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The Great A.I. Awakening. Part 5

Posted by Roman Bodnarchuk on Sun, Jan 01, 2017 @ 17:01 PM


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Part II: Language Machine

5. The Linguistic Turn

The hundred or so current members of Brain — it often feels less like a department within a colossal corporate hierarchy than it does a club or a scholastic society or an intergalactic cantina — came in the intervening years to count among the freest and most widely admired employees in the entire Google organization. They are now quartered in a tiered two-story eggshell building, with large windows tinted a menacing charcoal gray, on the leafy northwestern fringe of the company’s main Mountain View campus. Their microkitchen has a foosball table I never saw used; a Rock Band setup I never saw used; and a Go kit I saw used on a few occasions. (I did once see a young Brain research associate introducing his colleagues to ripe jackfruit, carving up the enormous spiky orb like a turkey.)

When I began spending time at Brain’s offices, in June, there were some rows of empty desks, but most of them were labeled with Post-it notes that said things like “Jesse, 6/27.” Now those are all occupied. When I first visited, parking was not an issue. The closest spaces were those reserved for expectant mothers or Teslas, but there was ample space in the rest of the lot. By October, if I showed up later than 9:30, I had to find a spot across the street.

Brain’s growth made Dean slightly nervous about how the company was going to handle the demand. He wanted to avoid what at Google is known as a “success disaster” — a situation in which the company’s capabilities in theory outpaced its ability to implement a product in practice. At a certain point he did some back-of-the-envelope calculations, which he presented to the executives one day in a two-slide presentation.

“If everyone in the future speaks to their Android phone for three minutes a day,” he told them, “this is how many machines we’ll need.” They would need to double or triple their global computational footprint.

“That,” he observed with a little theatrical gulp and widened eyes, “sounded scary. You’d have to” — he hesitated to imagine the consequences — “build new buildings.”

There was, however, another option: just design, mass-produce and install in dispersed data centers a new kind of chip to make everything faster. These chips would be called T.P.U.s, or “tensor processing units,” and their value proposition — counterintuitively — is that they are deliberately less precise than normal chips. Rather than compute 12.246 times 54.392, they will give you the perfunctory answer to 12 times 54. On a mathematical level, rather than a metaphorical one, a neural network is just a structured series of hundreds or thousands or tens of thousands of matrix multiplications carried out in succession, and it’s much more important that these processes be fast than that they be exact. “Normally,” Dean said, “special-purpose hardware is a bad idea. It usually works to speed up one thing. But because of the generality of neural networks, you can leverage this special-purpose hardware for a lot of other things.”

Just as the chip-design process was nearly complete, Le and two colleagues finally demonstrated that neural networks might be configured to handle the structure of language. He drew upon an idea, called “word embeddings,” that had been around for more than 10 years. When you summarize images, you can divine a picture of what each stage of the summary looks like — an edge, a circle, etc. When you summarize language in a similar way, you essentially produce multidimensional maps of the distances, based on common usage, between one word and every single other word in the language. The machine is not “analyzing” the data the way that we might, with linguistic rules that identify some of them as nouns and others as verbs. Instead, it is shifting and twisting and warping the words around in the map. In two dimensions, you cannot make this map useful. You want, for example, “cat” to be in the rough vicinity of “dog,” but you also want “cat” to be near “tail” and near “supercilious” and near “meme,” because you want to try to capture all of the different relationships — both strong and weak — that the word “cat” has to other words. It can be related to all these other words simultaneously only if it is related to each of them in a different dimension. You can’t easily make a 160,000-dimensional map, but it turns out you can represent a language pretty well in a mere thousand or so dimensions — in other words, a universe in which each word is designated by a list of a thousand numbers. Le gave me a good-natured hard time for my continual requests for a mental picture of these maps. “Gideon,” he would say, with the blunt regular demurral of Bartleby, “I do not generally like trying to visualize thousand-dimensional vectors in three-dimensional space.”

