“Everybody wondered: How had Google Translate become so uncannily artful?”
“The Google of the future, Pichai had said on several occasions, was going to be “A.I. first.””
“What it meant in practice, with any luck, was that soon the company’s products would no longer represent the fruits of traditional computer programming, exactly, but “machine learning.””
“A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility.”
“Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts.”
“The A.I. system had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime.”
“The new Google Translate was run on the first machines that had, in a sense, ever learned to read anything at all.”
“Google’s decision to reorganize itself around A.I. was the first major manifestation of what has become an industrywide machine-learning delirium.”
“Practically nobody today, however, would bestow upon Google Maps the honorific “A.I.,” so sentimental and sparing are we in our use of the word “intelligence.” Artificial intelligence, we believe, must be something that distinguishes HAL from whatever it is a loom or wheelbarrow can do.”
“The minute we can automate a task, we downgrade the relevant skill involved to one of mere mechanism.”
“Today Google Maps seems, in the pejorative sense of the term, robotic: It simply accepts an explicit demand (the need to get from one place to another) and tries to satisfy that demand as efficiently as possible. The goal posts for “artificial intelligence” are thus constantly receding.”
“When he has an opportunity to make careful distinctions, Pichai differentiates between the current applications of A.I. and the ultimate goal of “artificial general intelligence.” Artificial general intelligence will not involve dutiful adherence to explicit instructions, but instead will demonstrate a facility with the implicit, the interpretive. It will be a general tool, designed for general purposes in a general context.”
“In a famous 1950 essay, Alan Turing proposed a test for an artificial general intelligence: a computer that could, over the course of five minutes of text exchange, successfully deceive a real human interlocutor. Once a machine can translate fluently between two natural languages, the foundation has been laid for a machine that might one day “understand” human language well enough to engage in plausible conversation. Google Brain’s members, who pushed and helped oversee the Translate project, believe that such a machine would be on its way to serving as a generally intelligent all-encompassing personal digital assistant.”
“What follows here is the story of how a team of Google researchers and engineers — at first one or two, then three or four, and finally more than a hundred — made considerable progress in that direction.”
“It is, in fact, three overlapping stories that converge in Google Translate’s successful metamorphosis to A.I. — a technical story, an institutional story and a story about the evolution of ideas.”
“The technical story is about one team on one product at one company, and the process by which they refined, tested and introduced a brand-new version of an old product in only about a quarter of the time anyone, themselves included, might reasonably have expected.”
“The institutional story is about the employees of a small but influential artificial-intelligence group within that company, and the process by which their intuitive faith in some old, unproven and broadly unpalatable notions about computing upended every other company within a large radius.”
“The story of ideas is about the cognitive scientists, psychologists and wayward engineers who long toiled in obscurity, and the process by which their ostensibly irrational convictions ultimately inspired a paradigm shift in our understanding not only of technology but also, in theory, of consciousness itself.”
“The first story, the story of Google Translate, takes place in Mountain View over nine months, and it explains the transformation of machine translation. The second story, the story of Google Brain and its many competitors, takes place in Silicon Valley over five years, and it explains the transformation of that entire community. The third story, the story of deep learning, takes place in a variety of far-flung laboratories — in Scotland, Switzerland, Japan and most of all Canada — over seven decades, and it might very well contribute to the revision of our self-image as first and foremost beings who think.”
“All three are stories about artificial intelligence. The seven-decade story is about what we might conceivably expect or want from it. The five-year story is about what it might do in the near future. The nine-month story is about what it can do right this minute. These three stories are themselves just proof of concept. All of this is only the beginning.”
“Part I: Learning Machine”
“1. The Birth of Brain”
“Since the term “artificial intelligence” was first coined, at a kind of constitutional convention of the mind at Dartmouth in the summer of 1956, a majority of researchers have long thought the best approach to creating A.I. would be to write a very big, comprehensive program that laid out both the rules of logical reasoning and sufficient knowledge of the world.”
“You would give the machine a language map that was, as Borges would have had it, the size of the territory. This perspective is usually called “symbolic A.I.” — because its definition of cognition is based on symbolic logic — or, disparagingly, “good old-fashioned A.I.””
“Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules.”
“There has always been another vision for A.I. — a dissenting view — in which the computers would learn from the ground up (from data) rather than from the top down (from rules). This notion dates to the early 1940s, when it occurred to researchers that the best model for flexible automated intelligence was the brain itself.”
