Tuesday, May 24. 2016
Note: even people developing automation will be automated, so to say...
Do you want to change this existing (and predictable) future? This would be the right time to come with counter-proposals then...
But I'm quite surprized by the absence of nuanced analysis in the Wired article btw (am I? further than "make the workd a better place" I mean): indeed, this is a scientific achievement, but then what? no stakes? no social issues? It seems to be the way things should go then... (and some people know pretty well how "The Way Things Go", always wrong ;)), to the point that " No, Asimo isn’t quite as advanced—or as frightening—as Skynet." Good to know!
By Cade Metz
Deep neural networks are remaking the Internet. Able to learn very human tasks by analyzing vast amounts of digital data, these artificially intelligent systems are injecting online services with a power that just wasn’t viable in years past. They’re identifying faces in photos and recognizing commands spoken into smartphones and translating conversations from one language to another. They’re even helping Google choose its search results. All this we know. But what’s less discussed is how the giants of the Internet go about building these rather remarkable engines of AI.
Part of it is that companies like Google and Facebook pay top dollar for some really smart people. Only a few hundred souls on Earth have the talent and the training needed to really push the state-of-the-art forward, and paying for these top minds is a lot like paying for an NFL quarterback. That’s a bottleneck in the continued progress of artificial intelligence. And it’s not the only one. Even the top researchers can’t build these services without trial and error on an enormous scale. To build a deep neural network that cracks the next big AI problem, researchers must first try countless options that don’t work, running each one across dozens and potentially hundreds of machines.
“It’s almost like being the coach rather than the player,” says Demis Hassabis, co-founder of DeepMind, the Google outfit behind the history-making AI that beat the world’s best Go player. “You’re coaxing these things, rather than directly telling them what to do.”
That’s why many of these companies are now trying to automate this trial and error—or at least part of it. If you automate some of the heavily lifting, the thinking goes, you can more rapidly push the latest machine learning into the hands of rank-and-file engineers—and you can give the top minds more time to focus on bigger ideas and tougher problems. This, in turn, will accelerate the progress of AI inside the Internet apps and services that you and I use every day.
In other words, for computers to get smarter faster, computers themselves must handle even more of the grunt work. The giants of the Internet are building computing systems that can test countless machine learning algorithms on behalf of their engineers, that can cycle through so many possibilities on their own. Better yet, these companies are building AI algorithms that can help build AI algorithms. No joke. Inside Facebook, engineers have designed what they like to call an “automated machine learning engineer,” an artificially intelligent system that helps create artificially intelligent systems. It’s a long way from perfection. But the goal is to create new AI models using as little human grunt work as possible.
Feeling the Flow
After Facebook’s $104 billion IPO in 2012, Hussein Mehanna and other engineers on the Facebook ads team felt an added pressure to improve the company’s ad targeting, to more precisely match ads to the hundreds of millions of people using its social network. This meant building deep neural networks and other machine learning algorithms that could make better use of the vast amounts of data Facebook collects on the characteristics and behavior of those hundreds of millions of people.
According to Mehanna, Facebook engineers had no problem generating ideas for new AI, but testing these ideas was another matter. So he and his team built a tool called Flow. “We wanted to build a machine-learning assembly line that all engineers at Facebook could use,” Mehanna says. Flow is designed to help engineers build, test, and execute machine learning algorithms on a massive scale, and this includes practically any form of machine learning—a broad technology that covers all services capable of learning tasks largely on their own.
Basically, engineers could readily test an endless stream of ideas across the company’s sprawling network of computer data centers. They could run all sorts of algorithmic possibilities—involving not just deep learning but other forms of AI, including logistic regression to boosted decision trees—and the results could feed still more ideas. “The more ideas you try, the better,” Mehanna says. “The more data you try, the better.” It also meant that engineers could readily reuse algorithms that others had built, tweaking these algorithms and applying them to other tasks.
Soon, Mehanna and his team expanded Flow for use across the entire company. Inside other teams, it could help generate algorithms that could choose the links for your Faceboook News Feed, recognize faces in photos posted to the social network, or generate audio captions for photos so that the blind can understand what’s in them. It could even help the company determine what parts of the world still need access to the Internet.