Still, certain dimensions in the space, it turned out, did seem to represent legible human categories, like gender or relative size. If you took the thousand numbers that meant “king” and literally just subtracted the thousand numbers that meant “queen,” you got the same numerical result as if you subtracted the numbers for “woman” from the numbers for “man.” And if you took the entire space of the English language and the entire space of French, you could, at least in theory, train a network to learn how to take a sentence in one space and propose an equivalent in the other. You just had to give it millions and millions of English sentences as inputs on one side and their desired French outputs on the other, and over time it would recognize the relevant patterns in words the way that an image classifier recognized the relevant patterns in pixels. You could then give it a sentence in English and ask it to predict the best French analogue.

The major difference between words and pixels, however, is that all of the pixels in an image are there at once, whereas words appear in a progression over time. You needed a way for the network to “hold in mind” the progression of a chronological sequence — the complete pathway from the first word to the last. In a period of about a week, in September 2014, three papers came out — one by Le and two others by academics in Canada and Germany — that at last provided all the theoretical tools necessary to do this sort of thing. That research allowed for open-ended projects like Brain’s Magenta, an investigation into how machines might generate art and music. It also cleared the way toward an instrumental task like machine translation. Hinton told me he thought at the time that this follow-up work would take at least five more years.

 

It’s no great tragedy if neural networks mislabel 1 percent of cats as dogs, but in something like a self-driving car we all want greater assurances. 

6. The Ambush

Le’s paper showed that neural translation was plausible, but he had used only a relatively small public data set. (Small for Google, that is — it was actually the biggest public data set in the world. A decade of the old Translate had gathered production data that was between a hundred and a thousand times bigger.) More important, Le’s model didn’t work very well for sentences longer than about seven words.

Mike Schuster, who then was a staff research scientist at Brain, picked up the baton. He knew that if Google didn’t find a way to scale these theoretical insights up to a production level, someone else would. The project took him the next two years. “You think,” Schuster says, “to translate something, you just get the data, run the experiments and you’re done, but it doesn’t work like that.”

Schuster is a taut, focused, ageless being with a tanned, piston-shaped head, narrow shoulders, long camo cargo shorts tied below the knee and neon-green Nike Flyknits. He looks as if he woke up in the lotus position, reached for his small, rimless, elliptical glasses, accepted calories in the form of a modest portion of preserved acorn and completed a relaxed desert decathlon on the way to the office; in reality, he told me, it’s only an 18-mile bike ride each way. Schuster grew up in Duisburg, in the former West Germany’s blast-furnace district, and studied electrical engineering before moving to Kyoto to work on early neural networks. In the 1990s, he ran experiments with a neural-networking machine as big as a conference room; it cost millions of dollars and had to be trained for weeks to do something you could now do on your desktop in less than an hour. He published a paper in 1997 that was barely cited for a decade and a half; this year it has been cited around 150 times. He is not humorless, but he does often wear an expression of some asperity, which I took as his signature combination of German restraint and Japanese restraint.

The issues Schuster had to deal with were tangled. For one thing, Le’s code was custom-written, and it wasn’t compatible with the new open-source machine-learning platform Google was then developing, TensorFlow. Dean directed to Schuster two other engineers, Yonghui Wu and Zhifeng Chen, in the fall of 2015. It took them two months just to replicate Le’s results on the new system. Le was around, but even he couldn’t always make heads or tails of what they had done.

As Schuster put it, “Some of the stuff was not done in full consciousness. They didn’t know themselves why they worked.”

This February, Google’s research organization — the loose division of the company, roughly a thousand employees in all, dedicated to the forward-looking and the unclassifiable — convened their leads at an offsite retreat at the Westin St. Francis, on Union Square, a luxury hotel slightly less splendid than Google’s own San Francisco shop a mile or so to the east. The morning was reserved for rounds of “lightning talks,” quick updates to cover the research waterfront, and the afternoon was idled away in cross-departmental “facilitated discussions.” The hope was that the retreat might provide an occasion for the unpredictable, oblique, Bell Labs-ish exchanges that kept a mature company prolific.

At lunchtime, Corrado and Dean paired up in search of Macduff Hughes, director of Google Translate. Hughes was eating alone, and the two Brain members took positions at either side. As Corrado put it, “We ambushed him.”

“O.K.,” Corrado said to the wary Hughes, holding his breath for effect. “We have something to tell you.”

