You may rapidly translate words as they occur in your Facebook News Feed by pressing a button. Facebook provides a platform for communication with millions of individuals who speak different languages in addition to the millions of people who speak your language. It almost does, at least. Facebook comes with a warning, like so many other internet translation services: Its translations don’t always make sense.
But Facebook is attempting to remove that fairly important proviso, much like a number of other internet firms. This morning, a document revealing a novel method that may hasten the development of machine translation not only within Facebook but also throughout the internet was published by the company’s main artificial intelligence lab. According to Facebook’s experiments, their method—which is based on picture recognition—generates translations that are superior to the state of the art and significantly more effective than other approaches. This might potentially result in translations that are even finer.
It is an “impressive achievement,” particularly because it can train translation models more quickly than previous systems, according to Christopher Manning, a professor of machine translation at Stanford University who evaluated the study. Additionally, Facebook reports that its developers are already implementing this method on the company’s social network, which is used by more than 1.8 billion people worldwide.
The strategy used by Facebook is based on neural networks, sophisticated mathematical computers that can learn tasks by analyzing enormous volumes of data. This generic method has quickly changed everything over the last several years, including image identification, speech recognition, and internet search. It is currently revolutionizing the machine translation industry. Google announced a new translation system last autumn that outperformed prior models and was entirely powered by neural networks. Many other businesses and researchers, most notably Microsoft and Chinese web giant Baidu, are moving in the same way.
According to John Tinsley, CEO of Dublin-based Iconic Translation Machines, a provider of translation technology, “we’ve seen more improvements over the past two years than we have seen in the past decade.”
But compared to the majority of the other major competitors, Facebook has a somewhat different strategy. It employs convolutional neural networks, a method developed by eminent deep learning expert Yann LeCun, who currently heads Facebook’s AI unit. A convolutional neural network can analyze several distinct parts of a sentence simultaneously before arranging those parts into a logical hierarchy rather than sequentially analyzing each part of a sentence one at a time.
The convolutional neural network is a time-tested concept that has been shown to be incredibly successful in identifying objects in pictures. And others, notably researchers at DeepMind, a Google AI lab with offices in London, have investigated such networks as a fundamental machine translation approach. However, Manning claims that Facebook’s translation tool is the most spectacular example so far.
The business claims its technology is around nine times more efficient than previous neural network-based approaches, even if the system is only slightly more accurate than systems like the one Google released in the autumn. Convolutional neural networks are superior at simultaneously processing several dataset components. According to Manning, it is possible to do simultaneous computing on various phrase components. It’s not necessary to move things ahead word by word.
Facebook can now train its systems with a lot less computational resources. Jaime Carbonell, the director of the Language Technologies Institute at Carnegie Mellon University, claims that this enables the organization to make better use of the hardware in its data centers and, in principle, advance the technology much more quickly. This can be a little benefit in some circumstances, he claims. “But it might be a huge benefit,”
The skill may also be advanced with the aid of others. Similar to Google before it, Facebook is not only releasing a paper outlining its new system but is also open-sourcing the software engine that powers it, making the code available to everyone in the globe. Even models that it has already trained on its own data are being shared. This is a component of a bigger initiative by the top websites to openly disclose their AI research. It implies that translation will advance far more swiftly online, not only on Facebook.
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