‘The Imitation Game’ Stars Benedict Cumberbatch

More fundamentally, “The Imitation Game” is a parable of disruption. It not only provides an origin myth for the digital age, but it also projects the ideology of the present back into the past. Turing, an eccentric visionary stuck in an organization that is bureaucratic, hierarchical and wedded to tradition, is an apostle of innovation. Commander Denniston lectures him about the importance of “order, discipline and chain of command” for the war effort, but the solving of Enigma decisively rebuts this old-fashioned notion. The strategic acumen of generals and the tactical valor of soldiers is incidental. What won the war was data, and the heroes were the tech guys (and the one woman) who worked late, snacked freely, fiddled with crossword puzzles and geeked out over a piece of hardware that looked like a giant toy. Hut 8 at Bletchley Park serves as a prototype for the corporate campuses of Apple, Google and Facebook.

A Rare Peek Into The Massive Scale of AWS

The Amazon online retail business may be a $70 billion behemoth, but it does not throw off a lot of cash. Amazon founder and CEO Jeff Bezos is not interested in profits as much as he is about transforming the world around him, but the cloud computing business is one of the most capital intensive businesses there are in the world. Thanks to its near monopoly in online search, Google can spend tens of billions of dollars on datacenters and not bat an eyelash. Microsoft, thanks to its near monopoly on desktop software and its dominant position in datacenter software, also has very deep pockets and can spend as much.

.. So, the answer is that AWS probably has somewhere between 2.8 million and 5.6 million servers across its infrastructure. I realize those are some pretty big error bars, but this is the data we have to work with.

.. “Networking is a red alert situation for us right now,” explained Hamilton. “The cost of networking is escalating relative to the cost of all other equipment. It is Anti-Moore. All of our gear is going down in cost, and we are dropping prices, and networking is going the wrong way. That is a super-big problem, and I like to look out a few years, and I am seeing that the size of the networking problem is getting worse constantly. At the same time that networking is going Anti-Moore, the ratio of networking to compute is going up.”

.. The first thing that Amazon learned from its custom network gear is what it learned about servers and storage a long time ago: If you build it yourself with minimalist attitudes and only with the features you need, it is a lot cheaper.

.. But the surprising thing, even to Hamilton, was that network availability went up, not down. And that is because AWS switches and routers only had features that AWS needed in its network.

.. And so when it first tested out its homegrown networking, it did so on a 3 megawatt datacenter with 8,000 servers that cost something on the order of $40 million to build. This is not something even the largest network equipment providers can do, but AWS could, and did, literally rent the capacity from itself for a couple of hundred thousand dollars to test at this vast scale for a couple of months. (Yet another example of scale and how to leverage it.) Today all of the AWS network is using this custom network stack. Equally important to owning the stack and testing it thoroughly, Amazon continuously develops the code and puts it into production. “Even if it doesn’t start out better, it keeps getting better.”

.. The AZs are usually under 1 millisecond apart in terms of latency and are always less than 2 milliseconds apart; this speed is what allows for synchronous data replication, since committing data to a solid state drive takes – wait for it – between 1 and 2 milliseconds.

David Carr: Automation Makes Us Dumb

It has been a slow process. The first wave of automation rolled through U.S. industry after World War II, when manufacturers began installing electronically controlled equipment in their plants. The new machines made factories more efficient and companies more profitable. They were also heralded as emancipators. By relieving factory hands of routine chores, they would do more than boost productivity. They would elevate laborers, giving them more invigorating jobs and more valuable talents. The new technology would be ennobling.

Then, in the 1950s, a Harvard Business School professor named James Bright went into the field to study automation’s actual effects on a variety of industries, from heavy manufacturing to oil refining to bread baking. Factory conditions, he discovered, were anything but uplifting. More often than not, the new machines were leaving workers with drabber, less demanding jobs. An automated milling machine, for example, didn’t transform the metalworker into a more creative artisan; it turned him into a pusher of buttons.

..  Harvard Medical School professor Beth Lown, in a 2012 journal article written with her student Dayron Rodriquez, warned that when doctors become “screen-driven,” following a computer’s prompts rather than “the patient’s narrative thread,” their thinking can become constricted. In the worst cases, they may miss important diagnostic signals.

The risk isn’t just theoretical. In a recent paper published in the journal Diagnosis, three medical researchers—including Hardeep Singh, director of the health policy, quality and informatics program at the Veterans Administration Medical Center in Houston—examined the misdiagnosis of Thomas Eric Duncan, the first person to die of Ebola in the U.S., at Texas Health Presbyterian Hospital Dallas. They argue that the digital templates used by the hospital’s clinicians to record patient information probably helped to induce a kind of tunnel vision. “These highly constrained tools,” the researchers write, “are optimized for data capture but at the expense of sacrificing their utility for appropriate triage and diagnosis, leading users to miss the forest for the trees.” Medical software, they write, is no “replacement for basic history-taking, examination skills, and critical thinking.”

 

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Pushing automation in a more humane direction doesn’t require any technical breakthroughs. It requires a shift in priorities and a renewed focus on human strengths and weaknesses.

Airlines, for example, could program cockpit computers to shift control back and forth between computer and pilot during a flight. By keeping the aviator alert and active, that small change could make flying even safer.