No matter how hard they try, brain scientists and cognitive psychologists will never find a copy of Beethoven’s 5th Symphony in the brain – or copies of words, pictures, grammatical rules or any other kinds of environmental stimuli. The human brain isn’t really empty, of course. But it does not contain most of the things people think it does – not even simple things such as ‘memories’.
Our shoddy thinking about the brain has deep historical roots, but the invention of computers in the 1940s got us especially confused. For more than half a century now, psychologists, linguists, neuroscientists and other experts on human behaviour have been asserting that the human brain works like a computer.
To see how vacuous this idea is, consider the brains of babies. Thanks to evolution, human neonates, like the newborns of all other mammalian species, enter the world prepared to interact with it effectively. A baby’s vision is blurry, but it pays special attention to faces, and is quickly able to identify its mother’s. It prefers the sound of voices to non-speech sounds, and can distinguish one basic speech sound from another. We are, without doubt, built to make social connections.
A healthy newborn is also equipped with more than a dozen reflexes – ready-made reactions to certain stimuli that are important for its survival. It turns its head in the direction of something that brushes its cheek and then sucks whatever enters its mouth. It holds its breath when submerged in water. It grasps things placed in its hands so strongly it can nearly support its own weight. Perhaps most important, newborns come equipped with powerful learning mechanisms that allow them to change rapidly so they can interact increasingly effectively with their world, even if that world is unlike the one their distant ancestors faced.
Senses, reflexes and learning mechanisms – this is what we start with, and it is quite a lot, when you think about it. If we lacked any of these capabilities at birth, we would probably have trouble surviving.
But here is what we are not born with: information, data, rules, software, knowledge, lexicons, representations, algorithms, programs, models, memories, images, processors, subroutines, encoders, decoders, symbols, or buffers – design elements that allow digital computers to behave somewhat intelligently. Not only are we not born with such things, we also don’t develop them – ever.
We don’t store words or the rules that tell us how to manipulate them. We don’t create representations of visual stimuli, store them in a short-term memory buffer, and then transfer the representation into a long-term memory device. We don’t retrieve information or images or words from memory registers. Computers do all of these things, but organisms do not.
Computers, quite literally, process information – numbers, letters, words, formulas, images. The information first has to be encoded into a format computers can use, which means patterns of ones and zeroes (‘bits’) organised into small chunks (‘bytes’). On my computer, each byte contains 8 bits, and a certain pattern of those bits stands for the letter d, another for the letter o, and another for the letter g. Side by side, those three bytes form the word dog. One single image – say, the photograph of my cat Henry on my desktop – is represented by a very specific pattern of a million of these bytes (‘one megabyte’), surrounded by some special characters that tell the computer to expect an image, not a word.
Computers, quite literally, move these patterns from place to place in different physical storage areas etched into electronic components. Sometimes they also copy the patterns, and sometimes they transform them in various ways – say, when we are correcting errors in a manuscript or when we are touching up a photograph. The rules computers follow for moving, copying and operating on these arrays of data are also stored inside the computer. Together, a set of rules is called a ‘program’ or an ‘algorithm’. A group of algorithms that work together to help us do something (like buy stocks or find a date online) is called an ‘application’ – what most people now call an ‘app’.
Forgive me for this introduction to computing, but I need to be clear: computers really do operate on symbolic representations of the world. They really store and retrieve. They really process. They really have physical memories. They really are guided in everything they do, without exception, by algorithms.
Humans, on the other hand, do not – never did, never will. Given this reality, why do so many scientists talk about our mental life as if we were computers?
In his book In Our Own Image (2015), the artificial intelligence expert George Zarkadakis describes six different metaphors people have employed over the past 2,000 years to try to explain human intelligence.
In the earliest one, eventually preserved in the Bible, humans were formed from clay or dirt, which an intelligent god then infused with its spirit. That spirit ‘explained’ our intelligence – grammatically, at least.
The invention of hydraulic engineering in the 3rd century BCE led to the popularity of a hydraulic model of human intelligence, the idea that the flow of different fluids in the body – the ‘humours’ – accounted for both our physical and mental functioning. The hydraulic metaphor persisted for more than 1,600 years, handicapping medical practice all the while.
