Day 124: Book Excerpt: Mindless

When I first did research on Walmart’s workplace practices in the early 2000s, I came away convinced that Walmart was the most egregiously ruthless corporation in America. However, ten years later, there is a strong challenger for this dubious distinction—Amazon Corporation. Within the corporate world, Amazon now ranks with Apple as among the United States’ most esteemed businesses. Jeff Bezos, Amazon’s founder and CEO, came in second in the Harvard Business Review’s 2012 world rankings of admired CEOs, and Amazon was third in CNN’s 2012 list of the world’s most admired companies. Amazon is now a leading global seller not only of books but also of music and movie DVDs, video games, gift cards, cell phones, and magazine subscriptions. Like Walmart itself, Amazon combines state-of-the-art CBSs with human resource practices reminiscent of the nineteenth and early twentieth centuries.

Amazon equals Walmart in the use of monitoring technologies to track the minute-by-minute movements and performance of employees and in settings that go beyond the assembly line to include their movement between loading and unloading docks, between packing and unpacking stations, and to and from the miles of shelving at what Amazon calls its “fulfillment centers”—gigantic warehouses where goods ordered by Amazon’s online customers are sent by manufacturers and wholesalers, there to be shelved, packaged, and sent out again to the Amazon customer.

Amazon’s shop-floor processes are an extreme variant of Taylorism that Frederick Winslow Taylor himself, a near century after his death, would have no trouble recognizing. With this twenty-first-century Taylorism, management experts, scientific managers, take the basic workplace tasks at Amazon, such as the movement, shelving, and packaging of goods, and break down these tasks into their subtasks, usually measured in seconds; then rely on time and motion studies to find the fastest way to perform each subtask; and then reassemble the subtasks and make this “one best way” the process that employees must follow.

Amazon is also a truly global corporation in a way that Walmart has never been, and this globalism provides insights into how Amazon responds to workplaces beyond the United States that can follow different rules. In the past three years, the harsh side of Amazon has come to light in the United Kingdom and Germany as well as the United States, and Amazon’s contrasting conduct in America and Britain, on one side, and in Germany, on the other, reveals how the political economy of Germany is employee friendly in a way that those of the other two countries no longer are.

Amazon, like General Electric and Walmart, prides itself as a self-consciously ideological corporation, with Jeff Bezos and his senior executives proclaiming an “Amazon Way” that can illuminate the path forward for less innovative businesses. In December 2009 Mark Onetto, chief of operations and customer relations at Amazon and a close collaborator of Bezos, gave an hourlong lecture on the Amazon Way to master’s of business administration students at the University of Virginia’s Darden School of Business. Onetto is a disconcerting figure, because once he starts talking, style and substance are in sharp contrast. He is French born, and he still speaks with the rather faded insouciance of Maurice Chevalier and “Gay Paree,” and he makes much of this in his lecture. But there was nothing gay (in the traditional sense) or insouciant about the Amazon workplace that Onetto described for UVA’s MBA candidates.

Like most such corporate mission statements, Onetto’s uses a coded language that hides the harshness of his underlying message, which needs translation along with a hefty reality check. As with Walmart so at Amazon, there is a quasi-religious cult of the customer as an object of “trust” and “care”; Amazon “cares about the customer,” and “everything is driven” for him or her. Early in the lecture, Onetto quotes Bezos himself as saying, “I am not selling stuff. I am facilitating for my customers to buy what they need.”

Amazon’s larding of its customer cult with the moral language of “care” and “trust” comes with a strong dose of humbug because Amazon’s customers are principally valued by the corporation as mainstays of the bottom line, and not as vehicles for the fulfillment of personal relationships. There is still more humbug in the air because Amazon treats a second significant grouping of men and women with whom it has dealings—its employees—with the very opposite of care and trust. Amazon’s employees are almost completely absent from Onetto’s lecture, and they make their one major appearance when they too are wheeled in as devotees of the cult of the customer: “We make sure that every associate at Amazon is really a customercentric person, that cares about the customer.”

