Trevor Paglen: ‘This is Not an Apple.’ Or is it?

Trevor Paglen: From ‘Apple’ to ‘Anomaly’
The Curve, Barbican Centre, London
26.09.19 – 16.02.20

The exhibition begins, like the biblical story of mankind, with an apple. Or rather, it begins with a glut of photographs of apples: red apples, green apples, yellow apples, sliced apples, bushels of apples, an apple with the word ‘Google’ carved into it. These images are pinned to a wall and clustered around a single, simple noun: ‘apple’.

The exhibition features a project by artist Trevor Paglen, who culled more than 30,000 images from the online database ImageNet, printed them out in small-scale versions, and pinned them to a long curved wall that unfolds like a sprawling mosaic of the universe, categorised by noun: ‘apple’, ‘soil’, ‘valley’, ‘syringe’, ‘mascot’. It is breathtakingly expansive, like an endless quilt of colours and shapes, a visual catalogue of everything under the sun. Especially seen from a distance of a few feet, it looks like a kind of shimmering mirage of animals, plants, minerals and people.

This infinite visual library is possible because ImageNet is one of the largest publicly available data sets of images online. It is also the foundation of most of the world’s image recognition technology. Algorithms are trained to see the world using its data, which was amassed over the course of nearly a decade by workers who matched images to associated words for pennies on the task website Amazon Mechanical Turk. The result was a data set of 14 million images, catalogued and labelled, so that machines could learn to match images of roses to the word ‘rose’. Paglen makes visible this wild taxonomy in From ‘Apple’ to ‘Anomaly’.

Paglen, an American artist, is known for work that deals with technology, image culture, and the way networked systems of surveillance are integrated invisibly into our everyday lives. His artworks are often ambitious in both scale and methodology. He learned to scuba dive in order to photograph fibre-optic cables on the ocean floor, and launched a satellite into perpetual orbit, in what he called a ‘time capsule’ that critiqued the commercialisation of space. He flew over the headquarters of the United States National Security Agency, aerially photographing the expansive complex of buildings and land. There is an element of activism to his work: the National Security Agency, for instance, objected to Paglen’s aerial flight, but he went ahead with the project anyway. He is less didactic than cerebral, however, and his works visualise highly conceptual and often hidden structures of surveillance.

Sometimes, Paglen uses the tools of the technology he is critiquing, as he did in 2019, when he accidentally entered a new realm of internet fame. He and researcher Kate Crawford released the project ImageNet Roulette, which allowed users to upload their own photos to see how an algorithm trained on ImageNet would label them. The project went instantly viral, as users started encountering racist stereotypes. One twenty-four-year-old Black man found that the algorithm labelled his smiling selfie ‘wrongdoer’ and ‘offender’; an Asian-American woman was labelled ‘gook, slant-eye’. More and more reports of algorithmic bias rolled in, until ImageNet eventually said that it would take down 600,000 images in its systems. ImageNet Roulette was a participatory, networked artwork that immediately, shockingly, exposed the sexism, racism and other biases embedded in image recognition technologies. From ‘Apple’ to ‘Anomaly’, which was based on the same research, exposes how these biases are based in the very foundations of machine learning.

Part of being human is possessing the ability to tell apples from oranges. It’s also being able to tell a green apple from a red one, an apple tree from an apple seed, and an apple – object, fruit – from the Apple logo. Naming and organising the world into categories was one of Adam and Eve’s first tasks in Genesis. It is something we are taught to do in primary school, when we learn to organise blocks by shape and size. Our ability to recognise and place something into a category – black, white, round, square, dog, cat, wolf – is both a primal instinct and a sophisticated impulse that can lead to profiling and stereotyping.

Because of its centrality to human experience, taxonimising is also an essential part of the growing field of image recognition technology; machines need to learn how to break the world into human concepts and categories in order to do any kind of ‘recognition’. Like children, algorithms must be taught the shape and size of an apple, and what makes it distinct from an orange. But breaking the universe into categories is not always so easy, and can lead to big philosophical questions, such as ‘What makes an apple an apple?’ Paglen acknowledges this with a wink to Surrealist painter René Magritte. Near the entrance to the exhibition, Paglen included a reproduction of Magritte’s Ceci n’est pas une pomme, a painting of an apple beneath the phrase ‘This is not an apple’. Magritte was toying with the distinction between an object and the representation of it, the distance between a painted apple and an edible Granny Smith. In Paglen’s updated version, Magritte’s painting has been overlaid with an algorithm that identifies it: ‘red and green apple’. The machine has declared, with no ambiguity, that ceci est une pomme, after all.

Philosophical questions about representation and reality get flattened by algorithms that break the world into definite categories based on human assumptions. The ambition of ImageNet is to make all things knowable by splitting them into groups. This leads to some absurdity, evident in the exhibition. From ‘apple’, we move to ‘apple tree’ and ‘apple orchard’, which is simple enough. Then there is ‘valley’ and ‘soil’, the general realm of the pastoral. And then there is ‘labourer’. Here we see men at work, bent over in rice paddies and irrigated fields. Almost all are non-white. Soon, travelling along the curved wall, we arrive at ‘investor’, a group composed almost entirely of white men in suits and ties, pointing at whiteboards or bent over laptops. We begin, perhaps, to see the cracks in the foundation of this labelled visual universe. We recognise stereotypes about race, class and gender, spat back to us by machines.