By the 1500s, automata powered by springs and gears had been devised, eventually inspiring leading thinkers such as René Descartes to assert that humans are complex machines. In the 1600s, the British philosopher Thomas Hobbes suggested that thinking arose from small mechanical motions in the brain. By the 1700s, discoveries about electricity and chemistry led to new theories of human intelligence – again, largely metaphorical in nature. In the mid-1800s, inspired by recent advances in communications, the German physicist Hermann von Helmholtz compared the brain to a telegraph.
"The mathematician John von Neumann stated flatly that the function of the human nervous system is ‘prima facie digital’, drawing parallel after parallel between the components of the computing machines of the day and the components of the human brain"
Each metaphor reflected the most advanced thinking of the era that spawned it. Predictably, just a few years after the dawn of computer technology in the 1940s, the brain was said to operate like a computer, with the role of physical hardware played by the brain itself and our thoughts serving as software. The landmark event that launched what is now broadly called ‘cognitive science’ was the publication of Language and Communication (1951) by the psychologist George Miller. Miller proposed that the mental world could be studied rigorously using concepts from information theory, computation and linguistics.
This kind of thinking was taken to its ultimate expression in the short book The Computer and the Brain (1958), in which the mathematician John von Neumann stated flatly that the function of the human nervous system is ‘prima facie digital’. Although he acknowledged that little was actually known about the role the brain played in human reasoning and memory, he drew parallel after parallel between the components of the computing machines of the day and the components of the human brain.
Propelled by subsequent advances in both computer technology and brain research, an ambitious multidisciplinary effort to understand human intelligence gradually developed, firmly rooted in the idea that humans are, like computers, information processors. This effort now involves thousands of researchers, consumes billions of dollars in funding, and has generated a vast literature consisting of both technical and mainstream articles and books. Ray Kurzweil’s book How to Create a Mind: The Secret of Human Thought Revealed (2013), exemplifies this perspective, speculating about the ‘algorithms’ of the brain, how the brain ‘processes data’, and even how it superficially resembles integrated circuits in its structure.
The information processing (IP) metaphor of human intelligence now dominates human thinking, both on the street and in the sciences. There is virtually no form of discourse about intelligent human behaviour that proceeds without employing this metaphor, just as no form of discourse about intelligent human behaviour could proceed in certain eras and cultures without reference to a spirit or deity. The validity of the IP metaphor in today’s world is generally assumed without question.
But the IP metaphor is, after all, just another metaphor – a story we tell to make sense of something we don’t actually understand. And like all the metaphors that preceded it, it will certainly be cast aside at some point – either replaced by another metaphor or, in the end, replaced by actual knowledge.
Just over a year ago, on a visit to one of the world’s most prestigious research institutes, I challenged researchers there to account for intelligent human behaviour without reference to any aspect of the IP metaphor. They couldn’t do it, and when I politely raised the issue in subsequent email communications, they still had nothing to offer months later. They saw the problem. They didn’t dismiss the challenge as trivial. But they couldn’t offer an alternative. In other words, the IP metaphor is ‘sticky’. It encumbers our thinking with language and ideas that are so powerful we have trouble thinking around them.
The faulty logic of the IP metaphor is easy enough to state. It is based on a faulty syllogism – one with two reasonable premises and a faulty conclusion. Reasonable premise #1: all computers are capable of behaving intelligently. Reasonable premise #2: all computers are information processors. Faulty conclusion: all entities that are capable of behaving intelligently are information processors.
Setting aside the formal language, the idea that humans must be information processors just because computers are information processors is just plain silly, and when, some day, the IP metaphor is finally abandoned, it will almost certainly be seen that way by historians, just as we now view the hydraulic and mechanical metaphors to be silly.
If the IP metaphor is so silly, why is it so sticky? What is stopping us from brushing it aside, just as we might brush aside a branch that was blocking our path? Is there a way to understand human intelligence without leaning on a flimsy intellectual crutch? And what price have we paid for leaning so heavily on this particular crutch for so long? The IP metaphor, after all, has been guiding the writing and thinking of a large number of researchers in multiple fields for decades. At what cost?
In a classroom exercise I have conducted many times over the years, I begin by recruiting a student to draw a detailed picture of a dollar bill – ‘as detailed as possible’, I say – on the blackboard in front of the room. When the student has finished, I cover the drawing with a sheet of paper, remove a dollar bill from my wallet, tape it to the board, and ask the student to repeat the task. When he or she is done, I remove the cover from the first drawing, and the class comments on the differences.