With Flow, Mehanna says, Facebook trains and tests about 300,000 machine learning models each month. Whereas it once rolled a new AI model onto its social network every 60 days or so, it can now release several new models each week.
The Next Frontier
The idea is far bigger than Facebook. It’s common practice across the world of deep learning. Last year, Twitter acquired a startup, WhetLab, that specializes in this kind of thing, and recently, Microsoft described how its researchers use a system to test a sea of possible AI models. Microsoft researcher Jian Sun calls it “human-assisted search.”
Mehanna and Facebook want to accelerate this. The company plans to eventually open source Flow, sharing it with the world at large, and according to Mehanna, outfits like LinkedIn, Uber, and Twitter are already interested in using it. Mehanna and team have also built a tool called AutoML that can remove even more of the burden from human engineers. Running atop Flow, AutoML can automatically “clean” the data needed to train neural networks and other machine learning algorithms—prepare it for testing without any human intervention—and Mehanna envisions a version that could even gather the data on its own. But more intriguingly, AutoML uses artificial intelligence to help build artificial intelligence.
As Mehana says, Facebook trains and tests about 300,000 machine learning models each month. AutoML can then use the results of these tests to train another machine learning model that can optimize the training of machine learning models. Yes, that can be a hard thing to wrap your head around. Mehanna compares it to Inception. But it works. The system can automatically chooses algorithms and parameters that are likely to work. “It can almost predict the result before the training,” Mehanna says.
Inside the Facebook ads team, engineers even built that automated machine learning engineer, and this too has spread to the rest of the company. It’s called Asimo, and according to Facebook, there are cases where it can automatically generate enhanced and improved incarnations of existing models—models that human engineers can then instantly deploy to the net. “It cannot yet invent a new AI algorithm,” Mehanna says. “But who knows, down the road…”
It’s an intriguing idea—indeed, one that has captivated science fiction writers for decades: an intelligent machine that builds itself. No, Asimo isn’t quite as advanced—or as frightening—as Skynet. But it’s a step toward a world where so many others, not just the field’s sharpest minds, will build new AI. Some of those others won’t even be human.
Wednesday, April 27. 2016
Note: we blogged last week about automation and funilly, the Jacquard process was mentioned as one of the early stage of automation and computing during an exhibition in Wien. The "Métiers Jacquard" were an inspiration to Ch. Babbage when he started to design his Difference Engine, one of the early mechanic autonomous and programmable computer (in the sense of a calculator). We should also not forget that in reality, "computers" were real persons doing calculations -- often women (in particular during last world wars), which then became the first operators of automatic computers (see the ENIAC girls) -- until back in the middle of 20th century.
So to say, digital computers have already replaced "person computers" and automated, as well as by far quickened their activities... The first purpose of the computer as we know it was automation. It is part of its DNA.
Now, as a wink to this history but also as a possible "return of the repressed", Google literally enters the textile business and brings computing (back) to fabrics! So it is not by chance that they've picked up this name obviously, "Jacquard".
More about it on MIT Technology Review.
Thursday, April 21. 2016
Note: the idea of automation is very present again recently. And it is more and more put together with the related idea of a society without work, or insufficient work for everyone --which is already the case in the liberal way of thinking btw--, as most of it would be taken by autonomous machines, AIs, etc.
Many people are warning about this (Bill Gates among them, talking precisely about "software substitution"), some think about a "universal income" as a possible response, some say we shouldn't accept this and use our consumer power to reject such products (we spoke passionatey about it with my good old friend Eric Sadin last week during a meal at the Palais de Tokyo in Paris, while drinking --almost automatically as well-- some good wine), some say it is almost too late and we should plan and have visions for what is coming upon us...
Now comes also an exhibition about the same subject at Kunsthalle Wien that tries to articulate the questions: "Technical devices that were originally designed to serve and assist us and are now getting smarter and harder to control and comprehend. Does their growing autonomy mean that the machines will one day overpower us? Or will they remain our subservient little helpers, our gateway to greater knowledge and sovereignty?"
