superhuman intelligence to harness the full power of AI and pioneer the next generation of HPC systems. In this evolving digital economy, data is the cornerstone of success. Big Data is redefining the way we think, act, and understand the world, and accelerating insight is the difference between making the next major discovery and missing it. The more information we can effectively capture, analyze, and act on, the more opportunities there are to drive technological advancements, ensure economic control, strengthen national security, and fuel scientific research. Organizations across all sectors are putting this data to work.

Much work, and many tools, are still needed to integrate artificial intelligence into the software engineering workflow, noted Peter Norvig, Google's director of research, speaking at the O'Reilly Artificial Intelligence conference in New York last week. Fundamentally, AI software is inherently different from other forms of widely used software, said Norvig, who is also a co-author of perhaps the most popular book of programming instruction for the field, Artificial Intelligence: A Modern Approach. "One way of looking at the traditional model of programming is to look at the programmer is a micro-manager, who tells a computer exactly how to do something step by step," he said. With AI, we should look at the programmer more as a teacher, rather than a micro-manager. This will require big changes in how programming is done, and the tools needed to program easily.

Many supervised learning tasks are emerged in dual forms, e.g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation. Two dual tasks have intrinsic connections with each other due to the probabilistic correlation between their models. This connection is, however, not effectively utilized today, since people usually train the models of two dual tasks separately and independently. In this work, we propose training the models of two dual tasks simultaneously, and explicitly exploiting the probabilistic correlation between them to regularize the training process. For ease of reference, we call the proposed approach \emph{dual supervised learning}. We demonstrate that dual supervised learning can improve the practical performances of both tasks, for various applications including machine translation, image processing, and sentiment analysis.

We propose a new neural sequence model training method in which the objective function is defined by $\alpha$-divergence. We demonstrate that the objective function generalizes the maximum-likelihood (ML)-based and reinforcement learning (RL)-based objective functions as special cases (i.e., ML corresponds to $\alpha \to 0$ and RL to $\alpha \to1$). We also show that the gradient of the objective function can be considered a mixture of ML- and RL-based objective gradients. The experimental results of a machine translation task show that minimizing the objective function with $\alpha > 0$ outperforms $\alpha \to 0$, which corresponds to ML-based methods.

It sounds like Amazon Web Services is getting ready to bring translation technology used on the Amazon.com CNBC reported Monday that AWS will likely announce the availability of a machine translation service before the big re:Invent user conference this November. AWS has been first among cloud rivals many times in the past when it comes to releasing new services for its customers, one of the many reasons why it enjoys a leading portion of the market for cloud services. But it's playing catch-up here: Google has been working on computer-assisted translation for almost a decade as part of its search technology, and offers a translation API through Google Cloud Platform for its customers. Microsoft also has an API for Azure customers. But Amazon does have a lot of experience in developing natural language processing technology, as AWS VP Swami Sivasubramanian explained earlier this month at our Cloud Tech Summit.