A fanciful belonging

Reading The Official History of Britain: Our story in numbers as told by the Office for National Statistics by Boris Starling with David Bradbury for The Telegraph, 18 October 2020

Next year’s national census may be our last. Opinions are being sought as to whether it makes sense, any longer, for the nation to keep taking its own temperature every ten years. Discussions will begin in 2023. Our betters may conclude that the whole rigmarole is outdated, and that its findings can be gleaned more cheaply and efficiently by other methods.

How the UK’s national census was established, what it achieved, and what it will mean if it’s abandoned, is the subject of The Official History of Britain — a grand title for what is, to be honest, a rather messy book, its facts and figures slathered in weak and irrelevant humour, most of it to do with football, I suppose as an intellectual sugar lump for the proles.

Such condescension is archetypally British; and so too is the gimcrack team assembled to write this book. There is something irresistibly Dad’s Army about the image of David Bradbury, an old hand at the Office of National Statistics, comparing dad jokes with novelist Boris Starling, creator of Messiah’s DCI Red Metcalfe, who was played on the telly by Ken Stott.

The charm of the whole enterprise is undeniable. Within these pages you will discover, among other tidbits, the difference between critters and spraggers, whitsters and oliver men. Such were the occupations introduced into the Standard Classification of 1881. (Recent additions include YouTuber and dog sitter.) Nostalgia and melancholy come to the fore when the authors say a fond farewell to John and Margaret — names, deeply unfashionable now, that were pretty much compulsory for babies born between 1914 and 1964. But there’s rigour, too; I recommend the author’s highly illuminating analysis of today’s gender pay gap.

Sometimes the authors show us up for the grumpy tabloid zombies we really are. Apparently a sizeable sample of us, quizzed in 2014, opined that 15 per cent of all our girls under sixteen were pregnant. The lack of mathematical nous here is as disheartening as the misanthropy. The actual figure was a still worryingly high 0.5 per cent, or one in 200 girls. A 10-year Teenage Pregnancy Strategy was created to tackle the problem, and the figure for 2018 — 16.8 conceptions per 1000 women aged between 15 and 17 — is the lowest since records began.

This is why census records are important: they inform enlightened and effective government action. The statistician John Rickman said as much in a paper written in 1796, but his campaign for a national census only really caught on two years later, when the clergyman Thomas Malthus scared the living daylights out of everyone with his “Essay on the Principle of Population”. Three years later, ministers rattled by Malthus’s catalogue of checks on the population of primitive societies — war, pestilence, famine, and the rest — peeked through their fingers at the runaway population numbers for 1801.

The population of England then was the same as the population of Greater London now. The population of Scotland was almost exactly the current population of metropolitan Glasgow.

Better to have called it “The Official History of Britons”. Chapter by chapter, the authors lead us (wisely, if not too well) from Birth, through School, into Work and thence down the maw of its near neighbour, Death, reflecting all the while on what a difference two hundred years have made to the character of each life stage.

The character of government has changed, too. Rickman wanted a census because he and his parliamentary colleagues had almost no useful data on the population they were supposed to serve. The job of the ONS now, the writers point out, “is to try to make sure that policymakers and citizens can know at least as much about their populations and economies as the internet behemoths.”

It’s true: a picture of the state of the nation taken every ten years just doesn’t provide the granularity that could be fetched, more cheaply and more efficiently, from other sources: “smaller surveys, Ordnance Survey data, GP registrations, driving licence details…”

But this too is true: near where I live there is a pedestrian crossing. There is a button I can push, to change the lights, to let me cross the road. I know that in daylight hours, the button is a dummy, that the lights are on a timer, set in some central office, to smooth the traffic flow. Still, I press that button. I like that button. I appreciate having my agency acknowledged, even in a notional, fanciful way.

Next year, 2021, I will tell the census who and what I am. It’s my duty as a citizen, and also my right, to answer how I will. If, in 2031, the state decides it does not need to ask me who I am, then my idea of myself as a citizen, notional as it is, fanciful as it is, will be impoverished.

Cutting up the sky

Reading A Scheme of Heaven: Astrology and the Birth of Science by Alexander Boxer
for the Spectator, 18 January 2020

Look up at sky on a clear night. This is not an astrological game. (Indeed, the experiment’s more impressive if you don’t know one zodiacal pattern from another, and rely solely on your wits.) In a matter of seconds, you will find patterns among the stars.

We can pretty much apprehend up to five objects (pennies, points of light, what-have-you) at a single glance. Totting up more than five objects, however, takes work. It means looking for groups, lines, patterns, symmetries, boundaries.

The ancients cut up the sky into figures, all those aeons ago, for the same reason we each cut up the sky within moments of gazing at it: because if we didn’t, we wouldn’t be able to comprehend the sky at all.

