Digital Shield – Battle Against Deceptive Content.

Digital Shield – Battle Against Deceptive Content.

Digital Shield – Battle Against Deceptive Content.

Globally, people are overriding content at a progressively fast speed, and as a consequence, the demand for translated content has reached an all-time high levels. From blogs to e-books to on-demand TV shows and e-commerce websites, today’s companies must translate large quantities of content — as quick as possible — to consumers all around the world.

2020 was the year known for language technology. It was a year where people from all over the world were following the latest language tech trends, hence resulting in the continuous rise of AI. We witnessed new AI models being released as well as noteworthy projects centered around content creation and summarization. While some clinched up to these developments, others mostly remained cynical.

In such fast-growing digital world and the need for quick localization, mostly businesses fall into the sting of translating content the wrong way. An application for AI, called the “Digital Shield”, plays an important part of the battle against deceptive and false content.

Deep learning is a subsection of AI and machine learning that customs multidimensional artificial neural networks to provide state-of-the-art precision in tasks such as object detection, speech recognition, language translation, and others. Deep learning in contrast with traditional machine can automatically learn demonstrations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Their extremely flexible architectures can learn directly from raw data and can increase their predictive accuracy when provided with more data. Deep learning has led to many modern breakthroughs in AI such as Google DeepMind’s AlphaGo, self-driving cars, intelligent voice assistants and many more.

In the fast-growing world of digitization and globalization, technology companies are investing heavily in machine translation. This investment and recent progressions in deep learning have generated major enhancements in translation quality. According to Google, swapping to deep learning produced a 60% increase in translation accuracy compared to the phrase-based approach previously used in Google Translate. Today, Google and Microsoft can translate over 100 different languages and are approaching human-level accuracy for many of them.

Deep learning language technology has transmuted numerous fields in the recent years, ranging from computer vision to artificial intelligence in games. With such great advancements, the field of Machine Translation has shifted to the use of deep-learning neural-based methods, which swapped prior methods of rule-based systems or statistical phrase-based methods. This elimination of past independence assumptions is the key reason behind the histrionic enhancement of translation quality. As a consequence, neural translation even succeeded in narrowing the gap in human-translation quality on isolated sentences.

With the help of growing technology in the world of language translation, Digital Shield helps in maintaining the right tone and providing accurate translation of given content. Whether content is sparse or overabundant, “Digital Shield” a part of AI can help feed text in the software and the technology will automatically generate content that matches the original style, tone, and intent of the writer. The technology can also create basic analogies, write recipes from scratch, complete basic code, and the list goes on and on.

Even with too much content the AI models are capable to examine the text and select the most significant points. Some technology providers debate that there is too much data, and too little time to process the translation task. For this reason, the year 2020 has seen numerous new AI projects focused on speeding up content perception through text summarization. Though text summarization startups have had limited achievement in creating comprehensible summaries, larger companies are now getting in on the act, and the outcomes may turn out to be quite a lot more encouraging. In December 2020, Facebook announced a project codenamed “TLDR” (too long, didn’t read) with the aim of plummeting articles to bullet points and providing narration. It also features a virtual assistant to answer questions. Similar technology is also being used to automatically summarize comments on social media platforms for the tenacity of summarizing and attaining commenter intuitions. It also has applications for speech identification. Speech recognition and text handling are now seen as behaviors to mend information reclamation and analysis that may be needed at a later stage in the game.

Whether translation of content is done by a human or by a machine, content can be controlling and misleading. In fact, it can be fashioned in such a way that it’s equivalent to brainwashing. The notion of forming such a model as “digital shield” is to develop a software that is capable of detecting verbal hypnosis in any text. And, since brainwashing can take a number of numerous forms, the addition of analytical tool — called the emotional detector is used as a proprietary invention based on practical expertise in psychology and advertising. The most thought-provoking feature about Digital Shield is that it can be used by content consumers to cross check if they are being influenced by the content advertised and displayed. But, it’s also a technique for content creators to evaluate presentations and publications and to regulate them consequently. With increasing AI-generated content becoming more common — the fear of aggravating “fake news” is becoming a problem but with Digital Shield we can now help keep AI content in proper check.

The increase in demand for localization has led to the technological advancement in the translation industry. The use of AI tools and machine translation is expected to even rise more further with the mounting need for translation and localization of content for seamless global communication.

The fast-growing translation need for quick, affordable and zero-misleading content, leads to high demand for localization of content, and availability of massive volume of Big Data contributes to the fast growth of machine translation.

With time, the AI tools are projected to progress, resulting in the overall development of the translation practice. These tools can support professional translation companies to meet short deadlines and provide large volumes of translated data without any compromise on the quality and providing translation of content in the exact tone as the source language.