Installation view The Promise of Total Automation. Image Kunsthalle Wien
The word ‘automation’ is appearing in places that would have seemed unlikely to most people less than a decade ago: journalism, art, design or law. Robots and algorithms are being increasingly convincing at doing things just like humans. And sometimes even better than humans.
The Promise of Total Automation, an exhibition recently opened at Kunsthalle Wien in Vienna, looks at our troubled relationship with machines. Technical devices that were originally designed to serve and assist us and are now getting smarter and harder to control and comprehend. Does their growing autonomy mean that the machines will one day overpower us? Or will they remain our subservient little helpers, our gateway to greater knowledge and sovereignty?
The Promise of Total Automation is an intelligent, inquisitive and engrossing exhibition. Its investigation into the tensions and dilemmas of human/machines relationship explore themes that go from artificial intelligence to industrial aesthetics, from bio-politics to theories of conspiracy, from e-waste to resistance to innovation, from archaeology of digital communication to utopias that won’t die.
The show is dense in information and invitations to ponder so don’t forget to pick up one of the free information booklet at the entrance of the show. You’re going to need it!
A not-so-quick walk around the show:
James Benning, Stemple Pass, 2012
James Benning‘s film Stemple Pass is made of four static shots, each from the same angle and each 30 minutes long, showing a cabin in the middle of a forest in spring, fall, winter and summer. The modest building is a replica of the hideout of anti-technology terrorist Ted Kaczynski. The soundtrack alternates between the ambient sound of the forest and Benning reading from the Unabomber’s journals, encrypted documents and manifesto.
Kaczynski’s texts hover between his love for nature and his intention to destroy and murder. Between his daily life in the woods and his fears that technology is going to turn into an instrument that enables the powerful elite to take control over society. What is shocking is not so much the violence of his words because you expect them. It’s when he gets it right that you get upset. When he expresses his distrust of the merciless rise of technology, his doubts regarding the promises of innovation and it somehow makes sense to you.
Konrad Klapheck, Der Chef, 1965. Photo: © Museum Kunstpalast – ARTOTHEK
Konrad Klapheck’s paintings ‘portray’ devices that were becoming mainstream in 1960s households: vacuum cleaner, typewriters, sewing machines, telephones, etc. In his works, the objects are abstracted from any context, glorified and personified. In the typewriter series, he even assigns roles to the objects. They are Herrscher (ruler), Diktator, Gesetzgeber (lawgiver) or Chef (boss.) These titles allude to the important role that the instruments have taken in administrative and economic systems.
Tyler Coburn, Sabots, 2016, courtesy of the artist, photo: David Avazzadeh
This unassuming small pair of 3D-printed clogs alludes to the workers struggles of the Industrial Revolution. The title of the piece, Sabots, means clogs in french. The word sabotage allegedly comes from it. The story says that when French farmers left the countryside to come and work in factories they kept on wearing their peasant clogs. These shoes were not suited for factory works and as a consequence, the word ‘saboter’ came to mean ‘to work clumsily or incompetently’ or ‘to make a mess of things.’ Another apocryphal story says that disgruntled workers blamed the clogs when they damaged or tampered machinery. Another version saw the workers throwing their clogs at the machine to destroy it.
In the early 20th century, labor unions such as the Industrial Workers of the World (IWW) advocated withdrawal of efficiency as a means of self-defense against unfair working conditions. They called it sabotage.
Tyler Coburn contributed another work to the show. Waste Management looks like a pair of natural stones but the rocks are actually made out of electronic waste, more precisely the glass from old computer monitors and fiber powder from printed circuit boards that were mixed with epoxy and then molded in an electronic recycling factory in Taiwan. The country is not only a leader in the export of electronics, but also in the development of e-waste processing technologies that turn electronic trash into architectural bricks, gold potassium cyanide, precious metals—and even artworks such as these rocks. Coburn bought them there as a ready made. They evoke the Chinese scholar’s rocks. By the early Song dynasty (960–1279), the Chinese started collecting small ornamental rocks, especially the rocks that had been sculpted naturally by processes of erosion.