Our pattern-finding ability can get out of hand. During his Nobel lecture in 1973 the zoologist Konrad Lorenz recalled how he once :”… mistook a mill for a sternwheel steamer. A vessel was anchored on the banks of the Danube near Budapest. It had a little smoking funnel and at its stern an enormous slowly-turning paddle-wheel.”

Some false patterns persist. Some even flourish. And the brighter and more intellectually ambitious you are, the likelier you are to be suckered. John Dee, Queen Elizabeth’s court philosopher, owned the country’s largest library (it dwarfed any you would find at Oxford or Cambridge). His attempt to tie up all that knowledge in a single divine system drove him into the arms of angels — or at any rate, into the arms of the “scrier” Edward Kelley, whose prodigious output of symbolic tables of course could be read in such a way as to reveal fragments of esoteric wisdom.

This, I suspect, is what most of us think about astrology: that it was a fanciful misconception about the world that flourished in times of widespread superstition and ignorance, and did not, could not, survive advances in mathematics and science.

Alexander Boxer is out to show how wrong that picture is, and A Scheme of Heaven will make you fall in love with astrology, even as it extinguishes any niggling suspicion that it might actually work.

Boxer, a physicist and historian, kindles our admiration for the earliest astronomers. My favourite among his many jaw-dropping stories is the discovery of the precession of the equinoxes. This is the process by which the sun, each mid-spring and mid-autumn, rises at a fractionally different spot in the sky each year. It takes 26,000 years to make a full revolution of the zodiac — a tiny motion first detected by Hipparchus around 130 BC. And of course Hipparchus, to make this observation at all, “had to rely on the accuracy of stargazers who would have seemed ancient even to him.”

In short, a had a library card. And we know that such libraries existed because the “astronomical diaries” from the Assyrian library at Nineveh stretch from 652BC to 61BC, representing possibly the longest continuous research program ever undertaken in human history.

Which makes astrology not too shoddy, in my humble estimation. Boxer goes much further, dubbing it “the ancient world’s most ambitious applied mathematics problem.”

For as long as lives depend on the growth cycles of plants, the stars will, in a very general sense, dictate the destiny of our species. How far can we push this idea before it tips into absurdity? The answer is not immediately obvious, since pretty much any scheme we dream up will fit some conjunction or arrangement of the skies.

As civilisations become richer and more various, the number and variety of historical events increases, as does the chance that some event will coincide with some planetary conjunction. Around the year 1400, the French Catholic cardinal Pierre D’Ailly concluded his astrological history of the world with a warning that the Antichrist could be expected to arrive in the year 1789, which of course turned out to be the year of the French revolution.

But with every spooky correlation comes an even larger horde of absurdities and fatuities. Today, using a machine-learning algorithm, Boxer shows that “it’s possible to devise a model that perfectlly mimics Bitcoin’s price history and that takes, as its input data, nothing more than the zodiac signs of the planets on any given day.”

The Polish science fiction writer Stanislaw Lem explored this territory in his novel The Chain of Chance: “We now live in such a dense world of random chance,” he wrote in 1975, “in a molecular and chaotic gas whose ‘improbabilities’ are amazing only to the individual human atoms.” And this, I suppose, is why astrology eventually abandoned the business of describing whole cultures and nations (a task now handed over to economics, another largely ineffectual big-number narrative) and now, in its twilight, serves merely to gull individuals.

Astrology, to work at all, must assume that human affairs are predestined. It cannot, in the long run, survive the notion of free will. Christianity did for astrology, not because it defeated a superstition, but because it rendered moot astrology’s iron bonds of logic.

“Today,” writes Boxer, “there’s no need to root and rummage for incidental correlations. Modern machine-learning algorithms are correlation monsters. They can make pretty much any signal correlate with any other.”

We are bewitched by big data, and imagine it is something new. We are ever-indulgent towards economists who cannot even spot a global crash. We credulously conform to every algorithmically justified norm. Are we as credulous, then, as those who once took astrological advice as seriously as a medical diagnosis? Oh, for sure.

At least our forebears could say they were having to feel their way in the dark. The statistical tools you need to sort real correlations from pretty patterns weren’t developed until the late nineteenth century. What’s our excuse?

“Those of us who are enthusiastic about the promise of numerical data to unlock the secrets of ourselves and our world,” Boxer writes, “would do well simply to acknowledge that others have come this way before.”

“Intelligence is the wrong metaphor for what we’ve built”

Travelling From Apple to Anomaly, Trevor Paglen’s installation at the Barbican’s Curve gallery in London, for New Scientist, 9 October 2019

A COUPLE of days before the opening of Trevor Paglen’s latest photographic installation, From “Apple” to “Anomaly”, a related project by the artist found itself splashed all over the papers.

