rhatto: automação*

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  1. Without knowing it, Zuckerberg is the heir to a long political tradition. Over the last 200 years, the west has been unable to shake an abiding fantasy, a dream sequence in which we throw out the bum politicians and replace them with engineers – rule by slide rule. The French were the first to entertain this notion in the bloody, world-churning aftermath of their revolution. A coterie of the country’s most influential philosophers (notably, Henri de Saint-Simon and Auguste Comte) were genuinely torn about the course of the country. They hated all the old ancient bastions of parasitic power – the feudal lords, the priests and the warriors – but they also feared the chaos of the mob. To split the difference, they proposed a form of technocracy – engineers and assorted technicians would rule with beneficent disinterestedness. Engineers would strip the old order of its power, while governing in the spirit of science. They would impose rationality and order.

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    There’s another way to describe this historical progression. Automation has come in waves. During the industrial revolution, machinery replaced manual workers. At first, machines required human operators. Over time, machines came to function with hardly any human intervention. For centuries, engineers automated physical labour; our new engineering elite has automated thought. They have perfected technologies that take over intellectual processes, that render the brain redundant. Or, as the former Google and Yahoo executive Marissa Mayer once argued, “You have to make words less human and more a piece of the machine.” Indeed, we have begun to outsource our intellectual work to companies that suggest what we should learn, the topics we should consider, and the items we ought to buy. These companies can justify their incursions into our lives with the very arguments that Saint-Simon and Comte articulated: they are supplying us with efficiency; they are imposing order on human life.

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    Like economics, computer science has its preferred models and implicit assumptions about the world. When programmers are taught algorithmic thinking, they are told to venerate efficiency as a paramount consideration. This is perfectly understandable. An algorithm with an ungainly number of steps will gum up the machinery, and a molasses-like server is a useless one. But efficiency is also a value. When we speed things up, we’re necessarily cutting corners; we’re generalising.

    Algorithms can be gorgeous expressions of logical thinking, not to mention a source of ease and wonder. They can track down copies of obscure 19th-century tomes in a few milliseconds; they put us in touch with long-lost elementary school friends; they enable retailers to deliver packages to our doors in a flash. Very soon, they will guide self-driving cars and pinpoint cancers growing in our innards. But to do all these things, algorithms are constantly taking our measure. They make decisions about us and on our behalf. The problem is that when we outsource thinking to machines, we are really outsourcing thinking to the organisations that run the machines.

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    But how do the engineers know which dial to twist and how hard? There’s a whole discipline, data science, to guide the writing and revision of algorithms. Facebook has a team, poached from academia, to conduct experiments on users. It’s a statistician’s sexiest dream – some of the largest data sets in human history, the ability to run trials on mathematically meaningful cohorts. When Cameron Marlow, the former head of Facebook’s data science team, described the opportunity, he began twitching with ecstatic joy. “For the first time,” Marlow said, “we have a microscope that not only lets us examine social behaviour at a very fine level that we’ve never been able to see before, but allows us to run experiments that millions of users are exposed to.”

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    The many Facebook experiments add up. The company believes that it has unlocked social psychology and acquired a deeper understanding of its users than they possess of themselves. Facebook can predict users’ race, sexual orientation, relationship status and drug use on the basis of their “likes” alone. It’s Zuckerberg’s fantasy that this data might be analysed to uncover the mother of all revelations, “a fundamental mathematical law underlying human social relationships that governs the balance of who and what we all care about”. That is, of course, a goal in the distance. In the meantime, Facebook will keep probing – constantly testing to see what we crave and what we ignore, a never-ending campaign to improve Facebook’s capacity to give us the things that we want and things we don’t even know we want. Whether the information is true or concocted, authoritative reporting or conspiratorial opinion, doesn’t really seem to matter much to Facebook. The crowd gets what it wants and deserves.

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    The engineering mindset has little patience for the fetishisation of words and images, for the mystique of art, for moral complexity or emotional expression. It views humans as data, components of systems, abstractions. That’s why Facebook has so few qualms about performing rampant experiments on its users. The whole effort is to make human beings predictable – to anticipate their behaviour, which makes them easier to manipulate. With this sort of cold-blooded thinking, so divorced from the contingency and mystery of human life, it’s easy to see how long-standing values begin to seem like an annoyance – why a concept such as privacy would carry so little weight in the engineer’s calculus, why the inefficiencies of publishing and journalism seem so imminently disruptable.