Accompanying these objects is a printed broadsheet which narrates the circulation and transformation of a CRT monitor into the stone artworks. The story follows from the “it-narrative” or novel of circulation, a sub-genre of 18th Century literature, in which currencies and commodities narrated their circulation within a then-emerging global economy.
Osborne & Felsenstein, Personal Computer Osborne 1a and Monitor NEC, 1981, Loan Vienna Technical Museum, photo: David Avazzadeh
Adam Osborne and Lee Felsenstein, Personal Computer Osborne 1a, 1981, Courtesy Technisches Museum, Wien
Several artifacts ground the exhibition into the technological and cultural history of automation: A mechanical Jacquard loom, often regarded as a key step in the history of computing hardware because of the way it used punched cards to control operations. A mysterious-looking arithmometer, the first digital mechanical calculator reliable enough to be used at the office to automate mathematical calculations. A Morse code telegraph, the first invention to effectively exploit electromagnetism for long-distance communication and thus a pioneer of digital communication. A cybernetic model from 1956 (see further below) and the first ‘portable’ computer.
Released in 1981 by Osborne Computer Corporation, the Osborne 1 was the first commercially successful portable microcomputer. It weighed 10.7 kg (23.5 lb), cost $1,795 USD, had a tiny screen (5-inch/13 cm) and no battery.
At the peak of demand, Osborne was shipping over 10,000 units a month. However, Osborne Computer Corporation shot itself in the foot when they prematurely announced the release of their next generation models. The news put a stop to the sales of the current unit, contributing to throwing the company into bankruptcy. This has comes to be known as the Osborne effect.
Kybernetisches Modell Eier: Die Maus im Labyrinth (Cybernetics Model Eier: The Mouse in the Maze), 1956. Image Kunsthalle Wien
Around 1960, scientists started to build cybernetic machines in order to study artificial intelligence. One of these machines was a maze-solving mouse built by Claude E. Shannon to study the labyrinthian path that a call made using telephone switching systems should take to reach its destination. The device contained a maze that could be arranged to create various paths. The system followed the idea of Ariadne’s thread, the mouse marking each field with the path information, like the Greek mythological figure did when she helped Theseus find his way out of the Minotaur’s labyrinth. Richard Eier later re-built the maze-solving mouse and improved Shannon’s method by replacing the thread with two two-bits memory units.
Régis Mayot, JEANNE & CIE, 2015. Image Kunsthalle Wien
In 2011, the CIAV (the international center for studio glass in Meisenthal, France) invited Régis Mayot to work in their studios. The designer decided to explore the moulds themselves, rather than the objects that were produced using them. By a process of sand moulding, the designer revealed the mechanical beauty of some of these historical tools, producing prints of a selection of moulds that were then blown by craftsmen in glass.
Jeanne et Cie (named after one of the moulds chosen by the designer) highlights how the aesthetics of objects are the result of the industrial instruments and processes that enter into their manufacturing.
Bureau d’études, ME, 2013, © Léonore Bonaccini and Xavier Fourt
Bureau d’Etudes, Electromagnetic Propaganda, 2010
The exhibition also presented a selection of Bureau d´Études‘ intricate and compelling cartographies that visualize covert connections between actors and interests in contemporary political, social and economic systems. Because knowledge is power, the maps are meant as instruments that can be used as part of social movements. The ones displayed at Kunsthalle Wien included the maps of Electro-Magnetic Propaganda, Government of the Agro-Industrial System and the 8th Sphere.
I fell in love with Mark Leckey‘s video. So much that i’ll have to dedicate another post to his work. One day.
David Jourdan’s poster alludes to an ad in which newspaper Der Standard announced that its digital format was ‘almost as good as paper.’
