Gessen on Shalamov: “It was nothing personal. Just the twentieth century.”

I really enjoyed Keith Gessen’s new novel A Terrible Countrya charming, convincing and moving account of a young American’s complicated relationship with Russia. The narrator is a struggling low-level academic specializing in Slavic literature, who must return to Moscow to care for his aging grandmother. He gets by teaching an online course in Russian literature, but generally seems more passionate about playing pick-up hockey than the great Russian books.

One of the only exceptions, to my surprise and delight, is a digression on Varlam Shalamov’s Kolyma Stories, which I just read this year and immediately recognized as an indisputable masterpiece. Here is the narrator’s appreciation of Shalamov:

Shalamov saw things differently from Solzhenitsyn. He saw them doubly, ambivalently. He thought Solzhenitsyn was a windbag. Physical pain, hunger, and bitter cold: these could not be “overcome” by the spirit. Nor did the world divide neatly, as it did for Solzhenitsyn, between friends and enemies of the regime.

For Shalamov, in the camps there were people who helped him and there were people who brought him harm (who beat him, stole his food, ratted him out), but the majority of the people he encountered did neither. They were just, like him, trying to survive. There was great brutality in the camps, and very little heroism.

In his memoirs he told a remarkable story about learning, at one of the darkest moments of his camp life, that his sister-in-law, Asya, with whom he was close, was in a nearby camp. Shalamov was in the hospital with dysentery, and one of the doctors wanted to know if he wished to send Asya a message. Only half alive, Shalamov scribbled her a short and unsentimental note. “Asya,” it said, “I’m very sick. Send some tobacco.” That was all.

Shalamov clearly remembered this with shame, but also with understanding: he was weak, on the edge of death, and had been reduced to a bare animal existence. There was no great lesson in this, except that in certain conditions a man quickly ceases to be a man.

It was nothing personal, as the saying goes. Just the twentieth century.

This is very well put, far better than I managed when trying to express what is so great about Kolyma Stories (here is my previous post on Shalamov).

For more on Gessen’s novel, Francise Prose’s review is on the mark, and gives a good flavor of what the book is like.

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What I’ve been listening to lately

  • Johnny Dodds – Blue Clarinet Stomp. Dodds was the clarinetist in Louis Armstrong’s classic Hot Fives sessions, and these 1928-29 recordings on his own capture some of that same powerful early jazz magic. Particularly wonderful are the trios with just piano and bass; bassist Bill Johnson plays out front and melodically in a way that would not become the norm for jazz bassists for another generation.
  • Vijay Iyer & Wadada Leo Smith – A Cosmic Rhythm With Each Stroke. An absolutely entrancing duet. Iyer is a great partner for Smith’s gorgeous long tones and masterful use of space. I find Smith’s music hard to describe but increasingly rewarding.
  • Don Ellis – Essence. Ellis is best known for his 1970s big-band work which featured odd time signatures, but this stuff is, justifiably, “receding from historical view,” as Ethan Iverson put it. More enduring are Ellis’ first recordings in the early 1960s with some adventurous small groups. So far this one with Paul Bley and Gary Peacock is my favorite of the lot: Ellis really rips into some well-chosen standards and modern compositions.
  • Jakob Bro – BalladeeringI first heard this one in, of all places, that London bookshop on a boat by King’s Cross. It stood out then, as it should have, given the caliber of the bandmates the Danish guitarist found: Lee Konitz, Paul Motian, Bill Frisell. All of them excel at creating a spacious, almost ambient sound.
  • Jimmie Rowles – The Peacocks. A really lovely recording with Stan Getz; there are quartet and vocal numbers, but the real standouts are the duets, including the title track. Discovered via the amazing Ted Gioia, who is a Rowles advocate.

Seizing the moment for artificial intelligence: my take on the US-China rivalry

Today I am republishing a piece originally written for Gavekal Dragonomics clients a few months ago, on the US-China rivalry in artificial intelligence. Usually these pieces stay behind the paywall, but with our partner company Evergreen Gavekal we’re making it available for a general audience.

I wrote this piece as a way of sorting out of my own thinking on how to place recent technological trends in the broader story of China’s economic development. I make no claim to be an expert on artificial intelligence: these are just the thoughts of a China watcher trying to absorb what the technologists are saying.

