Translation and Artificial Intelligence

Translation and Artificial Intelligence

Artificial Intelligence (AI) and Deep Learning (DL) have undergone a massive growth in the last few years. As a result, companies all over have adopted Machine Translation (MT), to improve their bottom line. Machine Translation, having the distinction of being both excellent, yet cost effective, boasts of a combination that has resulted in it having the attention of everyone in the translation industry. To understand this storm of a tech revolution that is headed our way, we need to first understand the driving force behind it:

Big Data and Deep Learning:

Forward strides made in the world of Big Data have us processing information and data in a never before seen way. This coupled with the interest in deep learning, has allowed technology giants to make massive forward leaps. These companies are incentivized to find breakthroughs. Coming up with one, would allow them to capitalize on that technology and dominate their sector.

Majority of the companies though, struggle in their R&D. Exploring an uncharted technological territory has always been an expensive affair and advancements in the underlying technology of Machine Translation are only being held back by monetary constraints.

Innovations made in Big Data and Deep Learning so far have allowed us to perceive and analyse complicated patterns, and associations among those patterns. Huge growth in data has kept pace with the exponential growth of computing power. Much of the processing is hidden away— meaning it’s hard for us to see how Neural Machine Translation (NMT) makes its decisions.

Neural Machine Translation (NMT):

The most prominent user of NMT is Google. NMT, what Google uses for image and voice searches, is a relatively new branch. It only first gained everyone’s attention in 2014. We were earlier dependent on previous works of human translators for the machine to learn from. While NMT still feeds off a little from previous works, it combines it with Deep Learning to efficiently process this massive increase in volume of raw data.

Have a look here at a live demonstration of Google’s Pixel buds. Pixel buds are easy to use, interactive earpieces which make use of Google’s NMT driven ‘Google Translate’ to provide a real time translation experience:

Soon after its launch, Microsoft came out with ‘Microsoft Translator’— their own dictation app made for translating 60 languages. In real time. All these have presented us with more than enough evidence to suggest the massive reduction in human involvement that NMT will be bringing about. It is a game changer. But can they replace
human translators as of yet?

Human translators can make edits for localisation of your content. For instance, it might be deemed necessary to make edits to certain words and phrases— to alter the style and make sure the tone being conveyed to the audience, is the one originally intended. While ensuring its meaning hasn’t changed. They take into account the cultural nuances, helping the client better connect with their readers. They also go through the finished work to make the needful changes to help people find more helpful and relevant localized information.

The difference between traditional translation and machine translation will continue to bridge narrower—and we’re intent on finding out just how far this can go. We would love to hear your opinions and views on the subject. Do write to us at or ring us on 14156709780.