Artificial intelligence in translation

Artificial intelligence in translation

Human interaction with machines has experienced a great leap forward in recent years, largely driven by artificial intelligence (AI). From smart homes to self-driving cars, AI has become a seamless part of our daily lives. Voice interactions play a key role in many of these technological advances, most notably in language translation. Here, AI enables instant translation across a number of mediums: text, voice, images and even street signs. The technology works by recognizing individual words, then leveraging similarities in how various languages express the relationships between those words. we have to deal with huge volumes of data at a fast pace, which calls for a different approach. Today’s AI language translation tools leverage a deep learning technique called neural machine translation (NMT). Based on artificially created neural nets, this approach translates whole sentences rather than just individual words, making it faster and more accurate. Use of artificial intelligence in language translation comes with many benefits. One is the ability to deliver instant results across a wide range of languages. These tools are integrated into the websites we use daily. They provide added convenience and a more streamlined experience when interacting with international products, services and people.

What’s more, language translation tools are usually free of charge and easily accessible to anyone with a computer or smartphone and an internet connection. Many tools are now also available offline, opening up new possibilities for traveling or doing business in areas where internet connections are less reliable. 

Key challenges of AI-driven language translation 

Accuracy has long been one of the biggest concerns in language translation and the world of AI is no different. In fact it’s even more critical here. Deep learning, for all its perceived glamor, still has certain limitations. 

Researchers from Google spoke candidly about some of these limitations. In particular, they pointed out that simply upscaling the neural net and adding more data doesn’t necessarily mean it can replicate human abilities. 

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