“A brain, after all, is just a bunch of widgets, called neurons, that either pass along an electrical charge to their neighbors or don’t.”
“What’s important are less the individual neurons themselves than the manifold connections among them.”
“This structure, in its simplicity, has afforded the brain a wealth of adaptive advantages. The brain can operate in circumstances in which information is poor or missing; it can withstand significant damage without total loss of control; it can store a huge amount of knowledge in a very efficient way; it can isolate distinct patterns but retain the messiness necessary to handle ambiguity.”
“This attitude toward artificial intelligence was evolutionary rather than creationist. If you wanted a flexible mechanism, you wanted one that could adapt to its environment. If you wanted something that could adapt, you didn’t want to begin with the indoctrination of the rules of chess. You wanted to begin with very basic abilities — sensory perception and motor control — in the hope that advanced skills would emerge organically.”
“Humans don’t learn to understand language by memorizing dictionaries and grammar books, so why should we possibly expect our computers to do so?”
“2. The Unlikely Intern”
“Their system’s object-recognition abilities improved by an order of magnitude. This was not because Brain’s personnel had generated a sheaf of outrageous new ideas in just a year. It was because Google had finally devoted the resources — in computers and, increasingly, personnel — to fill in outlines that had been around for a long time.”
“A great preponderance of these extant and neglected notions had been proposed or refined by a peripatetic English polymath named Geoffrey Hinton. In the second year of Brain’s existence, Hinton was recruited to Brain as Andrew Ng left. (Ng now leads the 1,300-person A.I. team at Baidu.)”
“Hinton comes from one of those old British families emblazoned like the Darwins at eccentric angles across the intellectual landscape, where regardless of titular preoccupation a person is expected to make sideline contributions to minor problems in astronomy or fluid dynamics. His great-great-grandfather was George Boole, whose foundational work in symbolic logic underpins the computer; another great-great-grandfather was a celebrated surgeon, his father a venturesome entomologist, his father’s cousin a Los Alamos researcher; the list goes on. He trained at Cambridge and Edinburgh, then taught at Carnegie Mellon before he ended up at Toronto, where he still spends half his time. (His work has long been supported by the largess of the Canadian government.)”
“Hinton had been working on neural networks since his undergraduate days at Cambridge in the late 1960s, and he is seen as the intellectual primogenitor of the contemporary field.”
“Minsky’s criticism of the Perceptron extended only to networks of one “layer,” i.e., one layer of artificial neurons between what’s fed to the machine and what you expect from it — and later in life, he expounded ideas very similar to contemporary deep learning. But Hinton already knew at the time that complex tasks could be carried out if you had recourse to multiple layers.”
“The simplest description of a neural network is that it’s a machine that makes classifications or predictions based on its ability to discover patterns in data. With one layer, you could find only simple patterns; with more than one, you could look for patterns of patterns.”
“Take the case of image recognition, which tends to rely on a contraption called a “convolutional neural net.””
“The first layer of the network learns to identify the very basic visual trope of an “edge,” meaning a nothing (an off-pixel) followed by a something (an on-pixel) or vice versa. Each successive layer of the network looks for a pattern in the previous layer. A pattern of edges might be a circle or a rectangle. A pattern of circles or rectangles might be a face. And so on. This more or less parallels the way information is put together in increasingly abstract ways as it travels from the photoreceptors in the retina back and up through the visual cortex. At each conceptual step, detail that isn’t immediately relevant is thrown away. If several edges and circles come together to make a face, you don’t care exactly where the face is found in the visual field; you just care that it’s a face.”
“The issue with multilayered, “deep” neural networks was that the trial-and-error part got extraordinarily complicated.”
“3. A Deep Explanation of Deep Learning”
“An average brain has something on the order of 100 billion neurons. Each neuron is connected to up to 10,000 other neurons, which means that the number of synapses is between 100 trillion and 1,000 trillion. For a simple artificial neural network of the sort proposed in the 1940s, the attempt to even try to replicate this was unimaginable. We’re still far from the construction of a network of that size, but Google Brain’s investment allowed for the creation of artificial neural networks comparable to the brains of mice.”
“Now imagine that instead of hard-wiring the machine with a set of rules for classification stored in one location of the computer’s memory, you try the same thing on a neural network. There is no special place that can hold the definition of “cat.” There is just a giant blob of interconnected switches, like forks in a path. On one side of the blob, you present the inputs (the pictures); on the other side, you present the corresponding outputs (the labels). Then you just tell it to work out for itself, via the individual calibration of all of these interconnected switches, whatever path the data should take so that the inputs are mapped to the correct outputs.”
