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).

Enterprise cloud usage has been in the forefront of big players for the past few years. Amazon, IBM, Google and Microsoft are expanding their offerings to serve better the enterprise users and their needs. Google announced a set of machine learning based services focused on enterprise users. Similar to upcoming Amazon EC2's Elastic GPUs and Microsoft's Azure N-Series, powered by NVidia GPUs, Google will soon offer cloud based GPUs with per minute billing focused on Machine Learning tasks. Google slashed pricing for its Cloud Vision API to 1/5, offering face, label, OCR, company logos, explicit content and landmark and image properties recognition through off the shelf algorithms and their API.

In the closing weeks of 2016, Google published an article which quietly sailed under most people's radar. Which is a shame, because the article may just be the most astonishing thing about machine learning that I read last year. Don't feel bad if you missed it. Not only was the article competing with the pre-Christmas rush most of us were navigating, it was also tucked away on Google's Research Blog beneath the geektastic headline Zero-Shot Translation with Google's Multilingual Neural Machine Translation System. It doesn't exactly scream must read, does it?

The capacity of a neural network to absorb information is limited by its number of parameters. Conditional computation, where parts of the network are active on a per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice, however, there are significant algorithmic and performance challenges. In this work, we address these challenges and finally realize the promise of conditional computation, achieving greater than 1000x improvements in model capacity with only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to thousands of feed-forward sub-networks. A trainable gating network determines a sparse combination of these experts to use for each example. We apply the MoE to the tasks of language modeling and machine translation, where model capacity is critical for absorbing the vast quantities of knowledge available in the training corpora. We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.

Mr. HUANG talked about the challenges facing the industry in the era of AI and how it could translate those challenges into opportunities. He pointed out that the emergence of neural machine translation, the application of speech interactive technology and the rapid development of the Internet Era had a huge impact on traditional human translation. Though the industry has begun to realize the importance of AI and other technologies, the mechanism and platform to promote their application is still lacking. Mr. HUANG believed that governments, language colleges, language and AI service providers, experts and freelance translators should join hands in exploring this topic and advise the industry on development in the new era. In addition to the guidance of government and industrial organizations, an effective cooperative mechanism shall be established to ensure the maximum synergy between colleges, research institutes and language service providers.