The full text follows below, or you can download the PDF:

 

Seizing The Moment For Artificial Intelligence

by Andrew Batson (originally published May 3, 2018)

In the escalating trade dispute between the US and China, technology has increasingly become the key issue, overwhelming more traditional economic topics like tariffs, deficits and currency valuations. Both countries see their economic future as depending on their success in high technology, and each is worried they will lose out to the other. One of the most intense areas of focus is artificial intelligence, where recent rapid breakthroughs have captivated investors and the media—and where China has emerged as the main US rival.

In this piece, I will try to provide some nontechnical answers to the questions of the moment: What is artificial intelligence anyway, and why is it a hot topic? Why does China seem to be doing so well in artificial intelligence? And how should we think about the rivalry between the US and China to develop this technology? In brief, I think China will do well in artificial intelligence, in part because the technology is now in a phase that plays to its strengths. But it does not make sense to think of the US and China being engaged in an artificial-intelligence “race” along the lines of the US-Soviet space race.

Machines don’t think, but can do useful stuff

A common-sense definition of artificial intelligence, what a layman might understand the term to mean on the basis of reading the news and watching television, is probably something along the lines of: machines that can think like human beings. Artificial intelligence in this sense does not exist, and according to most researchers in the field there is no prospect of it coming into existence any time soon. There are enormous philosophical and technical challenges in understanding how human minds work and replicating those processes in software, and most of these challenges have not been solved.

A more precise definition of artificial intelligence, closer to that used in the industry, would be: the development of computer systems that can perform tasks associated with human intelligence, such as understanding speech, playing games, or driving cars. In a way the term is misleading, because what is being mimicked is not human intelligence itself, but the practical results of intelligence being applied in specific contexts. Artificial intelligence in this narrow sense does very much exist, and its progress is now attracting feverish interest from business, venture capitalists, governments and the media.

The turning point seems to have come around 2014-15. Since then, software programs have been able to match or exceed human performance in tasks that previously could not be reliably performed by machines: recognizing faces, transcribing spoken words, playing complex games. One landmark that had particular resonance in China was the 2016 victory of Google’s AlphaGo software over a master South Korean player of the board game Go (known as weiqi in Chinese); AlphaGo subsequently also defeated China’s top-ranked player. While chess programs have been beating human masters for years, Go is much more complex; the number of potential board positions is traditionally estimated at 10172, more than the number of atoms in the universe.

How are these feats possible? Most of what is referred to as “artificial intelligence” in the media is a subset of the field known as machine learning, and in particular a subset of machine learning called deep learning. All software works by following clearly specified instructions, known as algorithms, on how to perform a specific task. In machine learning, the algorithms are not fixed in advance, but evolve over time by building data-driven models.

Usually the type of software used in machine learning is called a neural network, because its structure is loosely inspired by the connections between neurons in a human brain. The network takes an input signal and repeatedly processes it into something more useful; what makes the learning “deep” is that there are a large number of “layers” that process the signal. The use of these techniques means that an algorithm can improve its performance of a task by repeated exposure to data. They are particularly useful where writing algorithms the traditional way—by specifying all possible details and eventualities in advance—is cumbersome or impossible. The concept of machine learning dates to the 1960s, and much of the original work underlying today’s approach of deep learning dates to the 1980s and 1990s.

More power, more data

The rapid improvement in the results of specific machine-learning applications in recent years is thus not a result of fresh theoretical breakthroughs. Rather, it has happened because advances in computing power have allowed machine-learning algorithms to run much faster, and the increased availability of very large amounts of structured data have given them much more to work with. The lesson has been that lots of processing of lots of data is required for the algorithms to be effective in finding patterns. This in itself is a sign that machine learning is not very much like human learning: humans can learn quickly from small numbers of examples, by building internal mental models. Machine learning by contrast is a massive and repetitive number-crunching exercise of building up statistical regularities.