ImageNet Roulette is an online collaboration with artificial intelligence researcher Kate Crawford at New York University. The website invites you to provide an image of your face. An algorithm will then compare your face against a database called ImageNet and assign you to one or two of its 21,000 categories.

ImageNet has become one of the most influential visual data sets in the fields of deep learning and AI. Its creators at Stanford, Princeton and other US universities harvested more than 14 million photographs from photo upload sites and other internet sources, then had them manually categorised by some 25,000 workers on Amazon’s crowdsourcing labour site Mechanical Turk. ImageNet is widely used as a training data set for image-based AI systems and is the secret sauce within many key applications, from phone filters to medical imaging, biometrics and autonomous cars.

According to ImageNet Roulette, I look like a “political scientist” and a “historian”. Both descriptions are sort-of-accurate and highly flattering. I was impressed. Mind you, I’m a white man. We are all over the internet, and the neural net had plenty of “my sort” to go on.

Spare a thought for Guardian journalist Julia Carrie Wong, however. According to ImageNet Roulette she was a “gook” and a “slant-eye”. In its attempt to identify Wong’s “sort”, ImageNet Roulette had innocently turned up some racist labels.

From “Apple” to “Anomaly” also takes ImageNet to task. Paglen took a selection of 35,000 photos from ImageNet’s archive, printed them out and stuck them to the wall of the Curve gallery at the Barbican in London in a 50-metre-long collage.

The entry point is images labelled “apple” – a category that, unsurprisingly, yields mostly pictures of apples – but the piece then works through increasingly abstract and controversial categories such as “sister” and “racist”. (Among the “racists” are Roger Moore and Barack Obama; my guess is that being over-represented in a data set carries its own set of risks.) Paglen explains: “We can all look at an apple and call it by its name. An apple is an apple. But what about a noun like ‘sister’, which is a relational concept? What might seem like a simple idea – categorising objects or naming pictures – quickly becomes a process of judgement.”

The final category in the show is “anomaly”. There is, of course, no such thing as an anomaly in nature. Anomalies are simply things that don’t conform to the classification systems we set up.

Halfway along the vast, gallery-spanning collage of photographs, the slew of predominantly natural and environmental images peters out, replaced by human faces. Discrete labels here and there indicate which of ImageNet’s categories are being illustrated. At one point of transition, the group labelled “bottom feeder” consists entirely of headshots of media figures – there isn’t one aquatic creature in evidence.

Scanning From “Apple” to “Anomaly” gives gallery-goers many such unexpected, disconcerting insights into the way language parcels up the world. Sometimes, these threaten to undermine the piece itself. Passing seamlessly from “android” to “minibar”, one might suppose that we are passing from category to category according to the logic of a visual algorithm. After all, a metal man and a minibar are not so dissimilar. At other times – crossing from “coffee” to “poultry”, for example – the division between categories is sharp, leaving me unsure how we moved from one to another, and whose decision it was. Was some algorithm making an obscure connection between hens and beans?

Well, no: the categories were chosen and arranged by Paglen. Only the choice of images within each category was made by a trained neural network.

This set me wondering whether the ImageNet data set wasn’t simply being used as a foil for Paglen’s sense of mischief. Why else would a cheerleader dominate the “saboteur” category? And do all “divorce lawyers” really wear red ties?

This is a problem for art built around artificial intelligence: it can be hard to tell where the algorithm ends and the artist begins. Mind you, you could say the same about the entire AI field. “A lot of the ideology around AI, and what people imagine it can do, has to do with that simple word ‘intelligence’,” says Paglen, a US artist now based in Berlin, whose interest in computer vision and surveillance culture sprung from his academic career as a geographer. “Intelligence is the wrong metaphor for what we’ve built, but it’s one we’ve inherited from the 1960s.”

Paglen fears the way the word intelligence implies some kind of superhuman agency and infallibility to what are in essence giant statistical engines. “This is terribly dangerous,” he says, “and also very convenient for people trying to raise money to build all sorts of shoddy, ill-advised applications with it.”

Asked what concerns him more, intelligent machines or the people who use them, Paglen answers: “I worry about the people who make money from them. Artificial intelligence is not about making computers smart. It’s about extracting value from data, from images, from patterns of life. The point is not seeing. The point is to make money or to amplify power.”

It is a point by no means lost on a creator of ImageNet itself, Fei-Fei Li at Stanford University in California, who, when I spoke to Paglen, was in London to celebrate ImageNet’s 10th birthday at the Photographers’ Gallery. Far from being the face of predatory surveillance capitalism, Li leads efforts to correct the malevolent biases lurking in her creation. Wong, incidentally, won’t get that racist slur again, following ImageNet’s announcement that it was removing more than half of the 1.2 million pictures of people in its collection.

