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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."
I am just terrified right now.
Wait until Google Research Scientists learn about matrices...
You've gone off on a tangent. It's polarizing. Stop it. Right now.
I've fallen off your lawn, and I can't get up.
Sounds an awful lot like the WordNet similarity vector which is commonly used in semantic analysis and is a measure of the 'relatedness' of words - http://search.cpan.org/dist/Wo...
They're doing context-aware translation based on massive well organized training sets and clever search algorithms. Woo.
Neither the Slashdot summary nor TFA contains a URL to where we can try this now rolled out new translator. Does this imply it's already used by translate.google.com? If so, I didn't notice any improvements, yet.
This AI hype has to stop. Neural networks are nothing like how the brain works. We have known that since 1975 at least! The only thing more annoying than a space nutter is an AI nutter.
There you go again.
AI-assisted translation is only going to get better and better and better as time goes on. It won't happen tomorrow or next week or next month, but come back in 5 years and I'd bet that it'll be a whole different ball game.
As someone who saw the idea of a portable phone go from "pipe dream" to "something you can buy for $9.95 at Walmart", I've learned not to scoff or say stuff like this can't be done. It will be done, just not at the breathless pace the press releases would like you to believe.
And that $9.95 phone? It also has a video camera, GPS mapping, accelerometers, a nice color display, and tons of other shit. You can do photo and video editing on it, send texts to the other side of the planet for free, and play all the silly games you could ever want on it. It has more computing power than the entire Department Of Defense had in 1960.
If you had told most people about this in 1960 they'd have had you committed. So yeah, I believe some form of AI will happen. It's inevitable.
Just cruising through this digital world at 33 1/3 rpm...
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.
A real professional, conscientious translator will make sure their translation is unambiguous, even if the original isn't. We don't have the right to practise GIGO.
So provided there were only conscientious professional translators in the chain, yes, they'd pass the test easily.
Having said that, I don't believe the bouncy translation method is a good yardstick at all. A good translation isn't judged on its repeatability.
No, your children are not the special ones. Nor are your pets.
How can this translation software be "brainlike"? Let's see... It doesn't translate the way human brains do...it produces results a small fraction of the quality a human brain produces...and, it can be fooled by trivial procedures like reverse-then-forward translation, where human brains are not fooled.
I know brains, and those ain't no brains.