Researchers in the field sometimes describe machine-learning algorithms as being “narrow” and “brittle.” Narrow means that an algorithm trained to solve one problem in one dataset does not develop general competencies that allow it to solve another problem in another dataset; an algorithm has to be trained separately for each problem. The Go-playing algorithm is not also capable of analyzing MRI scans. Brittle means that the algorithm only knows its dataset, and can break down if confronted with real-world situations not well represented in the data it learned from. An often-used example is facial-recognition software that is trained on databases consisting largely of photos of white men, which then fails to accurately recognize faces of black women.

But while it is important to understand that machine learning is not a magic wand, it would also not do to underestimate its potential. Machine learning is essentially a way of building better algorithms. That means it could be applied to almost any process that already uses software—which, in today’s world, is quite a lot—as well as many new processes that could not be effectively automated before. The most obvious example is self-driving cars, which can already operate in restricted contexts and could be in general use within a decade. Machine learning is already being used to spot patterns that previously required trained human expertise, such as recognizing financial fraud or early-stage cancers. Because of this broad applicability, enthusiasts call machine learning a “general purpose technology” that, like electricity a century ago, can boost productivity across every part of the economy.

Throwing resources at the problem

The key point is that machine learning has now moved from a pure research phase into a practical development phase. According to Oren Etzioni of the Allen Institute for Artificial Intelligence, all of the major recent successes in machine learning have followed the same template: apply machine-learning algorithms to a large set of carefully categorized data to solve problems in which there is a clear definition of success and failure.

All parts of this procedure are quite resource-intensive. Huge amounts of computing power are required to run the algorithms. The algorithms need huge quantities of data to find patterns. That data must also be first carefully structured and labeled so that the algorithms can draw the right conclusions—for instance, labeling pictures of objects to train an image-recognition algorithm—a process that is extremely labor-intensive. The repeated training and refining of the algorithms also requires a lot of labor by highly skilled workers, whose numbers are necessarily limited. But the reason for the excitement over artificial intelligence is that there is a now a sense that the main remaining constraints on progress are these limitations of resources—and such limitations will be solved over time.

According to its many boosters, China has all of the necessary resources to make progress in machine learning. It has large and well-funded technology companies, including publicly traded giants like Tencent, Alibaba and Baidu, but also private companies with multi-billion-dollar valuations like ByteDance, which runs a popular news app with personalized recommendations, and SenseTime, which specializes in image and facial recognition.

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China also has the world’s largest population of internet and mobile phone users, who are creating huge amounts of data on daily basis through their interactions with software. It has a huge population of relatively low-cost college graduates, for doing the more repetitive work of categorizing data. And it also has more top artificial-intelligence researchers than any country other than the US; indeed, many of the top Chinese in the field were educated in the US and have worked for US companies.

China also has a government that has decided that artificial intelligence is going to be the key technology of the future, and that will not accept being left behind. An ambitious national plan released in July 2017 calls for China to lead the world in artificial intelligence theory, technology and applications by 2030 (for detailed analysis of the plan, see these reports by the Paulson Institute and Oxford University’s Future of Humanity Institute). While it is difficult for government plans to create fundamental research breakthroughs on demand, such plans can be good at mobilizing lots of resources. So to the extent that advances in machine learning are now about mobilizing resources, it is reasonable to think China will indeed be able to make lots of progress.

AI-chart2

The prospect of China being something close to a peer of the US in a major new technology is a shocking development for many Americans. Everyone knows that China’s economy has grown rapidly and that it has accomplished a lot. But most of its past successes in technology involve deploying things developed elsewhere, such as mobile phones, wind turbines or high-speed trains, on a large scale. China has a per-capita GDP of roughly US$8-9,000 at market exchange rates, lower than Mexico or Turkey—and no one is talking about their dominance in the technologies of the future. It is tempting to try to resolve this paradox by focusing on China’s state support for artificial intelligence, implying that its advantages are unfair. The rivalry is also not a purely economic one, since there are military uses for machine learning.

The paradox is more apparent than real. China is such a huge, diverse and unequal country that averages are not a good guide to the location of the cutting edge. The reality, as anyone who has visited Beijing, Shanghai, or Shenzhen in recent years can attest, is that the income, skills and education levels of its best people can be comparable to those in the US. That elite of course is not representative of all the hundreds of millions of their compatriots, but neither is the Silicon Valley elite representative of middle-class Americans.

