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."

Learning a new language is hard. And if there's a new alphabet involved--like there is with Japanese for English-speaking travelers--it's even harder. But technology is here to help. Google announced on Thursday a new translation feature that will make it easier for travelers who don't speak the language to go on a trip to a Japanese-speaking destination. Google Word Lens--a service available through Google Translate on Android and iOS devices--allows you to point your phone's camera at text, and it'll show the translation on the screen in real time.

As anyone who's encountered a badly translated text could tell you, not all translations are created equal. Some translations are smooth, fluent and sound like a poet wrote them; some are jerky, non-grammatical and awkward. When a machine is doing the translating, it's awfully easy to end up with a robotic-sounding text; as the state of the art in machine translation improves, though, a natural question to ask is: according to what measure? How do we quantify a "good" translation? Enter the BLEU score, which is the standard metric for quantifying the quality of a machine translation.

The dream of building computers or robots that communicate like humans has been with us for many decades now. And if market trends and investment levels are any guide, it's something we would really like to have. MarketsandMarkets says the natural language processing (NLP) industry will be worth $16.07 billion by 2021, growing at a rate of 16.1 percent, and deep learning is estimated to reach $1.7 billion by 2022, growing at a CAGR of 65.3 percent between 2016 and 2022. Of course, if you've played with any chatbots, you will know that it's a promise that is yet to be fulfilled. There's an "uncanny valley" where, at one end, we sense we're not talking to a real person and, at the other end, the machine just doesn't "get" what we mean.

Google CEO Sundar Pichai is offering a big new update that should affect anyone who's ever used Google's translation services. The new version will be rolling out in 2017 via Google Cloud, Pichai said. "We have improved our translation ability more in one single year than all our improvements over the last 10 years combined," Pichai told investors in a quarterly call, after parent company Alphabet reported mixed results. Like Alphabet, a lot of technology companies -- from IBM to to Amazon -- are talking about how machine learning and artificial intelligence algorithms are making their offerings more efficient. Until very recently, users have not always see those algorithms in action.

An illustrated artificial neural network (ANN) (CC BY SA 4.0 LearnDataSci via Wikimedia Commons) The system's'neural network' is advanced, but its abilities are being exaggerated by observers I have a fascination with translation, primarily because I have an interest in languages. I'm what I like to call "an aspiring polyglot," with the implication that I don't have time to practice (and reach complete fluency in) the few foreign languages I have some knowledge of, yet I give myself plenty of time to learn about said languages, how they are all different and by extension how they all work. As a technology- and startups-focused journalist, that makes the evermore popular topic of machine translation (MT) and "translation memory" fascinating, giving me the chance to cover companies like Austrian startup LingoHub (an essential service for apps) or Portuguese startup Unbabel (the next-level stuff they're doing is very cool). I can ask people how they communicate with lovers from other countries and report on developments like Google Translate's upgrade from "phrase-based machine translation" (PMT) with a "neural machine translation" (NMT). "Google Translate invented its own language to help it translate more effectively," wrote UX developer Gil Fewster on Medium, with the bold emphasis his own.

The mechanics behind machine learning and artificial intelligence can tax many a non-techy brain at the best of times. But the travel industry should be aware of how prevalent it will become in various processes (especially in customer service) and how it can also solve many problems. Here is a good example, and one which is at the heart of one of the fundamental aspects of the travel experience: language. In late-2016, Google quietly pushed out what some are considering to be one of the most "astonishing" updates in the field of machine learning. The ten-year-old Google Translate platform, which apparently deciphers 140 billion words every day across 103 languages for thousands of websites and search terms, switched from its previous system to something called Neural Machine Translation (Google NMT).