“The training is the process by which a labyrinthine series of elaborate tunnels are excavated through the blob, tunnels that connect any given input to its proper output. The more training data you have, the greater the number and intricacy of the tunnels that can be dug. Once the training is complete, the middle of the blob has enough tunnels that it can make reliable predictions about how to handle data it has never seen before. This is called “supervised learning.””
“Imagine you want to program a cat-recognizer on the old symbolic-A.I. model. You stay up for days preloading the machine with an exhaustive, explicit definition of “cat.” You tell it that a cat has four legs and pointy ears and whiskers and a tail, and so on. All this information is stored in a special place in memory called Cat. Now you show it a picture. First, the machine has to separate out the various distinct elements of the image. Then it has to take these elements and apply the rules stored in its memory. If(legs=4) and if(ears=pointy) and if(whiskers=yes) and if(tail=yes) and if(expression=supercilious), then(cat=yes). But what if you showed this cat-recognizer a Scottish Fold, a heart-rending breed with a prized genetic defect that leads to droopy doubled-over ears? Our symbolic A.I. gets to (ears=pointy) and shakes its head solemnly, “Not cat.” It is hyperliteral, or “brittle.” Even the thickest toddler shows much greater inferential acuity.”
“The reason that the network requires so many neurons and so much data is that it functions, in a way, like a sort of giant machine democracy. Imagine you want to train a computer to differentiate among five different items. Your network is made up of millions and millions of neuronal “voters,” each of whom has been given five different cards: one for cat, one for dog, one for spider monkey, one for spoon and one for defibrillator. You show your electorate a photo and ask, “Is this a cat, a dog, a spider monkey, a spoon or a defibrillator?” All the neurons that voted the same way collect in groups, and the network foreman peers down from above and identifies the majority classification: “A dog?”
You say: “No, maestro, it’s a cat. Try again.””
“Now the network foreman goes back to identify which voters threw their weight behind “cat” and which didn’t. The ones that got “cat” right get their votes counted double next time — at least when they’re voting for “cat.” They have to prove independently whether they’re also good at picking out dogs and defibrillators, but one thing that makes a neural network so flexible is that each individual unit can contribute differently to different desired outcomes. What’s important is not the individual vote, exactly, but the pattern of votes. If Joe, Frank and Mary all vote together, it’s a dog; but if Joe, Kate and Jessica vote together, it’s a cat; and if Kate, Jessica and Frank vote together, it’s a defibrillator. The neural network just needs to register enough of a regularly discernible signal somewhere to say, “Odds are, this particular arrangement of pixels represents something these humans keep calling ‘cats.’ ” The more “voters” you have, and the more times you make them vote, the more keenly the network can register even very weak signals. If you have only Joe, Frank and Mary, you can maybe use them only to differentiate among a cat, a dog and a defibrillator. If you have millions of different voters that can associate in billions of different ways, you can learn to classify data with incredible granularity. Your trained voter assembly will be able to look at an unlabeled picture and identify it more or less accurately.”
“Part of the reason there was so much resistance to these ideas in computer-science departments is that because the output is just a prediction based on patterns of patterns, it’s not going to be perfect, and the machine will never be able to define for you what, exactly, a cat is. It just knows them when it sees them. This wooliness, however, is the point. The neuronal “voters” will recognize a happy cat dozing in the sun and an angry cat glaring out from the shadows of an untidy litter box, as long as they have been exposed to millions of diverse cat scenes. You just need lots and lots of the voters — in order to make sure that some part of your network picks up on even very weak regularities, on Scottish Folds with droopy ears, for example — and enough labeled data to make sure your network has seen the widest possible variance in phenomena.”
“4. The Cat Paper”
“Part II: Language Machine”
“5. The Linguistic Turn”
“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.””