Because you might never have seen a demonstration like this, or because you might have trouble imagining the outcome, I have asked Jinny Hyun, one of the student interns at the institute where I conduct my research, to make the two drawings. Here is her drawing ‘from memory’ (notice the metaphor):
And here is the drawing she subsequently made with a dollar bill present:
Jinny was as surprised by the outcome as you probably are, but it is typical. As you can see, the drawing made in the absence of the dollar bill is horrible compared with the drawing made from an exemplar, even though Jinny has seen a dollar bill thousands of times.
What is the problem? Don’t we have a ‘representation’ of the dollar bill ‘stored’ in a ‘memory register’ in our brains? Can’t we just ‘retrieve’ it and use it to make our drawing?
Obviously not, and a thousand years of neuroscience will never locate a representation of a dollar bill stored inside the human brain for the simple reason that it is not there to be found.
"The idea that memories are stored in individual neurons is preposterous: how and where is the memory stored in the cell?"
A wealth of brain studies tells us, in fact, that multiple and sometimes large areas of the brain are often involved in even the most mundane memory tasks. When strong emotions are involved, millions of neurons can become more active. In a 2016 study of survivors of a plane crash by the University of Toronto neuropsychologist Brian Levine and others, recalling the crash increased neural activity in ‘the amygdala, medial temporal lobe, anterior and posterior midline, and visual cortex’ of the passengers.
The idea, advanced by several scientists, that specific memories are somehow stored in individual neurons is preposterous; if anything, that assertion just pushes the problem of memory to an even more challenging level: how and where, after all, is the memory stored in the cell?
So what is occurring when Jinny draws the dollar bill in its absence? If Jinny had never seen a dollar bill before, her first drawing would probably have not resembled the second drawing at all. Having seen dollar bills before, she was changed in some way. Specifically, her brain was changed in a way that allowed her to visualise a dollar bill – that is, to re-experience seeing a dollar bill, at least to some extent.
The difference between the two diagrams reminds us that visualising something (that is, seeing something in its absence) is far less accurate than seeing something in its presence. This is why we’re much better at recognising than recalling. When we re-member something (from the Latin re, ‘again’, and memorari, ‘be mindful of’), we have to try to relive an experience; but when we recognise something, we must merely be conscious of the fact that we have had this perceptual experience before.
Perhaps you will object to this demonstration. Jinny had seen dollar bills before, but she hadn’t made a deliberate effort to ‘memorise’ the details. Had she done so, you might argue, she could presumably have drawn the second image without the bill being present. Even in this case, though, no image of the dollar bill has in any sense been ‘stored’ in Jinny’s brain. She has simply become better prepared to draw it accurately, just as, through practice, a pianist becomes more skilled in playing a concerto without somehow inhaling a copy of the sheet music.
From this simple exercise, we can begin to build the framework of a metaphor-free theory of intelligent human behaviour – one in which the brain isn’t completely empty, but is at least empty of the baggage of the IP metaphor.
As we navigate through the world, we are changed by a variety of experiences. Of special note are experiences of three types: (1) we observe what is happening around us (other people behaving, sounds of music, instructions directed at us, words on pages, images on screens); (2) we are exposed to the pairing of unimportant stimuli (such as sirens) with important stimuli (such as the appearance of police cars); (3) we are punished or rewarded for behaving in certain ways.
We become more effective in our lives if we change in ways that are consistent with these experiences – if we can now recite a poem or sing a song, if we are able to follow the instructions we are given, if we respond to the unimportant stimuli more like we do to the important stimuli, if we refrain from behaving in ways that were punished, if we behave more frequently in ways that were rewarded.
Misleading headlines notwithstanding, no one really has the slightest idea how the brain changes after we have learned to sing a song or recite a poem. But neither the song nor the poem has been ‘stored’ in it. The brain has simply changed in an orderly way that now allows us to sing the song or recite the poem under certain conditions. When called on to perform, neither the song nor the poem is in any sense ‘retrieved’ from anywhere in the brain, any more than my finger movements are ‘retrieved’ when I tap my finger on my desk. We simply sing or recite – no retrieval necessary.