Paglen is sympathetic to the challenge Li faces. “We’re not normally aware of the very narrow parameters that are built into computer vision and artificial intelligence systems,” he says. His job as artist-cum-investigative reporter is, he says, to help reveal the failures and biases and forms of politics built into such systems.

Some might feel that such work feeds an easy and unexamined public paranoia. Peter Skomoroch, former principal data scientist at LinkedIn, thinks so. He calls ImageNet Roulette junk science, and wrote on Twitter: “Intentionally building a broken demo that gives bad results for shock value reminds me of Edison’s war of the currents.”

Paglen believes, on the contrary, that we have a long way to go before we are paranoid enough about the world we are creating.

Fifty years ago it was very difficult for marketing companies to get information about what kind of television shows you watched, what kinds of drinking habits you might have or how you drove your car. Now giant companies are trying to extract value from that information. “I think,” says Paglen, “that we’re going through something akin to England and Wales’s Inclosure Acts, when what had been de facto public spaces were fenced off by the state and by capital.”

Ceiling Cat is watching you make art

Visiting 😹 LMAO at London’s Open Data Institute for New Scientist, 2 February 2018

On Friday 12 January 2018, curators Julie Freeman and Hannah Redler Hawes left work at London’s Open Data Institute confident that, come Monday morning, there would be at least a few packets of crisps in the office.

Artist Ellie Harrison‘s Vending Machine (2009; pictured below) sits in the ODI’s kitchen, one of the more venerable exhibits to have been acquired over the institute’s five-year programme celebrating data as culture. It has been hacked to dispense a packet of salty snacks whenever the BBC’s RSS feed carries a news item containing financial misfortune.

No one could have guessed that, come 7 am on Monday morning, Carillion, the UK government’s giant services contractor, would have gone into liquidation. There were so many packets in the hopper, no one could open the door, say staff.

Such apparently silly anecdotes are the stuff of this year’s show, the fifth in the ODI’s annual exhibition series “Data as Culture”. This year, humour and absurdity are being harnessed to ask big questions about internet culture, privacy and artificial intelligence.

Looking at the world through algorithmic lenses may bring occasional insight, but what really matters here are the pratfalls as, time and again, our machines misconstrue a world they cannot possibly comprehend.

In 2017, artist Pip Thornton fed famous poems to Google’s online advertising service, Google AdWords, and printed the monetised results on till receipts. The framed results value the word “cloud” (as in I Wandered Lonely as a Cloud by William Wordsworth) highly, at £4.73, presumably because Google’s algorithm was dreaming of internet servers. It had no time at all for Wilfred Owen: “Froth-corrupted” (Dulce et Decorum Est) earned exactly £0.00.

You can, of course, reverse this game and ask what happens to people when they over-interpret machine-generated data, seeing patterns that aren’t there.

This is what Lee Montgomery has done with Stupidity Tax (2017). In an effort to understand his father’s mild but unaccountably secretive gambling habit, Montgomery has used a variety of data analysis techniques to attempt to predict the UK National Lottery. The sting in this particular tale is the installation’s associated website, which implies (mischievously, I hope) that the whole tongue-in-cheek effort has driven the artist ever so slightly mad.

Watching over the whole exhibition – literally because it’s peeking through a hole in a ceiling tile – is Franco and Eva Mattes’s Ceiling Cat, a taxidermied realisation of the internet meme, and a comment on the nature of surveillance beliefs (pictured top). “It’s cute and scary at the same time,” the artists say, “like the internet.”

Co-curator Freeman is a data artist herself. If you visited last year’s New Scientist Live you may well have seen her naked mole-rat surveillance project. The 7.5 million data points acquired by the project are now keeping network analysts busy at Queen Mary University of London. “We want to know if mole-rats make good encryption objects,” says Freeman. Their nest behaviours might generate true random numbers, handy for data security. “But the mole-rat queens are far too predictable… Crisp?”

Through a mouthful of salt and vinegar, I ask Freeman where her playfulness comes from. And as I suspected, there’s intellectual steel beneath: “Data is being constantly visualised so we can comprehend it,” she says, “and those visualisations are often done in a very short space of time, for a particular purpose, in a particular context, for a particular audience. Then they acquire this afterlife. All of a sudden, they’re the lenses we’re looking through. If you start thinking about data as something rigid and objective and bearing the weight of truth, then you’ve stopped discerning what is right and what is wrong.”

Freeman wants us to analyse data, not abandon it, and her exhibition is an act of tough love. “When we fetishise data, we end up with what’s happening in social media,” she says. “So many people drowning in metadata, pointing to pointers, and never acquiring any knowledge that’s deep and valuable. There should be some words to express that glut, that need to roll back a little bit. Here, have another crisp.”