More images from the exhibition:
Magali Reus, Leaves, 2015
Thomas Bayrle, Kleiner koreanischer Wiper
Juan Downey, Nostalgic Item, 1967, Estate of Joan Downey courtesy of Marilys B. Downey, photo: David Avazzadeh
Judith Fegerl, still, 2013, © Judith Fegerl, Courtesy Galerie Hubert Winter, Wien
Installation view The Promise of Total Automation. Image Kunsthalle Wien
Installation view. Image Kunsthalle Wien
Installation view. Image Kunsthalle Wien
More images on my flickr album.
Also in the exhibition: Prototype II (after US patent no 6545444 B2) or the quest for free energy.
The Promise of Total Automation was curated by Anne Faucheret. The exhibition is open until 29 May at Kunsthalle Wien in Vienna. Don’t miss it if you’re in the area.
Wednesday, April 06. 2016
If not "algorithmic communism" then "algorithmic liberalism"? > At Uber, the Algorithm Is More Controlling Than the Real Boss 7 #algorithms #politics #economy
Note: to pursue fueling this resource and reflection about what kind of social, political and economic rules are implemented within algorithms that will then become the foundation layer of the so called "world without work"... (and to get how far current political parties seem not to address these stakes), here comes an interesting study to exemplify what algorithmic rules can be(come) and how they implement a "way of thinking", in this case at Uber. By extension, think of course about Amazon's automated or crowdsourced services, AirBn'B, etc.
Obviously, these algorithms are already being written right "under our noses" (think about algorithmic trading, smart cities, smart "things" & "stuff", autonomous cars and drones, etc.), certainly under the radar but not not under an "algorithmic communism" basis. Not that we know about ...
Keith Bedford for The Wall Street Journal
In defending his company against assertions that Uber drivers should be classified as employees, Uber CEO Travis Kalanick often wields the algorithm. Uber isn’t a boss, he argues. It’s a software platform that balances supply and demand to connect entrepreneurs with customers.
A new academic paper pokes holes in that argument.
Researchers Alex Rosenblat and Luke Stark at the Data and Society Research Institute and New York University point out that Uber uses software to exert similar control over workers that a human manager would. The company’s algorithm uses performance metrics, scheduling prompts, behavioral suggestions, dynamic prices, and information asymmetry “as a substitute for direct managerial power and control,” they wrote.
Uber did not immediately respond to a request for comment.
The researchers, who conducted in-depth interviews with Uber drivers and studied posts in drivers-only online forums, situate Uber and similar sharing-economy platforms in a wider conversation about the trend toward employee management and so-called on demand or predictive scheduling software. Starbucks, for instance, hasn’t replaced traditional managers, but it’s among a growing group of companies that increasingly rely on software to manage worker schedules and behavior.
Bottom line: Robots aren’t stealing your job – at least in this instance – but they’re becoming your boss. And the level of control and surveillance they exert is often far greater than human management would, the authors found.
Rather than undertaking a human-driven performance review process, Uber evaluates employees according to an automated rating system. Riders enter scores into the Uber app to rate drivers with one to five stars. Back-end software tallies the scores and sends drivers regular summaries of their performance and how they stack up to their peers.
The system, the researchers wrote, empowers Uber customers to serve as “middle managers,” essentially outsourcing management. It lets Uber “achieve an organization where the workforce behaves relatively homogeneously” without needing a manager to bark orders.
Uber’s software also exerts control over when and where drivers work, the researchers noted. The company never orders workers to drive, but its software does prod them. It alerts them when the software predicts that surge pricing is due to kick in, boosting the fare by up to four times and increasing the driver’s fee.
But drivers reported that it was difficult to tell the difference between the company’s predictions and an actual surge. They often showed up at a surge location to find the area saturated with drivers and the company no longer offering to reward them with higher payments.
Thus Uber’s software is not passive but manipulates the supply of labor and shapes the marketplace as a whole, the authors argued.
Drivers told the researchers they resisted Uber by failing to reply to company emails inquiring about their whereabouts and by posting on message boards to advise other drivers to “resist the surge.” They said they didn’t want Uber to know where they planned to be for fear the company would trick them into driving elsewhere without delivering the benefit of higher fees. Essentially, the authors said, Uber drivers resist the algorithm-boss by trying to trick him – perhaps not unlike the decisions traditional employees make about what information to share with a human boss.