A few years ago, I asked the neuroscientist Eric Kandel of Columbia University – winner of a Nobel Prize for identifying some of the chemical changes that take place in the neuronal synapses of the Aplysia (a marine snail) after it learns something – how long he thought it would take us to understand how human memory works. He quickly replied: ‘A hundred years.’ I didn’t think to ask him whether he thought the IP metaphor was slowing down neuroscience, but some neuroscientists are indeed beginning to think the unthinkable – that the metaphor is not indispensable.
A few cognitive scientists – notably Anthony Chemero of the University of Cincinnati, the author of Radical Embodied Cognitive Science (2009) – now completely reject the view that the human brain works like a computer. The mainstream view is that we, like computers, make sense of the world by performing computations on mental representations of it, but Chemero and others describe another way of understanding intelligent behaviour – as a direct interaction between organisms and their world.
My favourite example of the dramatic difference between the IP perspective and what some now call the ‘anti-representational’ view of human functioning involves two different ways of explaining how a baseball player manages to catch a fly ball – beautifully explicated by Michael McBeath, now at Arizona State University, and his colleagues in a 1995 paper in Science. The IP perspective requires the player to formulate an estimate of various initial conditions of the ball’s flight – the force of the impact, the angle of the trajectory, that kind of thing – then to create and analyse an internal model of the path along which the ball will likely move, then to use that model to guide and adjust motor movements continuously in time in order to intercept the ball.
That is all well and good if we functioned as computers do, but McBeath and his colleagues gave a simpler account: to catch the ball, the player simply needs to keep moving in a way that keeps the ball in a constant visual relationship with respect to home plate and the surrounding scenery (technically, in a ‘linear optical trajectory’). This might sound complicated, but it is actually incredibly simple, and completely free of computations, representations and algorithms.
"We will never have to worry about a human mind going amok in cyberspace, and we will never achieve immortality through downloading."
Two determined psychology professors at Leeds Beckett University in the UK – Andrew Wilson and Sabrina Golonka – include the baseball example among many others that can be looked at simply and sensibly outside the IP framework. They have been blogging for years about what they call a ‘more coherent, naturalised approach to the scientific study of human behaviour… at odds with the dominant cognitive neuroscience approach’. This is far from a movement, however; the mainstream cognitive sciences continue to wallow uncritically in the IP metaphor, and some of the world’s most influential thinkers have made grand predictions about humanity’s future that depend on the validity of the metaphor.
One prediction – made by the futurist Kurzweil, the physicist Stephen Hawking and the neuroscientist Randal Koene, among others – is that, because human consciousness is supposedly like computer software, it will soon be possible to download human minds to a computer, in the circuits of which we will become immensely powerful intellectually and, quite possibly, immortal. This concept drove the plot of the dystopian movie Transcendence (2014) starring Johnny Depp as the Kurzweil-like scientist whose mind was downloaded to the internet – with disastrous results for humanity.
Fortunately, because the IP metaphor is not even slightly valid, we will never have to worry about a human mind going amok in cyberspace; alas, we will also never achieve immortality through downloading. This is not only because of the absence of consciousness software in the brain; there is a deeper problem here – let’s call it the uniqueness problem – which is both inspirational and depressing.
Because neither ‘memory banks’ nor ‘representations’ of stimuli exist in the brain, and because all that is required for us to function in the world is for the brain to change in an orderly way as a result of our experiences, there is no reason to believe that any two of us are changed the same way by the same experience. If you and I attend the same concert, the changes that occur in my brain when I listen to Beethoven’s 5th will almost certainly be completely different from the changes that occur in your brain. Those changes, whatever they are, are built on the unique neural structure that already exists, each structure having developed over a lifetime of unique experiences.
This is why, as Sir Frederic Bartlett demonstrated in his book Remembering (1932), no two people will repeat a story they have heard the same way and why, over time, their recitations of the story will diverge more and more. No ‘copy’ of the story is ever made; rather, each individual, upon hearing the story, changes to some extent – enough so that when asked about the story later (in some cases, days, months or even years after Bartlett first read them the story) – they can re-experience hearing the story to some extent, although not very well (see the first drawing of the dollar bill, above).
This is inspirational, I suppose, because it means that each of us is truly unique, not just in our genetic makeup, but even in the way our brains change over time. It is also depressing, because it makes the task of the neuroscientist daunting almost beyond imagination. For any given experience, orderly change could involve a thousand neurons, a million neurons or even the entire brain, with the pattern of change different in every brain.
