Google is not designing a self-driving car that can recognize street conditions by machine vision only. The automobile’s computer decision making algorithms rely on a massive amount of data collection about the automobile’s surroundings. Similarly, humans have been working on automating translations since the early 50s and much progress has happened since.
Translation and localization tools have come a long way in the last decade, despite that, no vendor can offers a 100% complete solution. This is why relying solely on localization tools to parse all file formats can be risky, especially when dealing with Wiki markup files.
If you find yourself needing translation services, before you go to Google Translate or start looking for a translator ask yourself the following four important questions:
Can language students be trusted with commercial translation tasks? You probably heard of the $15M Series B financing to fund a web startup that is promising to use language students along with an online crowdsourcing methodology to disrupt the translation industry.
As we welcome the New Year, tis become the season for predictions! Hence our predictions on how 2012 will impact the translation services industry to keep you in anticipation of how the year will unfold.
As translation consumers look for ways to reduce translation costs, the quality conscious is turning to a professional translation company offering automation in the process without removing the human element that is the most essential to the success of the project–the translator!
In the previous blog titled 5 Most Important Factors in Document Translation, we discussed the factors to consider for document translation before you select a professional translation company. We argued that price, although important, should not be the sole factor in the equation.
Recently, one of our blog entries tiled “Fuzzy Match or Fuzzy Math” caused interesting comments on the G11n – Globalization Professionals group on LinkedIn, where readers argued the equal benefits of the Levenshtein distance method in calculating fuzzy matching, compared to more refined algorithms.
When we were preparing to open our doors for business 15 years ago, we studied the technology landscape of the translation localization market and quickly realized the necessity of investing in robust Translation Memory (TM) tools. Since then, we rarely process projects that don’t leverage the use of these tools.
This week, a computer named Watson, built by IBM over a 4-year period, won the 3-day Jeopardy! contest against 2 champions, Ken Jennings and Brad Rutter. With the word “Think” displayed prominently behind the contestants in many natural languages of the world, I couldn’t help but think what kind of impact this invention will have on the translation localization industry.
Are linguistic proofs getting on your nerves? In previous blogs, we discussed the importance of performing in-country linguistic reviews/proofs and the critical steps to implement a successful process. Here, we discuss how to optimize the process by applying specific steps and technologies.
Imagine you are tasked to organize people in different countries, operating in different time zones, with different cultures and languages, not reporting to you, to participate in what will be a very laborious task. In addition, these people are committed to doing other jobs that normally take up all their work time. Finally, it is very likely that you cannot offer them incremental pay!