Despite Uber’s depiction of drivers as entrepreneurs who control their own labor, an environment in which Uber has all the information makes it harder for drivers to make decisions that are in their interest, the authors said. Uber drivers are discouraged from turning down a fare – in some cities, drivers are prodded to pick up 90% of passengers who request a pickup – and they aren’t given fare information in advance. Drivers complained that this asymmetry resulted in sometimes losing money, since some rides are too short to be worthwhile, and they have no way to know how much they could expect to earn.
Of course, many of the practices that benefit Uber and annoy its drivers also benefit customers. And like Starbucks employees and other workers whose lives are made unpredictable by such predictive scheduling software, Uber drivers are free to quit. In that sense, they are the self-determined entrepreneurs that Uber describes. But in other ways, they clearly aren’t.
Monday, April 04. 2016
On Algorithmic Communism - Ian Lowrie on Inventing the Future : Postcapitalism and a World Without Work | #algorithms #future #postcapitalism
Note: in a time when we'll soon have for the first time a national vote in Switzeralnd about the Revenu de Base Inconditionnel ("Universal Basic Income") --next June, with a low chance of success this time, let's face it--, when people start to speak about the fact that they should get incomes to fuel global corporations with digital data and content of all sorts, when some new technologies could modify the current digital deal, this is a manifesto that is certainly more than interesting to consider. So as its criticism in this paper, as it appears truly complementary.
More generally, thinking the Future in different terms than liberalism is an absolute necessity. Especially in a context where, also as stated, "Automation and unemployment are the future, regardless of any human intervention".
By Ian Lowrie
January 8th, 2016
IN THE NEXT FEW DECADES, your job is likely to be automated out of existence. If things keep going at this pace, it will be great news for capitalism. You’ll join the floating global surplus population, used as a threat and cudgel against those “lucky” enough to still be working in one of the few increasingly low-paying roles requiring human input. Existing racial and geographical disparities in standards of living will intensify as high-skill, high-wage, low-control jobs become more rarified and centralized, while the global financial class shrinks and consolidates its power. National borders will continue to be used to control the flow of populations and place migrant workers outside of the law. The environment will continue to be the object of vicious extraction and the dumping ground for the negative externalities of capitalist modes of production.
It doesn’t have to be this way, though. While neoliberal capitalism has been remarkably successful at laying claim to the future, it used to belong to the left — to the party of utopia. Nick Srnicek and Alex Williams’s Inventing the Future argues that the contemporary left must revive its historically central mission of imaginative engagement with futurity. It must refuse the all-too-easy trap of dismissing visions of technological and social progress as neoliberal fantasies. It must seize the contemporary moment of increasing technological sophistication to demand a post-scarcity future where people are no longer obliged to be workers; where production and distribution are democratically delegated to a largely automated infrastructure; where people are free to fish in the afternoon and criticize after dinner. It must combine a utopian imagination with the patient organizational work necessary to wrest the future from the clutches of hegemonic neoliberalism.
Strategies and Tactics
In making such claims, Srnicek and Williams are definitely preaching to the leftist choir, rather than trying to convert the masses. However, this choir is not just the audience for, but also the object of, their most vituperative criticism. Indeed, they spend a great deal of the book arguing that the contemporary left has abandoned strategy, universalism, abstraction, and the hard work of building workable, global alternatives to capitalism. Somewhat condescendingly, they group together the highly variegated field of contemporary leftist tactics and organizational forms under the rubric of “folk politics,” which they argue characterizes a commitment to local, horizontal, and immediate actions. The essentially affective, gestural, and experimental politics of movements such as Occupy, for them, are a retreat from the tradition of serious militant politics, to something like “politics-as-drug-experience.”
