view between its inputs and outputs

KeyBERT is semantic, because it uses embeddings to capture a document’s overall meaning, producing thematic keywords like “malawi”, “reforms”, and “humanitarian” in Table 2. This works well for summaries but can miss specific details, such as the cease-fire line in the India-Pakistan resolution (Table 3), because it favours the general idea.

This attitude toward artificial intelligence was evolutionary rather than creationist.

They instructed the networks on enormous banks of “labeled” data — speech files with correct transcriptions, for example — and the computers improved their responses to better match reality.

The Google of the future, Pichai had said on several occasions, was going to be “A.I. first.” What that meant in theory was complicated and had welcomed much speculation. What it meant in practice, with any luck, was that soon the company’s products would no longer represent the fruits of traditional computer programming, exactly, but “machine learning.”

nd over the next few hours thousands of people broadcast their own experiments

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.

GOOGLE SAYS A.I. TRANSLATION IS INDISTINGUISHABLE FROM HUMANS’.

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.

Perhaps the most famous historic critique of artificial intelligence, or the claims made on its behalf, implicates the question of translation.

oogle 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.

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.

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 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.

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.

as the chip-design process was nearly complete, Le and two colleagues finally demonstrated th

“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.

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 machines could also deal with raw unlabeled data, perhaps even data of which humans had no established foreknowledge. This seemed like a major advance not only in cat-recognition studies but also in overall artificial intelligence.

supervised learning.”

supervised learning.”

here 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). Th

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

An average brain has something on the order of 100 billion neurons. Each neuron is connected to up to 10,000 other neurons, which

Each successive layer of the network looks for a pattern in the previous layer.

he 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.

Hinton already knew at the time that complex tasks could be carried out if you had recourse to multiple layers.

Minsky published a book that proved that there were painfully simple problems the Perceptron could never solve.

The New York Times reported that the machine’s sponsor, the United States Navy, expected it would “be able to walk, talk, see, write, reproduce itself and be conscious of its existence.”

Hinton had been working on neural networks since his undergraduate days at Cambridge in the late 1960s,

Hinton and two of his students demonstrated truly astonishing gains in a big image-recognition contest, run by an open-source collective called ImageNet, that asks computers not only to identify a monkey but also to distinguish between spider monkeys and howler monkeys, and among God knows how many different breeds of cat.

Humans don’t learn to understand language by memorizing dictionaries and grammar books, so why should we possibly expect our computers to do so?

his notion dates to the early 1940s, when it occurred to researchers that the best model for flexible automated intelligence was the brain itself.

Jeff Dean

a technical story, an institutional story and a story about the evolution of ideas.

“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.

Translate had been converted to an A.I.-based system for much of its traffic, not just in the United States but in Europe and Asia as well: The rollout included translations between English and Spanish, French, Portuguese, German, Chinese, Japanese, Korean and Turkish. The rest of Translate’s hundred-odd languages were to come, with the aim of eight per month, by the end of next year. The new incarnation, to the pleasant surprise of Google’s own engineers, had been completed in only nine months. The A.I. system had demonstrated overnight improvements roughly equal to the total gains the old one had accrued over its entire lifetime.

fivefold increase in translations between Arabic and German

Translate’s geopolitical importance

Translate made its debut in 2006 and since then has become one of Google’s most reliable and popular assets; it serves more than 500 million monthly users in need of 140 billion words per day in a different language. It exists not only as its own stand-alone app but also as an integrated feature within Gmail, Chrome and many other Google offerings, where we take it as a push-button given — a frictionless, natural part of our digital commerce.

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.

As dawn broke over Tokyo, Google Translate was the No. 1 trend on Japanese Twitter

with the machine-translation service. Some were successful,

and over the next few hours thousands of people broadcast their own experiments with the machine-translation service. Some were successful, o

Only 24 hours earlier, Google would have translated the same Japanese passage as follows:Kilimanjaro is 19,710 feet of the mountain covered with snow, and it is said that the highest mountain in Africa. Top of the west, “Ngaje Ngai” in the Maasai language, has been referred to as the house of God. The top close to the west, there is a dry, frozen carcass of a leopard. Whether the leopard had what the demand at that altitude, there is no that nobody explained.

The second half of Rekimoto’s post examined the service in the other direction, from Japanese to English. He dashed off his own Japanese interpretation of the opening to Hemingway’s “The Snows of Kilimanjaro,” then ran that passage back through Google into English. He published this version alongside Hemingway’s original, and proceeded to invite his readers to guess which was the work of a machine.

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.

The theoretical work to get them to this point had already been painstaking and drawn-out, but the attempt to turn it into a viable product — the part that academic scientists might dismiss as “mere” engineering — was no less difficult. For one thing, they needed to make sure that they were training on good data. Google’s billions of words of training “reading” were mostly made up of complete sentences of moderate complexity, like the sort of thing you might find in Hemingway. Some of this is in the public domain: The original Rosetta Stone of statistical machine translation was millions of pages of the complete bilingual records of the Canadian Parliament. Much of it, however, was culled from 10 years of collected data, including human translations that were crowdsourced from enthusiastic respondents. The team had in their storehouse about 97 million unique English “words.” But once they removed the emoticons, and the misspellings, and the redundancies, they had a working vocabulary of only around 160,000.

“phrase-based statistical machine translation,”