They told Hughes that 2016 seemed like a good time to consider an overhaul of Google Translate — the code of hundreds of engineers over 10 years — with a neural network. The old system worked the way all machine translation has worked for about 30 years: It sequestered each successive sentence fragment, looked up those words in a large statistically derived vocabulary table, then applied a battery of post-processing rules to affix proper endings and rearrange it all to make sense. The approach is called “phrase-based statistical machine translation,” because by the time the system gets to the next phrase, it doesn’t know what the last one was. This is why Translate’s output sometimes looked like a shaken bag of fridge magnets. Brain’s replacement would, if it came together, read and render entire sentences at one draft. It would capture context — and something akin to meaning.

The stakes may have seemed low: Translate generates minimal revenue, and it probably always will. For most Anglophone users, even a radical upgrade in the service’s performance would hardly be hailed as anything more than an expected incremental bump. But there was a case to be made that human-quality machine translation is not only a short-term necessity but also a development very likely, in the long term, to prove transformational. In the immediate future, it’s vital to the company’s business strategy. Google estimates that 50 percent of the internet is in English, which perhaps 20 percent of the world’s population speaks. If Google was going to compete in China — where a majority of market share in search-engine traffic belonged to its competitor Baidu — or India, decent machine translation would be an indispensable part of the infrastructure. Baidu itself had published a pathbreaking paper about the possibility of neural machine translation in July 2015.

 

‘You think to translate something, you just get the data, run the experiments and you’re done, but it doesn’t work like that.’

And in the more distant, speculative future, machine translation was perhaps the first step toward a general computational facility with human language. This would represent a major inflection point — perhaps the major inflection point — in the development of something that felt like true artificial intelligence.

Most people in Silicon Valley were aware of machine learning as a fast-approaching horizon, so Hughes had seen this ambush coming. He remained skeptical. A modest, sturdily built man of early middle age with mussed auburn hair graying at the temples, Hughes is a classic line engineer, the sort of craftsman who wouldn’t have been out of place at a drafting table at 1970s Boeing. His jeans pockets often look burdened with curious tools of ungainly dimension, as if he were porting around measuring tapes or thermocouples, and unlike many of the younger people who work for him, he has a wardrobe unreliant on company gear. He knew that various people in various places at Google and elsewhere had been trying to make neural translation work — not in a lab but at production scale — for years, to little avail.

Hughes listened to their case and, at the end, said cautiously that it sounded to him as if maybe they could pull it off in three years.

Dean thought otherwise. “We can do it by the end of the year, if we put our minds to it.” One reason people liked and admired Dean so much was that he had a long record of successfully putting his mind to it. Another was that he wasn’t at all embarrassed to say sincere things like “if we put our minds to it.”

Hughes was sure the conversion wasn’t going to happen any time soon, but he didn’t personally care to be the reason. “Let’s prepare for 2016,” he went back and told his team. “I’m not going to be the one to say Jeff Dean can’t deliver speed.”

A month later, they were finally able to run a side-by-side experiment to compare Schuster’s new system with Hughes’s old one. Schuster wanted to run it for English-French, but Hughes advised him to try something else. “English-French,” he said, “is so good that the improvement won’t be obvious.”

It was a challenge Schuster couldn’t resist. The benchmark metric to evaluate machine translation is called a BLEU score, which compares a machine translation with an average of many reliable human translations. At the time, the best BLEU scores for English-French were in the high 20s. An improvement of one point was considered very good; an improvement of two was considered outstanding.

The neural system, on the English-French language pair, showed an improvement over the old system of seven points.

Hughes told Schuster’s team they hadn’t had even half as strong an improvement in their own system in the last four years.

To be sure this wasn’t some fluke in the metric, they also turned to their pool of human contractors to do a side-by-side comparison. The user-perception scores, in which sample sentences were graded from zero to six, showed an average improvement of 0.4 — roughly equivalent to the aggregate gains of the old system over its entire lifetime of development.

 

 

Google’s Quoc Le (right), whose work demonstrated the plausibility of neural translation, with Mike Schuster, who helped apply that work to Google Translate.

In mid-March, Hughes sent his team an email. All projects on the old system were to be suspended immediately.

 

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