Worse still, even if we had the ability to take a snapshot of all of the brain’s 86 billion neurons and then to simulate the state of those neurons in a computer, that vast pattern would mean nothing outside the body of the brain that produced it. This is perhaps the most egregious way in which the IP metaphor has distorted our thinking about human functioning. Whereas computers do store exact copies of data – copies that can persist unchanged for long periods of time, even if the power has been turned off – the brain maintains our intellect only as long as it remains alive. There is no on-off switch. Either the brain keeps functioning, or we disappear. What’s more, as the neurobiologist Steven Rose pointed out in The Future of the Brain (2005), a snapshot of the brain’s current state might also be meaningless unless we knew the entire life history of that brain’s owner – perhaps even about the social context in which he or she was raised.
Think how difficult this problem is. To understand even the basics of how the brain maintains the human intellect, we might need to know not just the current state of all 86 billion neurons and their 100 trillion interconnections, not just the varying strengths with which they are connected, and not just the states of more than 1,000 proteins that exist at each connection point, but how the moment-to-moment activity of the brain contributes to the integrity of the system. Add to this the uniqueness of each brain, brought about in part because of the uniqueness of each person’s life history, and Kandel’s prediction starts to sound overly optimistic. (In a recent op-ed in TheNew York Times, the neuroscientist Kenneth Miller suggested it will take ‘centuries’ just to figure out basic neuronal connectivity.)
Meanwhile, vast sums of money are being raised for brain research, based in some cases on faulty ideas and promises that cannot be kept. The most blatant instance of neuroscience gone awry, documented recently in a report in Scientific American, concerns the $1.3 billion Human Brain Project launched by the European Union in 2013. Convinced by the charismatic Henry Markram that he could create a simulation of the entire human brain on a supercomputer by the year 2023, and that such a model would revolutionise the treatment of Alzheimer’s disease and other disorders, EU officials funded his project with virtually no restrictions. Less than two years into it, the project turned into a ‘brain wreck’, and Markram was asked to step down.
We are organisms, not computers. Get over it. Let’s get on with the business of trying to understand ourselves, but without being encumbered by unnecessary intellectual baggage. The IP metaphor has had a half-century run, producing few, if any, insights along the way. The time has come to hit the DELETE key.
It took Richard Ridel six months of tinkering in his workshop to create this contraption--a mechanical Turing machine made out of wood. The silent video above shows how the machine works. But if you're left hanging, wanting to know more, I'd recommend reading Ridel's fifteen page paper where he carefully documents why he built the wooden Turing machine, and what pieces and steps went into the construction.
If this video prompts you to ask, what exactly is a Turing Machine?, also consider adding this short primer by philosopher Mark Jago to your media diet.
New MoMA show plots the impact of computers on architecture and design. Pictured here: “Menu 23" layout of Cedric Price's Generator Project. (Courtesy California College of the Arts archive)
The beginnings of digital drafting and computational design will be on display at the Museum of Modern Art (MoMA) starting November 13th, as the museum presents Thinking Machines: Art and Design in the Computer Age, 1959–1989. Spanning 30 years of works by artists, photographers, and architects, Thinking Machines captures the postwar period of reconciliation between traditional techniques and the advent of the computer age.
Organized by Sean Anderson, associate curator in the museum’s Department of Architecture and Design, and Giampaolo Bianconi, a curatorial assistant in the Department of Media and Performance Art, the exhibition examines how computer-aided design became permanently entangled with art, industrial design, and space planning.
Drawings, sketches, and models from Cedric Price’s 1978-80 Generator Project, the never-built “first intelligent building project” will also be shown. The response to a prompt put out by the Gilman Paper Corporation for its White Oak, Florida, site to house theater and dance performances alongside travelling artists, Price’s Generator proposal sought to stimulate innovation by constantly shifting arrangements.
Ceding control of the floor plan to a master computer program and crane system, a series of 13-by-13-foot rooms would have been continuously rearranged according to the users’ needs. Only constrained by a general set of Price’s design guidelines, Generator’s program would even have been capable of rearranging rooms on its own if it felt the layout hadn’t been changed frequently enough. Raising important questions about the interaction between a space and its occupants, Generator House laid the groundwork for computational architecture and smart building systems.
