原创翻译:龙腾网 http://www.ltaaa.com 翻译:roroho 转载请注明出处
论坛地址:http://www.ltaaa.com/bbs/thread-489067-1-1.html

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

看看人工智能系统是如何根据你的自拍照对你进行分类的
ImageNet Roulette将尝试对你进行分类,而且失败了



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.

现代人工智能因其日益成熟而经常受到称赞,但大多说的是世界末日论。如果你濒临于世界末日,人工智能革命将使数以百万计的工作实现自动化,消除现实和技巧之间的障碍,并将最终迫使人类走向灭绝的边缘。一路走来,也许我们会有机器人管家,也许我们会被塞进胚胎荚里,吸收能量。谁知道呢。



“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.”

“两年多前,当我们第一次构思这个展览时,我们就想讲一个关于用计算机视觉和人工智能系统来“识别”人类图像的发展历史的故事。”克劳福德告诉米兰普拉达基金会博物馆(“训练人类”项目就在这里形成)说:“我们对人工智能的大肆炒作、市场营销以及反乌托邦机器人未来的故事都不感兴趣。我们想参与人工智能的实质性工作,并把那些日常图像作为快速发展的机器视觉文化的一部分来认真对待。这就要求我们能打开黑匣子,看看这些“视觉引擎”目前是如何运转的。”

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.

这是一个值得追求、有吸引力的项目,即使ImageNet Roulette 代表了其更愚蠢的一面。这主要是因为ImageNet,这个人工智能研究人员过去十年一直依赖的著名的训练数据集,通常不善于识别人像。它主要是一个对象识别集,但是它有一个“人”的类别,其中包含成千上万个子类,每个子类都积极地尝试以帮助软件完成似乎不可能完成的任务,对一个人进行识别分类。

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

猜猜怎么着?ImageNet Roulette非常不擅长这个。



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.

我根本就不抽烟!但出于某种原因,ImageNet Roulette却认为我抽。而且它好像还也以为我是在一架飞机上,尽管值得点赞的是,开放式办公室的布局比挂在几万英尺高空中令人窒息的狭窄金属管好那么一点点。



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是由帕格伦旗下的开发者莱夫·雷奇设计的,是一种让公众参与艺术展览的抽象概念的方式,使他们能了解机器学习系统不可思议的本质。

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

以下就是魔术幕后的秘密,正是它们令其发挥作用:

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 使用的是开源的Caffe深度学习框架(由加州大学伯克利分校开发),该框架用于“人”的类别(目前在“停机维护”)的图像和标识训练。通过不到100幅图片对专有名词和类别进行剔除。



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包含的许多有问题的、攻击性的和奇怪的类别——都是从英语词典WordNet上获取的。其中有些使用的是厌恶女性或种族主义的术语。因此,ImageNet Roulette的结果也将借鉴这些类别。这是故意设计成这样的:我们想弄清楚当技术系统使用有问题的训练数据进行训练时会发生什么。人工智能对人的分类很少让被分类的人看到。ImageNet Roulette使我们得以对这一过程略窥一二——而且表明这样做事情就可能会出错。

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 是人工智能历史上最重要的训练集之一,是一项重大的成就。这些标签来自WordNet英语词典,这些图像是通过搜索引擎搜罗过来的。“人”这一类别很少被使用或谈论。但这很奇怪、很吸引人,而且常常令人不快。
——凯特·克劳福德(@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.

尽管ImageNet Roulette 是一种有趣的消遣方式,但其“训练人类”项目所传递出的潜在信息却是一个黑暗但至关重要的信息。

“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.”

“‘训练人类’项目旨在探索两个基本问题:如何通过训练数据集来表现、解释和编码人类,以及(人工智能)技术系统是如何收获、标记和使用这种材料的,”展览描述中写道,“随着人工智能系统对人类的分类变得更具攻击性和复杂性,它们的偏见和政治就变得明显。在计算机视觉和人工智能系统中,进行测量的形式很容易变成道德判断——但很隐蔽。”