~~Mindless -by- Simon Head

Day 41: Book Excerpt: Countdown to Zero Day- Stuxnet and the launch of the World's First Digital Weapon

It was January 2010 when officials with the International Atomic Energy Agency (IAEA), the United Nations body charged with monitoring Iran’s nuclear program, first began to notice something unusual happening at the uranium enrichment plant outside Natanz in central Iran.

Inside the facility’s large centrifuge hall, buried like a bunker more than fifty feet beneath the desert surface, thousands of gleaming aluminum centrifuges were spinning at supersonic speed, enriching uranium hexafluoride gas as they had been for nearly two years. But over the last weeks, workers at the plant had been removing batches of centrifuges and replacing them with new ones. And they were doing so at a startling rate.
At Natanz each centrifuge, known as an IR-1, has a life expectancy of about ten years. But the devices are fragile and prone to break easily. Even under normal conditions, Iran has to replace up to 10 percent of the centrifuges each year due to material defects, maintenance issues, and worker accidents.

In November 2009, Iran had about 8,700 centrifuges installed at Natanz, so it would have been perfectly normal to see technicians decommission about 800 of them over the course of the year as the devices failed for one reason or another. But as IAEA officials added up the centrifuges removed over several weeks in December 2009 and early January, they realized that Iran was plowing through them at an unusual rate.

Inspectors with the IAEA’s Department of Safeguards visited Natanz an average of twice a month—sometimes by appointment, sometimes unannounced—to track Iran’s enrichment activity and progress. Anytime workers at the plant decommissioned damaged or otherwise unusable centrifuges, they were required to line them up in a control area just inside the door of the centrifuge rooms until IAEA inspectors arrived at their next visit to examine them. The inspectors would run a handheld gamma spectrometer around each centrifuge to ensure that no nuclear material was being smuggled out in them, then approve the centrifuges for removal, making note in reports sent back to IAEA headquarters in Vienna of the number that were decommissioned each time.

IAEA digital surveillance cameras, installed outside the door of each centrifuge room to monitor Iran’s enrichment activity, captured the technicians scurrying about in their white lab coats, blue plastic booties on their feet, as they trotted out the shiny cylinders one by one, each about six feet long and about half a foot in diameter. The workers, by agreement with the IAEA, had to cradle the delicate devices in their arms, wrapped in plastic sleeves or in open boxes, so the cameras could register each item as it was removed from the room.

The surveillance cameras, which weren’t allowed inside the centrifuge rooms, stored the images for later perusal. Each time inspectors visited Natanz, they examined the recorded images to ensure that Iran hadn’t removed additional centrifuges or done anything else prohibited during their absence. But as weeks passed and the inspectors sent their reports back to Vienna, officials there realized that the number of centrifuges being removed far exceeded what was normal.

Officially, the IAEA won’t say how many centrifuges Iran replaced during this period. But news reports quoting European “diplomats” put the number at 900 to 1,000. A former top IAEA official, however, thinks the actual number was much higher. “My educated guess is that 2,000 were damaged,” says Olli Heinonen, who was deputy director of the Safeguards Division until he resigned in October 2010.

Whatever the number, it was clear that something was wrong with the devices. Unfortunately, Iran wasn’t required to tell inspectors why they had replaced them, and, officially, the IAEA inspectors had no right to ask. The agency’s mandate was to monitor what happened to uranium at the enrichment plant, not keep track of failed equipment.

What the inspectors didn’t know was that the answer to their question was right beneath their noses, buried in the bits and memory of the computers in Natanz’s industrial control room. Months earlier, in June 2009, someone had quietly unleashed a destructive digital warhead on computers in Iran, where it had silently slithered its way into critical systems at Natanz, all with a single goal in mind—to sabotage Iran’s uranium enrichment program and prevent President Mahmoud Ahmadinejad from building a nuclear bomb.

The answer was there at Natanz, but it would be nearly a year before the inspectors would obtain it, and even then it would come only after more than a dozen computer security experts around the world spent months deconstructing what would ultimately become known as one of the most sophisticated viruses ever discovered—a piece of software so unique it would make history as the world’s first digital weapon and the first shot across the bow announcing the age of digital warfare.