R. Buckminster Fuller’s 1970 work for Radical Hardware magazine will also appear. (Courtesy PBS)
Thinking Machines: Art and Design in the Computer Age, 1959–1989 will be running from November 13th to April 8th, 2018. MoMA members can preview the show from November 10th through the 12th.
Note: following the two previous posts about algorythms and bots ("how do they ... ?), here comes a third one.
Slighty different and not really dedicated to bots per se, but which could be considered as related to "machinic intelligence" nonetheless. This time it concerns techniques and algoritms developed to understand the brain (BRAIN initiative, or in Europe the competing Blue Brain Project).
In a funny reversal, scientists applied techniques and algorythms developed to track human intelligence patterns based on data sets to the computer itself. How do a simple chip "compute information"? And the results are surprising: the computer doesn't understand how the computer "thinks" (or rather works in this case)!
This to confirm that the brain is certainly not a computer (made out of flesh)...
When you apply tools used to analyze the human brain to a computer chip that plays Donkey Kong, can they reveal how the hardware works?
Many research schemes, such as the U.S. government’s BRAIN initiative, are seeking to build huge and detailed data sets that describe how cells and neural circuits are assembled. The hope is that using algorithms to analyze the data will help scientists understand how the brain works.
But those kind of data sets don’t yet exist. So Eric Jonas of the University of California, Berkeley, and Konrad Kording from the Rehabilitation Institute of Chicago and Northwestern University wondered if they could use their analytical software to work out how a simpler system worked.
They settled on the iconic MOS 6502 microchip, which was found inside the Apple I, the Commodore 64, and the Atari Video Game System. Unlike the brain, this slab of silicon is built by humans and fully understood, down to the last transistor.
The researchers wanted to see how accurately their software could describe its activity. Their idea: have the chip run different games—including Donkey Kong, Space Invaders, and Pitfall, which have already been mastered by some AIs—and capture the behavior of every single transistor as it did so (creating about 1.5 GB per second of data in the process). Then they would turn their analytical tools loose on the data to see if they could explain how the microchip actually works.
For instance, they used algorithms that could probe the structure of the chip—essentially the electronic equivalent of a connectome of the brain—to establish the function of each area. While the analysis could determine that different transistors played different roles, the researchers write in PLOS Computational Biology, the results “still cannot get anywhere near an understanding of the way the processor really works.”
Elsewhere, Jonas and Kording removed a transistor from the microchip to find out what happened to the game it was running—analogous to so-called lesion studies where behavior is compared before and after the removal of part of the brain. While the removal of some transistors stopped the game from running, the analysis was unable to explain why that was the case.
In these and other analyses, the approaches provided interesting results—but not enough detail to confidently describe how the microchip worked. “While some of the results give interesting hints as to what might be going on,” explains Jonas, “the gulf between what constitutes ‘real understanding’ of the processor and what we can discover with these techniques was surprising.”
It’s worth noting that chips and brains are rather different: synapses work differently from logic gates, for instance, and the brain doesn’t distinguish between software and hardware like a computer. Still, the results do, according to the researchers, highlight some considerations for establishing brain understanding from huge, detailed data sets.
First, simply amassing a handful of high-quality data sets of the brains may not be enough for us to make sense of neural processes. Second, without many detailed data sets to analyze just yet, neuroscientists ought to remain aware that their tools may provide results that don’t fully describe the brain’s function.
As for the question of whether neuroscience can explain how an Atari works? At the moment, not really.
Note: we've been working recently at fabric | ch on a project that we couldn't publish or talk about for contractual reasons... It concerned a relatively large information pavilion we had to create for three new museums in Switzerland (in Lausanne) and a renewed public space (railway station square). This pavilion was supposed to last for a decade, or a bit longer. The process was challenging, the work was good (we believed), but it finally didn't get build...
Sounds sad but common isn't it?
...
We'll see where these many "..." will lead us, but in the meantime and as a matter of documentation, let's stick to the interesting part and publish a first report about this project.
It consisted in an evolution of a prior spatial installation entitled Heterochrony (pdf). A second post will follow soon with the developments of this competition proposal. Both posts will show how we try to combine small size experiments (exhibitions) with more permanent ones (architecture) in our work. It also marks as well our desire at fabric | ch to confront more regularly our ideas and researches with architectural programs.