The nouns Paglen chooses trend increasingly towards the abstract, and the matching becomes more and more chaotic. The word ‘segregator’ is associated with images of both Barack Obama and George W. Bush. Photos associated with ‘wine drinker’ and ‘alcoholic’ bleed together on the wall, but are subtly different: a white man sniffing wine is a ‘wine drinker’, while a Black woman with a large salted Martini rim is an ‘alcoholic’. Paglen visualises the consequences of a taxonomy that’s based on collective human biases, which are then replicated exponentially by machines. He also shows how difficult it is for machines to make sense of any kind of abstraction, or for us to try to teach them what a ‘segregator’ would look like.

One of the paradoxes is that there is quite a bit of beauty in From ‘Apple’ to ‘Anomaly’, formally and conceptually. The repetition of fried eggs and sunsets side by side leads to surprising visual resonance. Even the glitches can be serendipitously beautiful, as when a cheetah is mistakenly classified as a ‘honeycomb’. The project is strangely awe-inspiring; after all, we are in the presence of an attempt to make a catalogue of the physical world, the vast and expansive fields of human and non-human experience. ImageNet has quasi-religious dimensions, as it attempts to make the entire universe knowable by name.

But in From ‘Apple’ to ‘Anomaly’, Paglen probes the darker underside of this effort to understand by categorising. As in ImageNet Roulette, this project elucidates algorithmic bias, but it also makes clear a more foundational absurdity – the idea that we can easily break the universe down into clearly defined categories and teach machines to understand what they are. The whole system collapses by the end of From ‘Apple’ to ‘Anomaly’, and the exhibition reminds us that our vast, strange, complicated universe is fundamentally impossible to contain within easy-to-define boundaries.

“这不是苹果”,是吗?—论特雷弗·帕格伦的“从‘苹果’到‘异常’”

特雷弗·帕格兰:从“苹果”到“异常”
巴比肯艺术中心,伦敦
2020年9月26日-2020年2月16日

如同《圣经》中的人类故事那样,展览以一颗苹果开始。或者更确切地说,以一堆苹果的照片开始:红苹果、绿苹果、黄苹果、切成片的苹果、大量的苹果,还有一颗上面刻着单词“谷歌”的苹果。这些图片被钉在墙上,围绕着一个简单的名词:“苹果”。

2019年9月26日至2020年2月16日,在伦敦巴比肯艺术中心的展览“从‘苹果’到‘异常’”如此面向观众。这是艺术家特雷弗·帕格伦的项目,他从在线网络数据库ImageNet上截取了超过30000张苹果的图像,将它们按照小尺寸打印出来,并排列于一段长长的弯曲的墙面上,仿佛一个庞大的马赛克的宇宙。这座宇宙以名词分类:“苹果”“土壤”“山谷”“喷射器”“吉祥物”。它是如此惊人的广阔,如同一床颜色和形状都无穷无尽的被子,在阳光下展示着一切。特别是当我们站在几英尺远的地方,它就像一座由动物、植物、矿物和人类组成的闪闪发光的海市蜃楼。

作为全球最大的在线公开图像数据库之一,ImageNet让这个无限的视觉图录成为可能。它也是世界上大多数图像识别技术的资料基础。这些数据由工作人员耗费近十年时间,在任务网站“Amazon Mechanical Turk”上以几分钱的价格,将图像与相关单词进行配对后所积累起来的。其结果是打造了包含1400万张图像的庞大的数据库,系统还对其进行了分类和标记,如此一来,电脑就可以学会将玫瑰的照片与单词“rose”进行匹配。帕格伦在“从‘苹果’到‘异常’”中展示了这种疯狂的分类。

特雷弗·帕格伦是美国艺术家,他的作品涉及技术和图像文化,也探讨了网络监控系统是如何无形地融入了我们的日常生活。他的作品在规模和方法上通常都显得雄心勃勃。为了拍摄海底光纤电缆,帕格伦学会了水肺潜水,他还曾把一颗卫星送入轨道,用这颗他所称的“时间胶囊”来批评太空的商业化。帕格伦飞驶过美国国家安全局总部的上空,俯瞰拍摄这座庞大的建筑群和基地。他的创作展露出几丝激进主义。比如,美国国家安全局就反对帕格伦在基地空域进行飞行,但他还是继续推进了这个项目。然而,帕格伦与其说是在说教,不如说是在进行智力上的思考,他的作品将高度概念化的、往往被隐藏起来的监视结构加以形象化。