The fact that China now has the capability to contribute to cutting-edge research is also in large part a result of its integration with the US: it is the decades of sending top Chinese students to top US universities that have built up the necessary human capital. Rather than say there is a competition between the artificial intelligence sectors in the US and China, it might be more accurate to say that there is a single, global field of machine-learning research that has a significant presence in both North America (Canada also has some top people) and China.

There is no AI race

More fundamentally, it is wrong to think of China and the US as being in a “race” for supremacy in artificial intelligence. Evoking the “space race” with the Soviet Union in the 1960s is the wrong analogy. The space race was about achieving clear technical landmarks defined in advance: first satellite in orbit, first human in orbit, first human on the moon, etc. Today, it’s not clear what the technical landmarks for an artificial intelligence race might be. There is a vague goal of “general purpose artificial intelligence,” which means the kind of thinking, talking computers that are familiar from decades of portrayal in science fiction. But there is no race to make one, since no one knows how.

Rather, there are multiple related efforts going on to make progress on diverse sets of specific technical challenges and applications. If China is the first to achieve some technical breakthrough, that does not prevent the US from also doing so, nor does it guarantee that a Chinese company will control the market for applying that breakthrough. Recall that machine-learning applications can be “narrow” and “brittle”: software that is excellent at predicting, say, the video-watching habits of Chinese will not necessarily also dominate the American market. What we can say is that there are economies of scale and scope in machine-learning research: teams of experts who have successfully developed one machine-learning application themselves learn things that will make them better at developing other machine-learning applications (see this recent paper by Avi Goldfarb and Daniel Trefler for more).

Artificial intelligence is not a prize to be won, or even a single technology. Machine learning is a technique for solving problems and making software. At the moment, it is far from clear what the most commercially important use of machine learning will be. In a way, it is a solution in search of problems. China is making a big push in this area not because it knows what artificial intelligence will be able to do and wants to get there first, but because it does not know, and wants to make sure it does not lose out on the potential benefits. China’s development plan for artificial intelligence is mostly a laundry list of buzzwords and hoped-for technical breakthroughs.

The fact that machine learning is now in a resource-intensive phase does play to China’s strengths. There is an enormous amount of venture-capital money and government largesse flowing toward anything labeled “artificial intelligence” in China, and Chinese companies have had some notable successes in attracting high-profile figures in the field to join them. But fears that China will somehow monopolize the resources needed to make progress in machine learning are fanciful. After all, most of the key resources are human beings, who have minds of their own. And many of the key tools and concepts for creating machine-learning applications are in the public domain.

Will today’s advantages endure?

It is also not certain that the current resource-intensive phase of machine learning will last forever. As is usually the case when limited resources constrain development, people are trying to find ways to use fewer resources: in this case, refining machine learning so that it does not require so much human effort in categorizing data and fine-tuning algorithms. Some of the current buzzwords in the field are “unsupervised learning,” where the machine-learning algorithm is trained on raw data that is not classified or labeled, and “transfer learning,” where an algorithm that has already been trained on one dataset is repurposed onto another dataset, which requires much less data the second time around. Progress in these areas could lessen the advantages of China’s “big push” approach, though of course Chinese researchers would also benefit from them.

China’s current strength in machine learning is the result of a convergence between its own capabilities and the needs of the technology; since both are evolving, this convergence may not be a permanent one. But China’s government is correct to see the current moment as a great opportunity. China was already becoming one of the global clusters of machine-learning research even before the government decided to throw lots of subsidies at the technology. The self-reinforcing dynamics of clusters mean that today’s successes will make it easier for China to attract more machine-learning experts and companies in the future.

The biggest loser from this trend, however, is not the US, which already has well-established clusters of machine-learning research, but smaller nations who would also like to become home to such clusters. European countries, for instance, seem to be struggling to hold their own. The perception that there is a rivalry or race between the US and China ultimately derives from the fact that the two countries are rivals rather than friends. Artificial intelligence may indeed be the first example of a major cutting-edge technology whose development is led by geopolitical competitors—the US and China—rather than a group of friendly nations. The rising tensions between the US and China pose the question of whether a global artificial-intelligence field structured in this way is sustainable, or will be forced to split into national communities. The loss of those exchanges would slow progress in both countries.