~~Countdown to Zero Day- Stuxnet and the launch of the World's First Digital Weapon -by- Kim Zetter

Day 40: Book Excerpt: The Information

Solomonoff, Kolmogorov, and Chaitin tackled three different problems and came up with the same answer. Solomonoff was interested in inductive inference: given a sequence of observations, how can one make the best predictions about what will come next? Kolmogorov was looking for a mathematical definition of randomness: what does it mean to say that one sequence is more random than another, when they have the same probability of emerging from a series of coin flips? And Chaitin was trying to find a deep path into Gödel incompleteness by way of Turing and Shannon—as he said later, “putting Shannon’s information theory and Turing’s computability theory into a cocktail shaker and shaking vigorously.” They all arrived at minimal program size. And they all ended up talking about complexity.

The following bitstream (or number) is not very complex, because it is rational:

D: 14285714285714285714285714285714285714285714285714…


It may be rephrased concisely as “PRINT 142857 AND REPEAT,” or even more concisely as “1/7.” If it is a message, the compression saves keystrokes. If it is an incoming stream of data, the observer may recognize a pattern, grow more and more confident, and settle on one-seventh as a theory for the data.

In contrast, this sequence contains a late surprise:

E: 10101010101010101010101010101010101010101010101013


The telegraph operator (or theorist, or compression algorithm) must pay attention to the whole message. Nonetheless, the extra information is minimal; the message can still be compressed, wherever pattern exists. We may say it contains a redundant part and an arbitrary part.

It was Shannon who first showed that anything nonrandom in a message allows compression:

F: 101101011110110110101110101110111101001110110100111101110


Heavy on ones, light on zeroes, this might be emitted by the flip of a biased coin. Huffman coding and other such algorithms exploit statistical regularities to compress the data. Photographs are compressible because of their subjects’ natural structure: light pixels and dark pixels come in clusters; statistically, nearby pixels are likely to be similar; distant pixels are not. Video is even more compressible, because the differences between one frame and the next are relatively slight, except when the subject is in fast and turbulent motion. Natural language is compressible because of redundancies and regularities of the kind Shannon analyzed. Only a wholly random sequence remains incompressible: nothing but one surprise after another.
...
Even Π retains some mysteries:

C: 3.1415926535897932384626433832795028841971693993751…


The world’s computers have spent many cycles analyzing the first trillion or so known decimal digits of this cosmic message, and as far as anyone can tell, they appear normal. No statistical features have been discovered—no biases or correlations, local or remote. It is a quintessentially nonrandom number that seems to behave randomly. Given the nth digit, there is no shortcut for guessing the nth plus one. Once again, the next bit is always a surprise.

How much information, then, is represented by this string of digits? Is it information rich, like a random number? Or information poor, like an ordered sequence?

The telegraph operator could, of course, save many keystrokes—infinitely many, in the long run—by simply sending the message “Π.” But this is a cheat. It presumes knowledge previously shared by the sender and the receiver. The sender has to recognize this special sequence to begin with, and then the receiver has to know what Π is, and how to look up its decimal expansion, or else how to compute it. In effect, they need to share a code book.

This does not mean, however, that Π contains a lot of information. The essential message can be sent in fewer keystrokes. The telegraph operator has several strategies available. For example, he could say, “Take 4, subtract 4/3, add 4/5, subtract 4/7, and so on.” The telegraph operator sends an algorithm, that is. This infinite series of fractions converges slowly upon Π, so the recipient has a lot of work to do, but the message itself is economical: the total information content is the same no matter how many decimal digits are required.

The issue of shared knowledge at the far ends of the line brings complications. Sometimes people like to frame this sort of problem—the problem of information content in messages—in terms of communicating with an alien life-form in a faraway galaxy. What could we tell them? What would we want to say? The laws of mathematics being universal, we tend to think that Π would be one message any intelligent race would recognize. Only, they could hardly be expected to know the Greek letter. Nor would they be likely to recognize the decimal digits “3.1415926535 …” unless they happened to have ten fingers.

~~The Information -by- James Gleick