In 2016, artificial intelligence for translation took off as Google, Microsoft, and other tech giants made amazing breakthroughs in neural machine translation, or NMT. (Of course, we were so excited about the future of this new application of AI for translation services that we hosted a webinar on it.) Since then, true to experts’ predictions, machine learning and AI capabilities have grown exponentially and just keep on growing.
As AI takes the global marketing landscape by storm, this technology has allowed product development and marketing teams to transform their go-to-market strategies and increase the speed with which they dig into data. The ability to get more personal with customers means AI investments are expected to increase 300 percent this year.
But in which specific technologies and tools should product and marketing teams be investing to come up on top amongst global competitors? Let’s look at five of the most relevant translation tools today and how they’re being used.
- Speech recognition
One of the oldest applications of neural networks, modern speech recognition algorithms—otherwise known as voice-to-text transcription—have been around since the 1990s. Similar to image recognition, speech recognition used training data to identify the correct transcription of any given recording, making it useful for straightforward solutions such as dictation.
Today, advances in deep learning have turned speech recognition into an interaction gateway for interactive voice response (IVR) systems. One application most of us have experienced is the automatic call distributor, used by call centers to route calls or provide pre-recorded information according to the caller’s needs.
- Language generation
Often referred to as natural language generation or NLG, this subfield of AI utilizes computer data (the input) to produce text (the output). While this concept of interpreting ideas from data is not novel, computers are able to communicate ideas with increasing accuracy and scale. Using NLG, companies can turn large datasets or other assets into reports and summarized business intelligence insights, bringing a whole new level of understanding to employee and customer relationships.
- Virtual agents
We’re all familiar with Siri, Cortana, Alexa, and other intelligent personal assistants designed to help make consumers’ lives easier—but now the enterprise is catching up. As artificial agents become more advanced and customer expectations for self-service and automation continue to raise the bar, artificial agents can streamline a host of customer support tasks.
Automatic call distributors are one type of virtual agent but, of course, the shift to mobile and digital is upon us. Chat bots—which now offer the same personalized experience as live agents—are widely considered the future of customer service, and mobile messaging apps are fast becoming their most engaging channel.
- Machine learning
Machine learning is often confused with artificial intelligence as a whole, but it’s just one of many applications of AI. This is the science of “deep learning”—a term to describe how computers gain experiential intelligence over time from available or accumulated data.
Marketers are capitalizing on machine learning as an analytical tool to help find hidden patterns in unlimited amounts of data. For example, in e-commerce, retailers use consumers’ browsing and purchasing histories to make personalized product recommendations; in translation services, LSPs are using machine learning algorithms to teach computers how to turn source languages to target languages with more localized precision.
- Text analytics and natural language processing
In translation, natural language processing (NLP) is a statistical method that uses text analytics to better understand sentence structure, intent, and sentiment. This is not to be confused with natural language generation—while NLG turns structured data into text, NLP turns text into structured data.
NLP is another application of AI that can be used in virtual agents—like when we ask our phone for directions—but in the enterprise, NLP technology can be used to mine unstructured data. This is key, as marketing teams increasingly rely on unstructured data to make critical business decisions. Take social media sentiment analysis: NLP can uncover positive or negative emotion just from mentions of a brand, allowing for better segmentation and more targeted campaigns.
Could your product and marketing teams benefit from AI?
Vast amounts of unstructured data, coupled with the nuances of language and culture, can make it difficult for companies to develop clear global strategies. But by using artificial intelligence, companies can create unified customer experiences in several innovative ways. AI isn’t quite ready to replace human marketers yet, but in only a few years’ time, every global company should be prepared to compete in AI territory to survive.