Whatever their problems with the psychodynamics of such actions, Srnicek and Williams argue convincingly that localism and small-scale, prefigurative politics are simply inadequate to challenging the ideological dominance of neoliberalism — they are out of step with the actualities of the global capitalist system. While they admire the contemporary left’s commitment to self-interrogation, and its micropolitical dedication to the “complete removal of all forms of oppression,” Srnicek and Williams are ultimately neo-Marxists, committed to the view that “[t]he reality of complex, globalised capitalism is that small interventions consisting of relatively non-scalable actions are highly unlikely to ever be able to reorganise our socioeconomic system.” The antidote to this slow localism, however, is decidedly not fast revolution.
Instead, Inventing the Future insists that the left must learn from the strategies that ushered in the currently ascendant neoliberal hegemony. Inventing the Future doesn’t spend a great deal of time luxuriating in pathos, preferring to learn from their enemies’ successes rather than lament their excesses. Indeed, the most empirically interesting chunk of their book is its careful chronicle of the gradual, stepwise movement of neoliberalism from the “fringe theory” of a small group of radicals to the dominant ideological consensus of contemporary capitalism. They trace the roots of the “neoliberal thought collective” to a diverse range of trends in pre–World War II economic thought, which came together in the establishment of a broad publishing and advocacy network in the 1950s, with the explicit strategic aim of winning the hearts and minds of economists, politicians, and journalists. Ultimately, this strategy paid off in the bloodless neoliberal revolutions during the international crises of Keynesianism that emerged in the 1980s.
What made these putsches successful was not just the neoliberal thought collective’s ability to represent political centrism, rational universalism, and scientific abstraction, but also its commitment to organizational hierarchy, internal secrecy, strategic planning, and the establishment of an infrastructure for ideological diffusion. Indeed, the former is in large part an effect of the latter: by the 1980s, neoliberals had already spent decades engaged in the “long-term redefinition of the possible,” ensuring that the institutional and ideological architecture of neoliberalism was already well in place when the economic crises opened the space for swift, expedient action.
Srnicek and Williams argue that the left must abandon its naïve-Marxist hopes that, somehow, crisis itself will provide the space for direct action to seize the hegemonic position. Instead, it must learn to play the long game as well. It must concentrate on building institutional frameworks and strategic vision, cultivating its own populist universalism to oppose the elite universalism of neoliberal capital. It must also abandon, in so doing, its fear of organizational closure, hierarchy, and rationality, learning instead to embrace them as critical tactical components of universal politics.
There’s nothing particularly new about Srnicek and Williams’s analysis here, however new the problems they identify with the collapse of the left into particularism and localism may be. For the most part, in their vituperations, they are acting as rather straightforward, if somewhat vernacular, followers of the Italian politician and Marxist theorist Antonio Gramsci. As was Gramsci’s, their political vision is one of slow, organizationally sophisticated, passive revolution against the ideological, political, and economic hegemony of capitalism. The gradual war against neoliberalism they envision involves critique and direct action, but will ultimately be won by the establishment of a post-work counterhegemony.
In putting forward their vision of this organization, they strive to articulate demands that would allow for the integration of a wide range of leftist orientations under one populist framework. Most explicitly, they call for the automation of production and the provision of a basic universal income that would provide each person the opportunity to decide how they want to spend their free time: in short, they are calling for the end of work, and for the ideological architecture that supports it. This demand is both utopian and practical; they more or less convincingly argue that a populist, anti-work, pro-automation platform might allow feminist, antiracist, anticapitalist, environmental, anarchist, and postcolonial struggles to become organized together and reinforce one another. Their demands are universal, but designed to reflect a rational universalism that “integrates difference rather than erasing it.” The universal struggle for the future is a struggle for and around “an empty placeholder that is impossible to fill definitively” or finally: the beginning, not the end, of a conversation.
In demanding full automation of production and a universal basic income, Srnicek and Williams are not being millenarian, not calling for a complete rupture with the present, for a complete dismantling and reconfiguration of contemporary political economy. On the contrary, they argue that “it is imperative […] that [the left’s] vision of a new future be grounded upon actually existing tendencies.” Automation and unemployment are the future, regardless of any human intervention; the momentum may be too great to stop the train, but they argue that we can change tracks, can change the meaning of a future without work. In demanding something like fully automated luxury communism, Srnicek and Williams are ultimately asserting the rights of humanity as a whole to share in the spoils of capitalism.
