When we think of artificial intelligence, our initial thoughts turn to rogue computers taking over the world or hijacking spacecraft for nefarious ends. The reality of AI tends to be much more business- and service-oriented that we realize.
Artificial intelligence is an umbrella term for computer systems that can perform like the human thought process.
The ultimate example would be a computer that can make decisions, develop ideas, communicate, and understand the world around it in a way that is indistinguishable from a person.
For everyday life, however, much of AI research and development has been focused on solving problems more efficiently, quickly, and accurately than individuals.
In the realm of translation, AI is being leveraged to assist across industries and improve results. Whether it’s used for eLearning translation or for interpreting patents, breakthrough translation technologies are now often coming as a result of advancements in AI. Machine translation, in particular, is becoming more accurate thanks to this technology.
Machine Translation Software in Global Communication
Google Translate is a good example of the evolution of AI for business purposes. Introduced in 2006, Google Translate employs an AI system called “machine learning.” Machine learning processes vast amounts of data through rules-based algorithms to answer questions.
As the solutions become more accurate, the program uses and refines its algorithms to address new questions that arise. In other words, it learns from experience. As a result, a process that worked previously can be applied to a new problem and improved upon.
Google Translate takes a simple concept–translating text from one language to another–and tries to produce that most accurate translations that have the same meaning in a target language. The highly in-demand software does not require human intervention other than verifying solutions or soliciting improvements.
The downside to machine translation has always been its lack of recognizing context. Try as these programs might, they have trouble analyzing underlying meaning or subtle differences in word choices that a human understands by nature.
For example, the difference between “bird” and “fowl” is one of degree, but machine translation software can often view them as the same.
For businesses that use Google Translate, a quality review of translations remains a part of their translation process.
Sometimes the review is performed in-house by a fluent staffer. More likely, they engage a language service provider (LSP) that can translate content for local markets with a high level of expertise.
An LSP can also provide marketing and business knowledge that takes into account local market rules and regulations, as well as cultural sensitivities.
Neural Networks Are the Key to Future Language Services and Translation
Unlike machine learning, which operates from a linear, rules-based approach, deep learning forms a neural network consisting of inter-related nodes of information. The data that passes through this network is processed much like the human brain processes information.
GNMT has the potential to understand context in ways that machine learning can’t. By processing millions of bits of data through several layers of analysis, GNMT and software like it can produce a more accurate translation.
It yields increasingly better results by training itself to learn, taking its component solutions to a problem and creating a final holistic one. For instance, Google Translate moves from English to a target language and back again.
To translate German to Hindi, each word passes into English on the way. GNMT, on the other hand, has learned something fundamental about language that allows it to go directly from German to Hindi, even if it had never done so before, a process called “zero shot translation.”
For businesses in global markets, GNMT represents new opportunities for effective communications strategies.
As deep learning technology becomes broadly available, translating business, legal, and marketing materials into foreign languages will become much more cheap, accurate, and timely.
At present, these technologies are focused on language translation. While the understanding of context might be rising, subject matter expertise remains an elusive goal.
LSPs with field experts in anything from patent filings, legal cases, and business regulations to marketing and advertising in local markets are still needed for the highest-quality translations.
That level of understanding, communication, and intelligence is firmly in the realm of human language translation services–for now.