On the jury paper was written, under "price" -- as we didn't get paid for the 1st price itself -- : "Réalisation" (realization).
Just below in the same letter, "according to point 1.5 of the competition", no realization will be attributed... How ironic! We did work further on an extended study though.
A few words about the project taken from its presentation:
" (...) This platform with physically moving parts could almost be considered an archaeological footbridge or an unknown scientific device, reconfigurable and shiftable, overlooking and giving to see some past industrial remains, allowing to document the present, making foresee the future.
The pavilion, or rather pavilions, equipped with numerous sensor systems, could equally be considered an "architecture of documentation" and interaction, in the sense that there will be extensive data collected to inform in an open and fluid manner over the continuous changes on the sites of construction and tranformations. Taken from the various "points of interets' on the platform, these data will feed back applications ("architectural intelligence"?), media objects, spatial and lighting behaviors. The ensemble will play with the idea of a combination of various time frames and will combine the existing, the imagined and the evanescent. (...) "
Note: after a few weeks posting about the Universal Income, here comes the "Universal data accumulator for devices, sensors, programs, humans & more" by Wolfram (best known for Wolfram Alpha computational engine and the former Mathematica libraries, on which most of their other services seem to be built).
Funilly, we've picked a very similar name for a very similar data service we've set up for ourselves and our friends last year, during an exhibition at H3K: Datadroppers (!), with a different set of references in our mind (Drop City? --from which we borrowed the colors-- "Turn on, tune in, drop out"?) Even if our service is logically much more grassroots, less developed but therfore quite light to use as well.
We developed this project around data dropping/picking with another architectural project in mind that I'll speak about in the coming days: Public Platform of Future-Past. It was clearly and closely linked.
"Universal" is back in the loop as a keyword therefore... (I would rather adopt a different word for myself and the work we are doing though: "Diversal" --which is a word I'm using for 2 yearnow and naively thought I "invented", but not...)
"The Wolfram Data Drop is an open service that makes it easy to accumulate data of any kind, from anywhere—setting it up for immediate computation, visualization, analysis, querying, or other operations." - which looks more oriented towards data analysis than use in third party designs and projects.
"Datadroppers is a public and open data commune, it is a tool dedicated to data collection and sharing that tries to remain as simple, minimal and easy to use as possible." Direct and light data tool for designers, belonging to designers (fabric | ch) that use it for their own projects...
Note: I've posted several articles about automation recently. This was the occasion to continue collect some thoughts about the topic (automation then) so as the larger social implications that this might trigger.
But it was also a "collection" that took place at a special moment in Switzerland when we had to vote about the "Revenu the Base Inconditionnel" (Unconditional Basic Income). I mentioned it in a previous post ("On Algorithmic Communism"), in particular the relation that is often made between this idea (Basic Income / Universal Income) and the probable evolution of work in the decades to come (less work for "humans" vs. more for "robots").
Well, the campain and votation triggered very interesting debates among the civil population, but in the end and predictably, the idea was largely rejected (~25% of the voters accepted it, with some small geographical areas that indeed acceted it at more than 50% --urban areas mainly--. Some where not so far, for exemple the city capital, Bern, voted at 40% for the RBI).
This was very new and a probably too (?) early question for the Swiss population, but it will undoubtedlybecome a growing debate in the decades to come. A question that has many important associated stakes.
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Press talking about the RBI, image from RTS website.
Note: in a time when we'll soon have for the first timea 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".
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.
Demands
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.
Criticisms
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.
Note: j'aurai le plaisir d'être en entretien --en français-- ce vendredi 26.02 à 20h avec le journaliste Frédéric Pfyffer, de la Radio Télévision Suisse Romande, dans le cadre du programme Histoire Vivante qui traite cette semaine du sujet des "Big Data".
Cet entretien, qui a été enregistré en fin de semaine passée, nous verra évoquer la façon dont les artistes ou designers abordent aujourd'hui --mais aussi un peu hier-- cette question des données. En contrepoint ou complément peut-être des approches scientifiques. Pour ma part, aussi bien dans le contexte de ma pratique indépendante (fabric | ch où de nombreux projets réalisés ou en développement s'appuient sur des données) qu'académique (projet de recherche interdisciplinaire en cours autour des "nuages"... entre autres).