Inventing the Future emerged to a relatively high level of fanfare from leftist social media. Given the publicity, it is unsurprising that other more “engagé” readers have already advanced trenchant and substantive critiques of the future imagined by Srnicek and Williams. More than a few of these critics have pointed out that, despite their repeated insistence that their post-work future is an ecologically sound one, Srnicek and Williams evince roughly zero self-reflection with respect either to the imbrication of microelectronics with brutally extractive regimes of production, or to their own decidedly antiquated, doctrinaire Marxist understanding of humanity’s relationship towards the nonhuman world. Similarly, the question of what the future might mean in the Anthropocene goes largely unexamined.
More damningly, however, others have pointed out that despite the acknowledged counterintuitiveness of their insistence that we must reclaim European universalism against the proliferation of leftist particularisms, their discussions of postcolonial struggle and critique are incredibly shallow. They are keen to insist that their universalism will embrace rather than flatten difference, that it will be somehow less brutal and oppressive than other forms of European univeralism, but do little of the hard argumentative work necessary to support these claims. While we see the start of an answer in their assertion that the rejection of universal access to discourses of science, progress, and rationality might actually function to cement certain subject-positions’ particularity, this — unfortunately — remains only an assertion. At best, they are being uncharitable to potential allies in refusing to take their arguments seriously; at worst, they are unreflexively replicating the form if not the content of patriarchal, racist, and neocolonial capitalist rationality.
For my part, while I find their aggressive and unapologetic presentation of their universalism somewhat off-putting, their project is somewhat harder to criticize than their book — especially as someone acutely aware of the need for more serious forms of organized thinking about the future if we’re trying to push beyond the horizons offered by the neoliberal consensus.
However, as an anthropologist of the computer and data sciences, it’s hard for me to ignore a curious and rather serious lacuna in their thinking about automaticity, algorithms, and computation. Beyond the automation of work itself, they are keen to argue that with contemporary advances in machine intelligence, the time has come to revisit the planned economy. However, in so doing, they curiously seem to ignore how this form of planning threatens to hive off economic activity from political intervention. Instead of fearing a repeat of the privations that poor planning produced in earlier decades, the left should be more concerned with the forms of control and dispossession successful planning produced. The past decade has seen a wealth of social-theoretical research into contemporary forms of algorithmic rationality and control, which has rather convincingly demonstrated the inescapable partiality of such systems and their tendency to be employed as decidedly undemocratic forms of technocratic management.
Srnicek and Williams, however, seem more or less unaware of, or perhaps uninterested in, such research. At the very least, they are extremely overoptimistic about the democratization and diffusion of expertise that would be required for informed mass control over an economy planned by machine intelligence. I agree with their assertion that “any future left must be as technically fluent as it is politically fluent.” However, their definition of technical fluency is exceptionally narrow, confined to an understanding of the affordances and internal dynamics of technical systems rather than a comprehensive analysis of their ramifications within other social structures and processes. I do not mean to suggest that the democratic application of machine learning and complex systems management is somehow a priori impossible, but rather that Srnicek and Williams do not even seem to see how such systems might pose a challenge to human control over the means of production.
In a very real sense, though, my criticisms should be viewed as a part of the very project proposed in the book. Inventing the Future is unapologetically a manifesto, and a much-overdue clarion call to a seriously disorganized metropolitan left to get its shit together, to start thinking — and arguing — seriously about what is to be done. Manifestos, like demands, need to be pointed enough to inspire, while being vague enough to promote dialogue, argument, dissent, and ultimately action. It’s a hard tightrope to walk, and Srnicek and Williams are not always successful. However, Inventing the Future points towards an altogether more coherent and mature project than does their #ACCELERATE MANIFESTO. It is hard to deny the persuasiveness with which the book puts forward the positive contents of a new and vigorous populism; in demanding full automation and universal basic income from the world system, they also demand the return of utopian thinking and serious organization from the left.
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fabric | rblg
This blog is the survey website of fabric | ch - studio for architecture, interaction and research.
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