A novelist’s view of China’s rise, from 1983

Walter Tevis’ 1983 science-fiction novel The Steps of the Sun is mostly not a very good book; unlike some of his other books (the excellent chess novel The Queen’s Gambit, or The Color of Money) it has not aged well. The one thing about the book that does seem ahead of its time is the worldbuilding: it is set in a future world in which China has unquestionably risen. Here is one background passage:

Half the people on the street were Chinese. By midsummer New York always seems to be a Chinese city, a kind of cultural suburb of Peking. The Russians are ahead of everybody else at heavy industry; the art comes from Buenos Aires and Rio de Janeiro; the political life in Aberdeen and Hangchow is far more lively than New York’s; and if you want to make a really big business arrangement you go to Peking, the world’s richest city.

But New York is still New York, even with its elevators not working and a total of one hundred fifty taxis permitted to operate (Peking has thousands, they are electric powered and have leather upholstery). But Peking is still a stodgy businessman’s city, with all the old China erased from its neoclassical architecture. The Chinese come to New York for the civilized life.

New York is the major city of a second-rank power, of a country whose time is slipping away; but it still has a bounce you don’t find anywhere else. There are restaurants with white tablecloths, with waiters in tuxedos that look like they came from the last century, and, however they beer-feed and hand-rub their fat old steers in Japan, the Kansas City steak served in a New York restaurant, with the dim lights and the polished wooden bar and the tuxedoed waiters, is still one of the delights of the world. And New York theater is the only theater to hold anybody’s interest for long; American music is the most sophisticated in the world.

The Chinese are still, behind those stuffy facades, the greatest gamblers on earth and the trickiest businessmen; they’ve accommodated their ideology and their asceticism of the last century to their present wealth with the ease of the Renaissance Popes; they are Communists the way Cesare Borgia was a Christian. And they love New York.

Some of these details are remarkably prescient: the Chinese tourists crowding the streets of New York, and the way the city serves as a kind of living museum of a certain type and period of culture. The bit about China being ahead of the US in electric vehicles also has a ripped-from-the-headlines feel. In another passage, a billionaire shows off his ability to speak Chinese (remind you of anyone?).

This is pretty unusual stuff for 1983, when Americans were obsessed with the rise of Japan and had barely begun to notice China. William Gibson’s much more famous Neuromancer, from 1984, chose Japan as the natural setting for its hyper-technological fantasies. So I am curious what might have inspired this aspect of the book; there is little in Tevis’ biography to suggest a particular interest in or knowledge of Asia.

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Are giant factories a symptom of labor repression?

That is the suggestion made in Deborah Cohen’s interesting review of Joshua B. Freeman’s Behemoth: A History of the Factory and the Making of the Modern World.

Giant factories were a feature of both the US and Soviet economies in the 1930s, which led some observers at the time to speculate that capitalism and socialism were converging toward a single economic form. But this convergence turned out to be quite temporary, as giant factories lasted much longer in the USSR:

By the late 1940s, the era of the showcase factory was over in the United States. The strength of unionization, particularly demonstrated by the formidable strike wave of 1945–1946, made clear to industrialists the danger of concentrating workers in a few plants.

More than simply a means of controlling costs or rationalizing distribution, the drive to open smaller and decentralized plants, especially in the low-wage, nonunionized South, was also a strategy to ensure that a company’s entire operation couldn’t be hamstrung by a strike.

At the same time, by contrast, industrial gigantism continued apace across the Eastern Bloc. The East Germans built the steel town of Stalinstadt (now Eisenhüttenstadt); in Poland, there rose Nowa Huta, with a workforce of nearly 30,000 by 1967. Crippling labor unrest wasn’t a problem that particularly worried leaders in the Eastern Bloc, who could count on a network of spies as well as a cadre of factory workers who were fervent believers in socialism.

The current world champion of industrial gigantism is, of course, China. The “Foxconn City” facility in Shenzhen is generally thought to be the world’s largest manufacturing facility, employing something over 200,000 workers. Strikes in China are not uncommon but tend to be short-term events related to specific disputes, rather than an organized strategy as part of collective bargaining. This of course is because China does not have independent unions; the state-controlled union tends to side with management. So the risk to a company’s operations from an individual strike is still low–though it is worth noting that Foxconn does not depend on one single large facility but instead has lots of large facilities, in China and many other countries.

