Google's New Translation Software Powered By Brainlike Artificial Intelligence (sciencemag.org)

sciencehabit quotes a report from Science Magazine: Today, Google rolled out a new translation system that uses massive amounts of data and increased processing power to build more accurate translations. The new system, a deep learning model known as neural machine translation, effectively trains itself -- and reduces translation errors by up to 87%. When compared with Google's previous system, the neural machine translation system scores well with human reviewers. It was 58% more accurate at translating English into Chinese, and 87% more accurate at translating English into Spanish. As a result, the company is planning to slowly replace the system underlying all of its translation work -- one language at a time. The report adds: "The new method, reported today on the preprint server arXiv, uses a total of 16 processors to first transform words into a value known as a vector. What is a vector? 'We don't know exactly,' [Quoc Le, a Google research scientist in Mountain View, California, says.] But it represents how related one word is to every other word in the vast dictionary of training materials (2.5 billion sentence pairs for English and French; 500 million for English and Chinese). For example, 'dog' is more closely related to 'cat' than 'car,' and the name 'Barack Obama' is more closely related to 'Hillary Clinton' than the name for the country 'Vietnam.' The system uses vectors from the input language to come up with a list of possible translations that are ranked based on their probability of occurrence. Other features include a system of cross-checks that further increases accuracy and a special set of computations that speeds up processing time."

Comments

Re:Bullspit

[Anonymous Coward]

To be fair "deep learning" is a concept that could potentially spell the doom of traditional AI programs. The first problem for AI is that these learning networks don't use internal representations to do the "thinking", they perform analog computations which are just as mystifying as biological brains, hence the "what is a vector? We don't know" comment. The second problem is that in order to train one of the networks how to do something, you have to create the lessons that teach the subject you want it to learn, which is exactly what we already do for teaching children - something which is itself very hard to do. Symbol processing mechanical intelligence is a dying dream.