“6. The Ambush”
“7. Theory Becomes Product”
“8. A Celebration”
“Epilogue: Machines Without Ghosts”
“Perhaps the most famous historic critique of artificial intelligence, or the claims made on its behalf, implicates the question of translation. The Chinese Room argument was proposed in 1980 by the Berkeley philosopher John Searle. In Searle’s thought experiment, a monolingual English speaker sits alone in a cell. An unseen jailer passes him, through a slot in the door, slips of paper marked with Chinese characters. The prisoner has been given a set of tables and rules in English for the composition of replies. He becomes so adept with these instructions that his answers are soon “absolutely indistinguishable from those of Chinese speakers.” Should the unlucky prisoner be said to “understand” Chinese? Searle thought the answer was obviously not. This metaphor for a computer, Searle later wrote, exploded the claim that “the appropriately programmed digital computer with the right inputs and outputs would thereby have a mind in exactly the sense that human beings have minds.””
“For the Google Brain team, though, or for nearly everyone else who works in machine learning in Silicon Valley, that view is entirely beside the point. This doesn’t mean they’re just ignoring the philosophical question. It means they have a fundamentally different view of the mind. Unlike Searle, they don’t assume that “consciousness” is some special, numinously glowing mental attribute — what the philosopher Gilbert Ryle called the “ghost in the machine.” They just believe instead that the complex assortment of skills we call “consciousness” has randomly emerged from the coordinated activity of many different simple mechanisms. The implication is that our facility with what we consider the higher registers of thought are no different in kind from what we’re tempted to perceive as the lower registers. Logical reasoning, on this account, is seen as a lucky adaptation; so is the ability to throw and catch a ball. Artificial intelligence is not about building a mind; it’s about the improvement of tools to solve problems. As Corrado said to me on my very first day at Google, “It’s not about what a machine ‘knows’ or ‘understands’ but what it ‘does,’ and — more importantly — what it doesn’t do yet.””
“And yet the rise of machine learning makes it more difficult for us to carve out a special place for us. If you believe, with Searle, that there is something special about human “insight,” you can draw a clear line that separates the human from the automated. If you agree with Searle’s antagonists, you can’t. It is understandable why so many people cling fast to the former view.”
“At a 2015 M.I.T. conference about the roots of artificial intelligence, Noam Chomsky was asked what he thought of machine learning. He pooh-poohed the whole enterprise as mere statistical prediction, a glorified weather forecast. Even if neural translation attained perfect functionality, it would reveal nothing profound about the underlying nature of language. It could never tell you if a pronoun took the dative or the accusative case. This kind of prediction makes for a good tool to accomplish our ends, but it doesn’t succeed by the standards of furthering our understanding of why things happen the way they do. A machine can already detect tumors in medical scans better than human radiologists, but the machine can’t tell you what’s causing the cancer.
Then again, can the radiologist?”
“Medical diagnosis is one field most immediately, and perhaps unpredictably, threatened by machine learning. Radiologists are extensively trained and extremely well paid, and we think of their skill as one of professional insight — the highest register of thought. In the past year alone, researchers have shown not only that neural networks can find tumors in medical images much earlier than their human counterparts but also that machines can even make such diagnoses from the texts of pathology reports. What radiologists do turns out to be something much closer to predictive pattern-matching than logical analysis. They’re not telling you what caused the cancer; they’re just telling you it’s there.”
“Once you’ve built a robust pattern-matching apparatus for one purpose, it can be tweaked in the service of others. One Translate engineer took a network he put together to judge artwork and used it to drive an autonomous radio-controlled car. A network built to recognize a cat can be turned around and trained on CT scans — and on infinitely more examples than even the best doctor could ever review. A neural network built to translate could work through millions of pages of documents of legal discovery in the tiniest fraction of the time it would take the most expensively credentialed lawyer. The kinds of jobs taken by automatons will no longer be just repetitive tasks that were once — unfairly, it ought to be emphasized — associated with the supposed lower intelligence of the uneducated classes. We’re not only talking about three and a half million truck drivers who may soon lack careers. We’re talking about inventory managers, economists, financial advisers, real estate agents. What Brain did over nine months is just one example of how quickly a small group at a large company can automate a task nobody ever would have associated with machines.”
“The most important thing happening in Silicon Valley right now is not disruption. Rather, it’s institution-building — and the consolidation of power — on a scale and at a pace that are both probably unprecedented in human history.”
“The recipients of these major investments in human cultivation — for they’re far more than perks for proles in some digital salt mine — have at hand the power of complexly coordinated servers distributed across 13 data centers on four continents, data centers that draw enough electricity to light up large cities.”
“Gideon Lewis-Kraus is a writer at large for the magazine and a fellow at New America. He last wrote about the contradictions of travel photography.”
“The New York Times Magazine”