Since originally writing this article, many people with far more expertise in these fields than myself have indicated that, while impressive, what Google have achieved is evolutionary, not revolutionary. In the very least, it's fair to say that I'm guilty of anthropomorphising in parts of the text. I've left the article's content unchanged, because I think it's interesting to compare the gut reaction I had with the subsequent comments from experts in the field. I strongly encourage readers to browse the comments beneath the version of this piece published on Medium.com In the closing weeks of 2016, Google published an article which quietly sailed under most people's radar.

As part of an ongoing effort within Microsoft to improve the accuracy of artificial intelligence (AI) systems, Microsoft Translator is publicly releasing a set of data that includes multiple conversations between bilingual speakers who are speaking French, German and English. This corpus, which was produced by Microsoft using bilingual speakers, aims to create a standard by which people can measure how well their conversational speech translation systems work. It can serve as a standardized data set for testing bilingual conversational speech translation systems such as the Microsoft Translator live feature and Skype Translator. Christian Federmann, a senior program manager working with the Microsoft Translator team, said there aren't as many standardized data sets for testing bilingual conversational speech translation systems. "You need high-quality data in order to have high-quality testing," Federmann said.

Morphology in unbalanced languages remains a big challenge in the context of machine translation. In this paper, we propose to de-couple machine translation from morphology generation in order to better deal with the problem. We investigate the morphology simplification with a reasonable trade-off between expected gain and generation complexity. For the Chinese-Spanish task, optimum morphological simplification is in gender and number. For this purpose, we design a new classification architecture which, compared to other standard machine learning techniques, obtains the best results. This proposed neural-based architecture consists of several layers: an embedding, a convolutional followed by a recurrent neural network and, finally, ends with sigmoid and softmax layers. We obtain classification results over 98% accuracy in gender classification, over 93% in number classification, and an overall translation improvement of 0.7 METEOR.

In an ideal world, everyone would speak the same language or at least be able to understand other languages fluently. But we don't live in that ideal world, yet. We do, however, live, work, and interact in a global society, where effective communication with co-workers is vital, and machine translation software has become a must for any company that works on internationally. There are many types of machine-based translation software. The two types most talked about assist translators and those who can do the translation themselves.

Intelligent keyboards can support writing by suggesting content. Certain types of phrases, when offered as suggestions, may be systematically chosen more often than their frequency in a corpus of text would predict. In order to generate those types of suggestions, we collected a dataset of how human authors responded to suggestions offered to them during open-ended writing tasks. We present an offline strategy for evaluating suggestions that enables us to learn the parameters of an improved suggestion generation policy without the expense of collecting additional data under that policy. We validate the approach by simulation and on human data by demonstrating improvement in held-out suggestion acceptance rate. Our approach can be applied to other scenarios where what is typical is not necessarily what is desirable.

We've talked before about the rapid advances being made in the area of speech recognition. It's only a matter of time before companies like Doppler Labs augment our hearing such that we're able to experience real-time language translation. You'll soon be able to call him on that but only if you feel with 100% certainty that the translation is accurate. Why wouldn't language translation be accurate you ask? The simple answer here is one word.

I mentioned in a blog last year that we are at the dawn of a new age of artificial intelligence (A.I.). And 2017 certainly is the beginning of a world that is rapidly embracing A.I. The halls at CES were filled with talking devices, many powered by the same presence, Alexa, Amazon's slowly evolving virtual assistant. There were several conversations about the impact the impending robot revolution would have on our lives, jobs and future occupations. IDC predicts that spending on A.I. will grow from $8 billion to $47 billion by 2020.

This article was written in collaboration with Gowling WLG. Gowling WLG is one of world's largest law firms and advises clients from offices in many of the world's most dynamic markets. It was recently ranked as the second most innovative firm in Europe in the prestigious FT Innovative Lawyer Awards 2016. "Gowling WLG is one of world's largest law firms and advises clients from offices in many of the world's most dynamic markets. It was recently ranked as the second most innovative firm in Europe in the prestigious FT Innovative Lawyer Awards 2016."

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. First, he compared a few sentences from two published versions of "The Great Gatsby," Takashi Nozaki's 1957 translation and Haruki Murakami's more recent iteration, with what this new Google Translate was able to produce.

Yesterday I read a fascinating article shared on LinkedIn written by Gil Fewster, Creative Technologist. The mind-blowing A.I. announcement from Google that you probably missed. I read it and I'm not going to lie, I was actually fully freaked out. Gil mentioned that at the end of last year Google'quietly' announced a new discovery for Google Translate, and here's what it does. "Google Translate invented its own language to help it translate more effectively. What's more, nobody told it to. It didn't develop a language (or interlingua, as Google call it), because it was coded to. It developed a new language because the software determined over time that this was the most efficient way to solve the problem of translation."