帕格伦有时会使用他所批评的技术工具,就像他在2019年所做的那样,当时他意外进入了“网红”的新领域。帕格伦和研究人员凯特·克劳福德( Kate Crawford )上线了“ImageNet轮盘赌”项目,该项目允许用户上传自己的照片,然后体验根据ImageNet训练的算法,这些照片是如何被贴上标签的。“ImageNet轮盘赌”迅速变得热门,因为用户开始遭受种族主义的刻板歧视。一名24岁的黑人男子发现,算法将他的微笑自拍标记为“罪犯”和“违法者”;一名亚裔美国女性被标记为“眯着眼睛的韩国人”。越来越多的关于算法偏见的举报涌入,直到ImageNet最终表示将删除系统中的60万张图片。“ImageNet轮盘赌”是一项具有参与性、网络化的艺术实践,它令人震惊地暴露了被默默嵌入图像识别技术中的性别歧视、种族歧视和其他偏见。“从‘苹果’到‘异常’”也基于同样的研究,艺术家揭示了这些偏见是如何在机器学习的基础上形成的。

大多数人类拥有分辨苹果和橘子的能力。还能分辨绿苹果和红苹果,苹果树和苹果种子,以及作为物体的苹果—一颗水果—和苹果的标志。给万物命名并将其分类,是亚当和夏娃在《创世纪》中的首要任务之一。这是我们在小学学习按照形状和大小组织积木时就了解的东西。我们识别和归类事物的能力—黑、白、圆、方、狗、猫、狼—既是一种原始的本能,也是一种复杂的冲动,甚至可以导致对事物的刻板印象。

由于以人类经验为中心,分类也是图像识别技术发展领域的重要构成要素;机器需要学习如何将世界按照人类的概念加以区分,以便进行任何类型的“识别”。如同孩子,任何算法必须牢记苹果的形状和大小,以及它与橙子的区别。但将宇宙划分为不同类别却并不总是那么容易,甚至可能遭遇一些重大的哲学问题,比如,是什么让苹果成为苹果?帕格伦向超现实主义画家勒内·马格利特( René Magritte )眨了眨眼睛,通过展览指涉了这一点。在展览入口附近,佩格伦展出了一幅马格利特名作《这不是一颗苹果》( Ceci n ‘est pas une pomme )的复制品,画中的苹果下面写着“这不是一颗苹果”。马格利特在玩味物体与它的表现形式之间的区别:一个涂了漆的苹果和一个可食用的绿苹果之间的距离。在帕格伦更新后的版本中,马格利特的画作上被覆盖了一种算法,识别出画里是“红色和绿色的苹果”。机器已经毫不含糊地宣布:无论如何,这是一颗苹果。

关于表象和现实的哲学问题被算法所压制,这些算法根据人类的假设将世界划分为明确的类别。ImageNet的目标是将所有的东西分类,让它们变得可知。这导致了一些在展览中突出的很明显的谬误。从“苹果”转移到“苹果树”和“苹果园”,这很简单;然后是“山谷”和“土壤”,通常的田园牧歌景象。再然后是“劳动者”,我们看到了劳作的人们,弯着腰在稻田里灌溉。几乎所有人都是非白人。很快,沿着弯曲的墙壁,我们来到了“投资者”的板块,这是一个几乎全部由西装革面的白人男子组成的集合,他们指着白板,或俯身在笔记本电脑前。也许,我们开始在这个被贴上标签的视觉宇宙中目睹细小的裂缝。我们认识到关于种族、阶级和性别的刻板印象,这些都经由机器被唾弃给我们。

艺术家选择的名词越来越趋于抽象,相联的匹配也越来越趋于混乱。“分隔夹”一词与巴拉克·奥巴马和乔治·w·布什的形象关联。“饮酒者”和“酒鬼”被挂在同一面墙上,却体现出细微的不同;一个闻着酒杯的白人男子显示为“酒饮者”,而一名端着一大杯马提尼的黑人女性则被称为“酒鬼”。帕格伦将基于人类集体偏见的分类法的结果可视化,然后通过机器成倍地复制。艺术家还展示了这样的现实:机器理解任何类型的抽象是多么的困难,或者,我们试图教会它们“分隔夹”究竟是什么东西这件事情,是多么的困难。

而展览产出的悖论之一在于,“从‘苹果’到‘异常’”在形式和概念上都相当具有审美。重复的煎蛋和并排的日落会产生令人惊讶的视觉共鸣。即使是算法的小故障,也可能导致偶然的美丽,就如同当猎豹被错误地归类为“蜂巢”时那样。艺术家的项目使人深受启发;毕竟,我们正试图为物质世界—人类和非人类经验所构成的广阔领域—进行一一编目。ImageNet具有类似宗教的向度,它试图为整个宇宙命名,使其变得可知。

但在“从‘苹果’到‘异常’”中,帕格伦通过分类探索了这种编目的阴暗面。如同“ImageNet轮盘赌”作出的尝试,展览阐明了算法的偏见,也指出了一个更深层的荒谬之处—我们可以轻易地将宇宙分成定义明确的类别,并教会机器理解它们分别是什么。展览的结尾处,艺术家搭建的整个系统崩溃了。事实提醒我们:辽阔、奇异而复杂的宇宙根本无法被框定在简单的事物边界之中。