    Facebook would never put it this way, but algorithms are meant to erode free will, to relieve humans of the burden of choosing, to nudge them in the right direction. Algorithms fuel a sense of omnipotence, the condescending belief that our behaviour can be altered, without our even being aware of the hand guiding us, in a superior direction. That’s always been a danger of the engineering mindset, as it moves beyond its roots in building inanimate stuff and begins to design a more perfect social world. We are the screws and rivets in the grand design.
    https://www.theguardian.com/technolog...017/sep/19/facebooks-war-on-free-will
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    http://epocanegocios.globo.com/Revist...imento-da-renda-basica-universal.html
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  3. Part of something being “frictionless” is getting the human part out of the way.
    http://davidbyrne.com/journal/eliminating-the-human
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  4. Here’s an exercise: The next time you hear someone talking about algorithms, replace the term with “God” and ask yourself if the meaning changes.

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    The algorithmic metaphor is just a special version of the machine metaphor, one specifying a particular kind of machine (the computer) and a particular way of operating it (via a step-by-step procedure for calculation). And when left unseen, we are able to invent a transcendental ideal for the algorithm. The canonical algorithm is not just a model sequence but a concise and efficient one. In its ideological, mythic incarnation, the ideal algorithm is thought to be some flawless little trifle of lithe computer code, processing data into tapestry like a robotic silkworm. A perfect flower, elegant and pristine, simple and singular. A thing you can hold in your palm and caress. A beautiful thing. A divine one.

    But just as the machine metaphor gives us a distorted view of automated manufacture as prime mover, so the algorithmic metaphor gives us a distorted, theological view of computational action.

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    “The Google search algorithm” names something with an initial coherence that quickly scurries away once you really look for it. Googling isn’t a matter of invoking a programmatic subroutine—not on its own, anyway. Google is a monstrosity. It’s a confluence of physical, virtual, computational, and non-computational stuffs—electricity, data centers, servers, air conditioners, security guards, financial markets—just like the rubber ducky is a confluence of vinyl plastic, injection molding, the hands and labor of Chinese workers, the diesel fuel of ships and trains and trucks, the steel of shipping containers.

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    If algorithms aren’t gods, what are they instead? Like metaphors, algorithms are simplifications, or distortions. They are caricatures. They take a complex system from the world and abstract it into processes that capture some of that system’s logic and discard others. And they couple to other processes, machines, and materials that carry out the extra-computational part of their work.

    Unfortunately, most computing systems don’t want to admit that they are burlesques. They want to be innovators, disruptors, world-changers, and such zeal requires sectarian blindness. The exception is games, which willingly admit that they are caricatures—and which suffer the consequences of this admission in the court of public opinion. Games know that they are faking it, which makes them less susceptible to theologization. SimCity isn’t an urban planning tool, it’s a cartoon of urban planning. Imagine the folly of thinking otherwise! Yet, that’s precisely the belief people hold of Google and Facebook and the like.

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    This attitude blinds us in two ways. First, it allows us to chalk up any kind of computational social change as pre-determined and inevitable. It gives us an excuse not to intervene in the social shifts wrought by big corporations like Google or Facebook or their kindred, to see their outcomes as beyond our influence. Second, it makes us forget that particular computational systems are abstractions, caricatures of the world, one perspective among many. The first error turns computers into gods, the second treats their outputs as scripture.

    Computers are powerful devices that have allowed us to mimic countless other machines all at once. But in so doing, when pushed to their limits, that capacity to simulate anything reverses into the inability or unwillingness to distinguish one thing from anything else. In its Enlightenment incarnation, the rise of reason represented not only the ascendency of science but also the rise of skepticism, of incredulity at simplistic, totalizing answers, especially answers that made appeals to unseen movers. But today even as many scientists and technologists scorn traditional religious practice, they unwittingly invoke a new theology in so doing.

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    Let’s keep the computer around without fetishizing it, without bowing down to it or shrugging away its inevitable power over us, without melting everything down into it as a new name for fate. I don’t want an algorithmic culture, especially if that phrase just euphemizes a corporate, computational theocracy.
    https://www.theatlantic.com/technolog...1/the-cathedral-of-computation/384300
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  5. -
    http://theoldschooldevops.com/2014/09...rience-or-why-not-automate-everything
    Tags: , , by rhatto (2017-02-16) | Cache | Permalink
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    http://debops.org
    Tags: , , by rhatto (2015-06-30) | Cache | Permalink
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    http://devstructure.com/blueprint
    Tags: , by rhatto (2015-05-25) | Cache | Permalink
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    https://sslmate.com
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  9. -
    https://gitlab.com/groups/shared-puppet-modules-group
    Tags: , by rhatto (2015-02-22) | Cache | Permalink
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  10. -
    https://tech.immerda.ch/2015/01/introducing-ibox-stemcells
    Tags: , by rhatto (2015-02-06) | Cache | Permalink
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