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See how an AI system classifies you based on your selfie
ImageNet Roulette will take a stab at categorizing you, and it will fail

看看人工智能系統是如何根據你的自拍照對你進行分類的
ImageNet Roulette將嘗試對你進行分類,而且失敗了



Modern artificial intelligence is often lauded for its growing sophistication, but mostly in doomer terms. If you’re on the apocalyptic end of the spectrum, the AI revolution will automate millions of jobs, eliminate the barrier between reality and artifice, and, eventually, force humanity to the brink of extinction. Along the way, maybe we get robot butlers, maybe we’re stuffed into embryonic pods and harvested for energy. Who knows.

現代人工智能因其日益成熟而經常受到稱贊,但大多說的是世界末日論。如果你瀕臨于世界末日,人工智能革命將使數以百萬計的工作實現自動化,消除現實和技巧之間的障礙,并將最終迫使人類走向滅絕的邊緣。一路走來,也許我們會有機器人管家,也許我們會被塞進胚胎莢里,吸收能量。誰知道呢。



“When we first started conceptualizing this exhibition over two years ago, we wanted to tell a story about the history of images used to ‘recognize’ humans in computer vision and AI systems. We weren’t interested in either the hyped, marketing version of AI nor the tales of dystopian robot futures,” Crawford told the Fondazione Prada museum in Milan, where Training Humans is featured. “We wanted to engage with the materiality of AI, and to take those everyday images seriously as a part of a rapidly evolving machinic visual culture. That required us to open up the black boxes and look at how these ‘engines of seeing’ currently operate.”

“兩年多前,當我們第一次構思這個展覽時,我們就想講一個關于用計算機視覺和人工智能系統來“識別”人類圖像的發展歷史的故事。”克勞福德告訴米蘭普拉達基金會博物館(“訓練人類”項目就在這里形成)說:“我們對人工智能的大肆炒作、市場營銷以及反烏托邦機器人未來的故事都不感興趣。我們想參與人工智能的實質性工作,并把那些日常圖像作為快速發展的機器視覺文化的一部分來認真對待。這就要求我們能打開黑匣子,看看這些“視覺引擎”目前是如何運轉的。”

It’s a worthy pursuit and a fascinating project, even if ImageNet Roulette represents the goofier side of it. That’s mostly because ImageNet, a renown training data set AI researchers have relied on for the last decade, is generally bad at recognizing people. It’s mostly an obxt recognition set, but it has a category for “People” that contains thousands of subcategories, each valiantly trying to help software do the seemingly impossible task of classifying a human being.

這是一個值得追求、有吸引力的項目,即使ImageNet Roulette 代表了其更愚蠢的一面。這主要是因為ImageNet,這個人工智能研究人員過去十年一直依賴的著名的訓練數據集,通常不善于識別人像。它主要是一個對象識別集,但是它有一個“人”的類別,其中包含成千上萬個子類,每個子類都積極地嘗試以幫助軟件完成似乎不可能完成的任務,對一個人進行識別分類。

And guess what? ImageNet Roulette is super bad at it.

猜猜怎么著?ImageNet Roulette非常不擅長這個。



I don’t even smoke! But for some reason, ImageNet Roulette thinks I do. It also appears to believe that I am located in an airplane, although to its credit, open office layouts are only slightly less suffocating than narrow metal tubes suspended tens of thousands of feet in the air.

我根本就不抽煙!但出于某種原因,ImageNet Roulette卻認為我抽。而且它好像還也以為我是在一架飛機上,盡管值得點贊的是,開放式辦公室的布局比掛在幾萬英尺高空中令人窒息的狹窄金屬管好那么一點點。



ImageNet Roulette was put together by developer Leif Ryge working under Paglen, as a way to let the public engage with the art exhibition’s abstract concepts about the inscrutable nature of machine learning systems.

ImageNet Roulette是由帕格倫旗下的開發者萊夫·雷奇設計的,是一種讓公眾參與藝術展覽的抽象概念的方式,使他們能了解機器學習系統不可思議的本質。

Here’s the behind-the-scenes magic that makes it tick:

以下就是魔術幕后的秘密,正是它們令其發揮作用:

ImageNet Roulette uses an open source Caffe deep learning frxwork (produced at UC Berkeley) trained on the images and labels in the “person” categories (which are currently ‘down for maintenance’). Proper nouns and categories with less than 100 pictures were removed.

ImageNet Roulette 使用的是開源的Caffe深度學習框架(由加州大學伯克利分校開發),該框架用于“人”的類別(目前在“停機維護”)的圖像和標識訓練。通過不到100幅圖片對專有名詞和類別進行剔除。



ImageNet contains a number of problematic, offensive and bizarre categories - all drawn from WordNet. Some use misogynistic or racist terminology. Hence, the results ImageNet Roulette returns will also draw upon those categories. That is by design: we want to shed light on what happens when technical systems are trained on problematic training data. AI classifications of people are rarely made visible to the people being classified. ImageNet Roulette provides a glimpse into that process – and to show the ways things can go wrong.

ImageNet包含的許多有問題的、攻擊性的和奇怪的類別——都是從英語詞典WordNet上獲取的。其中有些使用的是厭惡女性或種族主義的術語。因此,ImageNet Roulette的結果也將借鑒這些類別。這是故意設計成這樣的:我們想弄清楚當技術系統使用有問題的訓練數據進行訓練時會發生什么。人工智能對人的分類很少讓被分類的人看到。ImageNet Roulette使我們得以對這一過程略窺一二——而且表明這樣做事情就可能會出錯。

ImageNet is one of the most significant training sets in the history of AI. A major achievement. The labels come from WordNet, the images were scraped from search engines. The 'Person' category was rarely used or talked about. But it's strange, fascinating, and often offensive.
— Kate Crawford (@katecrawford) September 16, 2019

ImageNet 是人工智能歷史上最重要的訓練集之一,是一項重大的成就。這些標簽來自WordNet英語詞典,這些圖像是通過搜索引擎搜羅過來的。“人”這一類別很少被使用或談論。但這很奇怪、很吸引人,而且常常令人不快。
——凱特·克勞福德(@katecrawford)2019年9月16日

Although ImageNet Roulette is a fun distraction, the underlying message of Training Humans is a dark, but vital, one.

盡管ImageNet Roulette 是一種有趣的消遣方式,但其“訓練人類”項目所傳遞出的潛在信息卻是一個黑暗但至關重要的信息。

“Training Humans explores two fundamental issues in particular: how humans are represented, interpreted and codified through training datasets, and how technological systems harvest, label and use this material,” reads the exhibition descxtion “As the classifications of humans by AI systems becomes more invasive and complex, their biases and politics become apparent. Within computer vision and AI systems, forms of measurement easily — but surreptitiously — turn into moral judgments.”

“‘訓練人類’項目旨在探索兩個基本問題:如何通過訓練數據集來表現、解釋和編碼人類,以及(人工智能)技術系統是如何收獲、標記和使用這種材料的,”展覽描述中寫道,“隨著人工智能系統對人類的分類變得更具攻擊性和復雜性,它們的偏見和政治就變得明顯。在計算機視覺和人工智能系統中,進行測量的形式很容易變成道德判斷——但很隱蔽。”