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The Volgograd Tractor Factory in the 1930s

 

The Newcastle shipyards in world history

I recently paid my first visit to Newcastle-upon-Tyne in northern England, which is a worthwhile stop for anyone interested in the history of the Industrial Revolution. If you follow the promenade along the river to the west, outside the city center you come to a low-rise brick office park. There is no particular reason for a tourist to hang out there, but I was intrigued by the fact that several of the buildings had what appeared to be Japanese names. Looking around, sure enough there was an explanatory placard: the office park sits on the site of the old Newcastle shipyards, and one of their major customers in the late 19th century was the Japanese navy. The buildings were named after the ships.

I had not known until then that the UK had supplied much of the hardware that enabled Japan’s famous military victory over Russia in their war of 1904-5. British shipyards had built all six of the Japanese navy’s battleships, four of its eight cruisers (other European powers supplied the rest), and 16 of its 24 destroyers (the other eight were domestic).

Japan of course was busy building up its own shipbuilding industry, but being able to purchase leading-edge military technology on the open market was essential. Admiral Togo Heihachiro, who commanded the navy in the battle in which Russia’s Baltic fleet was destroyed, had studied in England as a young man, and in 1911 visited Newcastle to express his thanks for its role in arming the navy.

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Japan’s military victory over Russia was the first time an Asian power had defeated a European one in modern times. It was shocking to European and American observers at the time, as it overturned what had been assumed to be an established order. The Russo-Japanese War was a clear turning point in what was to become the century-long rise of Asia and the quest of non-European countries to achieve parity with the European imperial powers. But what does it mean that Japan’s victory was in fact aided and encouraged by some of those same European imperial powers?

Perhaps one point is that rivalry among existing great powers is one of the forces that helps produce new powers: an existing power can seek advantage over its adversaries by encouraging the rise of new powers. The US decision to engage with Communist China in the 1970s cannot, of course, be understood in isolation from its rivalry with the Soviet Union: the US wanted to make sure that China was, if not exactly on its side, at least not on the USSR’s side.

Another possible interpretation is that commercial interests (or, if you prefer, capitalism) can be disruptive to hierarchies in international relations. Would Japan’s aggressive drive to bring its navy up to European standards have been as successful if it did not also boost the sales and profits of Armstrong Whitwork & Co in Newcastle? Similarly, it is hard to imagine that the US would have been so accommodating of China’s “peaceful rise” over the past few decades if it had not also presented big opportunities for American companies.

What I’ve been listening to lately

 

  • Benny Goodman – The Complete RCA Victor Small Group Recordings. Small-group swing is one of the best sounds in jazz in my book, much more listenable today than most big-band music from the same era. The sound that Goodman’s quartet with Teddy Wilson on piano and Lionel Hamptom on vibes generates is just lovely.
  • Kitsos Harisiadis – Lament in a Deep Style 1929-1931I discovered this recording thanks to Andrew Katzenstein’s fascinating article in the New York Review of Books on the music produced in Epirus in the 1920s and 1930s. Harisiadis is a clarinetist and near-contemporary of Goodman but his sound ventures into territory jazz would not explore until the 1960s.
  • John Coltrane – Both Directions At Once. This will probably outsell any jazz recording by a living musician, so I don’t need to give it more publicity. But who could pass up more recordings from the Coltrane quarter’s classic period? While it did not surprise me, I certainly enjoyed this, especially the untitled original compositions.
  • Herbie Hancock – Sextant. Another one of those records I just didn’t hear right the first time: the goofy cover and synthesizer bleeps were apparently not serious enough for this young jazz fan. But with this passage of time, I find I do really like it: an excellent extension of the moody, complex Bitches Brew sound.
  • Ergo – If Not Inertia. The prepared piano pieces of John Cage are some of my favorite music outside the jazz idiom, mostly because of the lovely spooky sound. So I really enjoyed the incorporation of the prepared piano, along with electronics and other noises, into a more jazz-like context on this recording.