À noter encore qu'au terme de la semaine d'émissions thématiques sera diffusé sur la TSR (dimanche 28.02) le documentaire Citizenfour, qui relate toute l'aventure d'Edward Snowden et du journaliste Glenn Greenwald.
Une semaine d’Histoire Vivante consacrée à l’histoire de la recherche scientifique à la lumière de l’émergence de l’internet et des big data.
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Dimanche 28 février 2016, vous pouvez découvrir sur RTS Deux: CitizenFour, un documentaire de Laura Poitras (Allemagne-USA/2014):
"Citizenfour est le pseudonyme utilisé par Edward Snowden pour contacter la réalisatrice de ce documentaire lorsqu'il décide de révéler les méthodes de surveillance de la NSA. Accompagnée d'un journaliste d'investigation, elle le rejoint dans une chambre d'hôtel à Hong Kong. La suite est un huis-clos digne des meilleurs thrillers."
Note: can a computer "fake" a human? (hmmm, sounds a bit like Mr. Turing test isn't it?) Or at least be credible enough --because it sounds pretty clear in this video, at that time, that it cannot fake a human and that it is m ore about voice than "intelligence"-- so that the person on the other side of the phone doesn't hang up? This is a funny/uncanny experiment involving D. Sherman at Michigan State University, dating back 1974 and certainly one of the first public trial (or rather social experiment) of a text to speech/voice synthesizer.
Beyond the technical performance, it is the social experiment that is probably even more interesting. It's intertwined and odd nature. You can feel in the voice of the person on the other side of the phone (at the pizza factory --Domino's pizza--) that he really doesn't know how to take it and that the voice sounds like something not heard before. A few trials were necessary before somebody took it "seriously".
Every year, the researchers, students, and technology users who make up the community of the Michigan State University Artificial Language Laboratory celebrate the anniversary of the first use of a speech prosthesis in history: the use by a man with a communication disorder to order a pizza over the telephone using a voice synthesizer. This high-tech sociolinguistic experiment was conducted at the Lab on the evening of December 4, 1974. Donald Sherman, who has Moebius Syndrome and had never ordered a pizza over the phone before, used a system designed by John Eulenberg and J. J. Jackson incorporating a Votrax voice synthesizer, a product of the Federal Screw Works Co. of Troy, Michigan. The inventor of the Votrax voice synthesizer was Richard Gagnon from Birmingham, MI.
The event was covered at the time by the local East Lansing cable news reporter and by a reporter from the State News. About seven years later, in 1981, a BBC production team produced a documentary about the work of the Artificial Language Laboratory and included a scene of a man with cerebral palsy, Michael Williams, ordering a pizza with a newer version of the Lab's speech system. This second pizza order became a part of the documentary, which was broadcast throughout the U.S. as part of the "Nova" science series and internationally as part of the BBC's "Horizon" series.
In January, 1982, the Nova show on the Artificial Language Lab was shown for the first time. The Artificial Language Lab held a premiere party in the Communication Arts and Sciences Building for all the persons who appeared in the program plus all faculty members of the College of Communication Arts and Sciences and their families. The Domino's company generously provided free pizzas for all the guests.
The following December, Domino's again provided pizzas for a party, again held at the Communication Arts building, to commemorate the first ordering of a pizza eight years earlier. The Convocation was held thereafter every year through 1988, each year receiving pizzas through the generous gift of Domino's.
A Communication Enhancement Convocation was held in 1999, celebrating the 25th anniversary of the first pizza order.In addition to Dominos's contribution of pizzas, the Canada Dry Bottling Co. of Lansing provided drinks.The Convocations resumed in 2010 through 2012, when Dr. John Eulenberg advanced to Professor Emeritus status.
At each event, in addition to faculty and students, the convocation guests included local dignitaries from the MSU board of trustees and from the Michigan state legislature. Stevie Wonder, whose first talking computer and first singing computer were designed at the Artificial Language Lab, made telephone appearances and spoke with the youngsters using Artificial Language Lab technology through their
school district special education programs. MSU icons such as the football team, Sparty, and cheer leaders made appearances as well.
Now, through YouTube, we can relive this historical moment and take a thoughtful look back at 40 years of progress in the delivery of augmentative communication technology to persons with disabilities.
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