{"id":2846,"date":"2019-10-22T18:49:19","date_gmt":"2019-10-22T18:49:19","guid":{"rendered":"http:\/\/www.simonings.net\/?p=2846"},"modified":"2019-10-22T18:51:11","modified_gmt":"2019-10-22T18:51:11","slug":"intelligence-is-the-wrong-metaphor-for-what-weve-built","status":"publish","type":"post","link":"http:\/\/www.simonings.net\/?p=2846","title":{"rendered":"“Intelligence is the wrong metaphor for what we\u2019ve built”"},"content":{"rendered":"\n
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Travelling From Apple to Anomaly, Trevor Paglen’s installation at the Barbican’s Curve gallery in London, for New Scientist, 9 October 2019<\/a><\/p>\n\n\n\n

A COUPLE of days before the opening of Trevor Paglen\u2019s latest photographic installation, From \u201cApple\u201d to \u201cAnomaly\u201d, a related project by the artist found itself splashed<\/a> all over the papers.<\/p>\n\n\n\n

ImageNet Roulette is an online collaboration with artificial intelligence researcher Kate Crawford<\/a> at New York University<\/a>. 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.<\/p>\n\n\n\n

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\u2019s 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.<\/p>\n\n\n\n

According to ImageNet Roulette, I look like a \u201cpolitical scientist\u201d and a \u201chistorian\u201d. Both descriptions are sort-of-accurate and highly flattering. I was impressed. Mind you, I\u2019m a white man. We are all over the internet, and the neural net had plenty of \u201cmy sort\u201d to go on.<\/p>\n\n\n\n

Spare a thought for Guardian<\/em> journalist Julia Carrie Wong, however. According to ImageNet Roulette<\/a> she was a \u201cgook\u201d and a \u201cslant-eye\u201d. In its attempt to identify Wong\u2019s \u201csort\u201d, ImageNet Roulette had innocently turned up some racist labels.<\/p>\n\n\n\n

From \u201cApple\u201d to \u201cAnomaly\u201d also takes ImageNet to task. Paglen took a selection of 35,000 photos from ImageNet\u2019s 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.<\/p>\n\n\n\n

The entry point is images labelled \u201capple\u201d \u2013 a category that, unsurprisingly, yields mostly pictures of apples \u2013 but the piece then works through increasingly abstract and controversial categories such as \u201csister\u201d and \u201cracist\u201d. (Among the \u201cracists\u201d 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: \u201cWe can all look at an apple and call it by its name. An apple is an apple. But what about a noun like \u2018sister\u2019, which is a relational concept? What might seem like a simple idea \u2013 categorising objects or naming pictures \u2013 quickly becomes a process of judgement.\u201d<\/p>\n\n\n\n

The final category in the show is \u201canomaly\u201d. There is, of course, no such thing as an anomaly in nature. Anomalies are simply things that don\u2019t conform to the classification systems we set up.<\/p>\n\n\n\n

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\u2019s categories are being illustrated. At one point of transition, the group labelled \u201cbottom feeder\u201d consists entirely of headshots of media figures \u2013 there isn\u2019t one aquatic creature in evidence.<\/p>\n\n\n\n

Scanning From \u201cApple\u201d to \u201cAnomaly\u201d 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 \u201candroid\u201d to \u201cminibar\u201d, 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 \u2013 crossing from \u201ccoffee\u201d to \u201cpoultry\u201d, for example \u2013 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?<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

This set me wondering whether the ImageNet data set wasn\u2019t simply being used as a foil for Paglen\u2019s sense of mischief. Why else would a cheerleader dominate the \u201csaboteur\u201d category? And do all \u201cdivorce lawyers\u201d really wear red ties?<\/p>\n\n\n\n

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. \u201cA lot of the ideology around AI, and what people imagine it can do, has to do with that simple word \u2018intelligence\u2019,\u201d 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. \u201cIntelligence is the wrong metaphor for what we\u2019ve built, but it\u2019s one we\u2019ve inherited from the 1960s.\u201d<\/p>\n\n\n\n

Paglen fears the way the word intelligence implies some kind of superhuman agency and infallibility to what are in essence giant statistical engines. \u201cThis is terribly dangerous,\u201d he says, \u201cand also very convenient for people trying to raise money to build all sorts of shoddy, ill-advised applications with it.\u201d<\/p>\n\n\n\n

Asked what concerns him more, intelligent machines or the people who use them, Paglen answers: \u201cI worry about the people who make money from them. Artificial intelligence is not about making computers smart. It\u2019s 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.\u201d<\/p>\n\n\n\n

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\u2019s 10th birthday at the Photographers\u2019 Gallery<\/a>. Far from being the face of predatory surveillance capitalism, Li leads efforts to correct the malevolent biases lurking in her creation<\/a>. Wong, incidentally, won\u2019t get that racist slur again, following ImageNet\u2019s announcement<\/a> that it was removing more than half of the 1.2 million pictures of people in its collection.<\/p>\n\n\n\n

Paglen is sympathetic to the challenge Li faces. \u201cWe\u2019re not normally aware of the very narrow parameters that are built into computer vision and artificial intelligence systems,\u201d 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.<\/p>\n\n\n\n

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<\/a>: \u201cIntentionally building a broken demo that gives bad results for shock value reminds me of Edison\u2019s war of the currents<\/a>.\u201d<\/p>\n\n\n\n

Paglen believes, on the contrary, that we have a long way to go before we are paranoid enough about the world we are creating.<\/p>\n\n\n\n

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. \u201cI think,\u201d says Paglen, \u201cthat we\u2019re going through something akin to England and Wales\u2019s Inclosure Acts, when what had been de facto<\/em> public spaces were fenced off by the state and by capital.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

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\u2019s latest photographic installation, From \u201cApple\u201d to \u201cAnomaly\u201d, a related … Continue reading →<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[616,78],"tags":[392,434,500,371,232,241,696],"class_list":["post-2846","post","type-post","status-publish","format-standard","hentry","category-art","category-reviews-and-opinion","tag-ai","tag-barbican","tag-big-data","tag-data","tag-new-scientist","tag-photography","tag-surveillance"],"_links":{"self":[{"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/posts\/2846","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/www.simonings.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2846"}],"version-history":[{"count":2,"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/posts\/2846\/revisions"}],"predecessor-version":[{"id":2850,"href":"http:\/\/www.simonings.net\/index.php?rest_route=\/wp\/v2\/posts\/2846\/revisions\/2850"}],"wp:attachment":[{"href":"http:\/\/www.simonings.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2846"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.simonings.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2846"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.simonings.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2846"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}