
Machine-generated Text: What Changed?
by: Andy Thean There’s a 90s Britpop band whose song lyrics have always bugged me: Oasis. Each line of an Oasis song works fine in
by: Andy Thean There’s a 90s Britpop band whose song lyrics have always bugged me: Oasis. Each line of an Oasis song works fine in
Not to be crass about it, but are we even interested in what robots have to say? Once you’ve gotten a laugh out of hearing the robotic Siri say, “I can’t be your designated driver,” what’s left? Isn’t Siri kind of a killjoy, a shoddy electronic wash-out? Sure, we’ve spent the last four articles building up to a future of brilliant talking robots. But what can we realistically expect from our artificially intelligent friends?
In the previous articles in this series we saw how super-computers managed to beat humans at chess—and even at Jeopardy! We also saw how the Internet of Things embedded minicomputers everywhere, allowing us to use our voices for everything, from turning on TVs, to microwaving popcorn.
< Wordbee recently appointed a new CEO. Wordbee’s co-founder and former CEO José Vega is not going to rest on well-deserved laurels, though. He will
In the previous articles in this series, we discovered how machines learned to listen, learned to see, and even learned to speak (a bit). Progress was being made on all fronts—from greater computing power, to better data-processing, to fancier algorithms. But fluidly conversing with humans was still a pipe dream. That would soon evolve: the 2010s would prove to be a gamechanger.
We use standards every day, in all aspects of our lives. Some standards have been around for hundreds or even thousands of years. Think, for example, of weights and measures and how their differences and similarities affect us all. Think of electric plugs and outlets and the need to have a universal plug adapter on hand when traveling.
In this article we discuss the meaning of the terms Agile and Scrum in continuous software development and localization.
The field of Artificial Intelligence encompasses all possible approaches to simulating intelligence. Machine learning, one branch of AI, uses data and experience to train algorithms automatically. Deep learning, a sub-field of machine learning, in turn uses algorithms (called neural networks) to simulate the learning process.
If I were to ask, “Do LSPs play a role in in-country review?” you would probably reply with another question, “Why are you asking this?” Translation buyers that choose an in-country review usually have local offices branches or representatives in specific countries to whom they can entrust this task. And these buyers are typically large organizations. So, how can LSPs contribute to an efficient in-country review?
This is the first of the 3-part series “The Robot Spoke” by Dr. Patrice Caire, AI & Social Robotics Consulting Scientist. The Robot Spoke: But What
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