"There are so many smart people across the world, and the language barrier prevents them from having the confidence to communicate," says Shyam Gollakota, a professor at the University of Washington, who worked on the project. "My mom has such incredible ideas when she's speaking in Telugu, but it's so hard for her to communicate with people in the US when she visits from India. We think this kind of system could be transformative for people like her." While there are plenty of other live AI translation systems out there, such as the one running on Meta's Ray-Ban smart glasses, they focus on a single speaker, not multiple people speaking at once, and deliver robotic-sounding automated translations. The new system is designed to work with existing, off-the shelf noise-canceling headphones that have microphones, plugged into a laptop powered by Apple's M2 silicon chip, which can support neural networks.

In an effort to break down language barriers, Meta has created a new AI translator that can convert spoken languages such as Hokkien into spoken English. Hokkien, a dialect of southern Min Chinese, is primarily spoken and lacks a standard writing system, making it difficult to develop translation tools for it. The open-source translation system, which is part of Meta's Universal Speech Translator (UST) project, has made significant progress in this challenge. The company, formerly known as Facebook, hopes that this, along with other AI methods in development, will eventually allow for real-time speech-to-speech translation across hundreds of languages, including spoken languages. Languages such as Hokkien are difficult to translate because machine translation tools need a large amount of written text to train on, and such languages lack a widely used writing system.

Neural machine translation (NMT), or AI techniques that can translate between languages, is in widespread use today owing to its robustness and versatility. But it's been shown that NMT systems can be manipulated if provided prompts containing certain words, phrases, or alphanumeric symbols. For example, in 2015, Google fixed a bug that caused Google Translate to offer homophobic slurs like "poof" and "queen" to those translating the word "gay" from English into Spanish, French, or Portuguese. In another glitch, Reddit users discovered that typing repeated words like "dog" into Translate and asking the system to translate into English yielded "doomsday predictions." A new study from researchers at the University of Melbourne, Facebook, Twitter, and Amazon suggests that NMT systems are even more vulnerable than previously believed.

On Dec. 14, China's Ministry of Industry and Information Technology (MIIT) published its plan to develop China's artificial intelligence industry over the next three years. The MIIT report declares China's intention to gain an international competitive advantage in AI by 2020. What's in the report: The MIIT report identifies eight key AI development goals. The systems should also be able to support face recognition of people from different ethnicities. The devices should also detect user intent with an accuracy rating of over 90 percent.

Google is one of the leading providers of artificial intelligence-assisted language translation, and the company now says a new technique for doing so is vastly improving the results. The company's AI team calls it the Google Neural Machine Translation system, or GNMT, and it initially provided a less resource-intensive way to ingest a sentence in one language and produce that same sentence in another language. Instead of digesting each word or phrase as a standalone unit, as prior methods do, GNMT takes in the entire sentence as a whole. "The advantage of this approach is that it requires fewer engineering design choices than previous Phrase-Based translation systems," writes Quoc V. Le and Mike Schuster, researchers on the Google Brain team. When the technique was first employed, it was able to match the accuracy of those existing translation systems.