As machine learning products continue to target the enterprise, they are diverging into two channels: those that are becoming increasingly meta in order to use machine learning itself to improve machine learning predictive capacity; and those that focus on becoming more granular by addressing specific problems facing specific verticals. And while the latest batch of machine learning products across both these channels may reduce some pain points for data science in the business environment, experts warn that machine learning can't solve two issues regardless of the predictive capacity of the new tools: Last year, new machine learning market entrants focused on speeding up processes around mapping the context that a machine learning algorithm would need to understand in order to predict needs in a given business situation. For example, if a voice translation machine learning product was listening in to a customer service call in order to more quickly help the call operator surface the appropriate solution-based content, the first job of the machine learning product would be to create an ontology that understands the customer call context: things like product codes, industry-specific language, brand items and other niche vocabulary. Products like MindMeld and MonkeyLearn built automatic ontology-creators so the resulting machine learning algorithm had a higher degree of accuracy without the end user first having to enter a whole heap of business-specific data into the product to make it work. Others, like Lingo24, created their own specific vertically-based machine learning engines for industries like banking and IT so that their machine learning translation service could apply the right phrase model to the right situation.

When I attended translation courses, I was assigned to write a commentary on George F. Will's column Reading, Writing and Rationality on the Newsweek issue of March 17, 1986. Even then, with no Internet, and television as the dominant media, students were urged to read. That day, green activists were giving a demonstration of solar energy applications in a public park near the school, and our professor opened his lesson with a witty comment about the experiment he had witnessed during his lunch break. The history of innovation is full of inventors and manufacturers unable to understand the impact and actual use of their own work. Similarly, most innovations do not necessarily use the most recent and sophisticated technology, with their makers showing an outstanding capacity of interpreting and accelerating the transformations that are already underway.

The aim of their collaboration is to achieve machine-based translation between the languages of the European Union so that comprehensible texts are achieved for as many language combinations as possible. Two of the EU-funded research projects are being led by the Saarbrücken computer linguist Josef van Genabith. Anyone who wants to learn Finnish has to be prepared to deal with a complex grammar that includes fifteen different cases. The grammatical cases are marked in part by appending syllables to nouns resulting in a dizzying array of word forms and expressive possibilities. "Teaching a computer to understand all these grammatical nuances and to translate them correctly into another language is exceptionally difficult," says Josef van Genabith, Professor of Translation-Oriented Language Technologies at Saarland University and a Scientific Director at the German Research Center for Artificial Intelligence (DFKI). His team is therefore following a different path.

More than 1.5 billion people use Facebook. And only half speak English. The rest speak so many dozens of other languages, effectively silo'd off from the English speakers and, in many cases, from each other. If you stumble onto a Facebook post in a foreign language, Facebook lets you instantly translate it--in a semi-effective way. And beginning today, millions of people will have the option of instantly translating their own posts into any one of 44 other languages, so that they will automatically show up in your News Feed in your native tongue.

More than one billion people use Facebook every day. If you picked two at random, they most likely wouldn't speak each other's language. But the company thinks they should still be able to socialize with each other. A new feature uses automatic translation software to help people post Facebook updates in multiple languages at the same time. Users viewing a post made that way are shown the version most likely to be readable to them in light of their own past language use and settings.

More than 1.5 billion people use Facebook. And only half speak English. The rest speak so many dozens of other languages, effectively silo'd off from the English speakers and, in many cases, from each other. If you stumble onto a Facebook post in a foreign language, Facebook lets you instantly translate it--in a semi-effective way. And beginning today, millions of people will have the option of instantly translating their own posts into any one of 44 other languages, so that they will automatically show up in your News Feed in your native tongue.

This is how it works. When you're writing a post, you'll see some text asking you if you want the post to appear in another language. Click it, and you can then choose which languages you want from a drop-down list. It'll then automatically fill out separate messages with the appropriate machine-translated text -- the sort that you'd find on Google Translate, for example. You can go with these if you like, but if you're multilingual and that machine-translation isn't up to snuff, you can actually go in and fix it up so it reads correctly in those other languages. Doing this also helps teach Facebook's machine-translation to get better over time.

I have been invited to write a book chapter on lexical choice for translators (contact me if you want to see a preprint). To get acquainted on this audience different from my usual computer science I read a few papers on professional translators use of technology. Two of them are quite interesting and I recommend them not only because they make for a good read and they have implications outside translation: Translation Skill-sets in a Machine-translation Age by Anthony Pym (2013) and Is Machine Translation Post-editing Worth the Effort?: A Survey of Research into Post-editing and Effort by Maarit Koponen (2016). This search finished by reading a short ebook by researchers at the MIT Center for Digital Business titled Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy. In that book plus the papers there's this call for humans, if we want to remain employed, to hybridize our work and to seek out ways to work with the computer as some sort of partnership.

Neural Machine Translation (MT) has reached state-of-the-art results. However, one of the main challenges that neural MT still faces is dealing with very large vocabularies and morphologically rich languages. In this paper, we propose a neural MT system using character-based embeddings in combination with convolutional and highway layers to replace the standard lookup-based word representations. The resulting unlimited-vocabulary and affix-aware source word embeddings are tested in a state-of-the-art neural MT based on an attention-based bidirectional recurrent neural network. The proposed MT scheme provides improved results even when the source language is not morphologically rich. Improvements up to 3 BLEU points are obtained in the German-English WMT task.

Imagine a far flung land where you can catch a ride from the Jackie Chan bus stop to a restaurant called Translate Server Error, and enjoy a hearty feast of children sandwiches and wife cake all washed down with some evil water. If such a rich lunch gets stuck in your gnashers, you'll be pleased to know there are plenty of Methodists on hand to remove your teeth. And if by this point you've had enough of the bus, fly home in style on a wide-boiled aircraft. But whatever you do, please remember that when you land at the airport, eating the carpet is strictly prohibited. No, I haven't gone mad.