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AI 可以帮我们超越人类的能力吗？神经网络艺术家索菲亚·克雷斯波（Sofia Crespo）向我们展示了融合科技、自然和艺术的荟萃，为我们带来了拓宽我们创意和想象边界的动物们。她对合成生物的艺术呈现融合了真实世界中濒危物种的照片，创造了崭新的物种，同时怀着鼓励真实世界中的生物保护的目的。一起来见证这关于不存在的生物的推测性研究，由 AI 实现。
I'd like to start by asking you to imagine a color that you've never seen before. Just for a second give this a try. Can you actually visualize a color that you've never been able to perceive? I never seem to get tired of trying this although I know it's not an easy challenge. And the thing is, we can't imagine something without drawing upon our experiences.
我想先请你们想象一种从未见过的颜色。姑且来试一下吧。你能想象出一种无法感知的颜色吗？我对此乐此不疲，即使我知道这并不容易。问题是，如果不基于我们自身的经历，我们就无法想象出某些东西。
A color we haven't yet seen outside the spectrum we can perceive is outside our ability to conjure up. It's almost like there's a boundary to our imagination where all the colors we can imagine can only be various shades of other colors we have previously seen. Yet we know for a fact that those color frequencies outside our visible spectrum are there.
我们从未见过的颜色，超出了我们可以感知的光谱，也就超出了我们的想象力。这就感觉像是我们的想象力有一个边界，我们可以想象出的所有颜色只能是我们之前见过的颜色的不同变体。但是我们知道有这么一个事实，超出可见光频谱的颜色频率确实存在。
And scientists believe that there are species that have many more photo receptors than just the three color ones we humans have. Which, by the way, not all humans see the world in the same way. Some of us are colorblind to various degrees, and very often we don't even agree on small things, like if a dress on the internet is blue and black or white and gold. But my favorite creature, one of my favorite creatures, is the peacock mantis shrimp, which is estimated to have 12 to 16 photo receptors. And that indicates the world to them might look so much more colorful.
科学家们相信有些物种拥有更多的光感受器，比我们人类拥有的三色光感受器要多。顺带一提，不是所有人眼中的世界都是一样的。有些人会有不同程度的色盲，我们经常会就一些小事争论不休，比如网上的一条裙子是蓝黑还是白金。但是我最喜欢的生物，最喜欢的生物之一，是雀尾螳螂虾，估计有12至16个光感受器。意味着它们眼中的世界会更加色彩斑斓。
So what about artificial intelligence? Can AI help us see beyond our human capabilities? Well, I've been working with AI for the past five years, and in my experience, it can see within the data it gets fed. But then you might be wondering, OK, if AI can't help imagine anything new, why would an artist see any point in using it? And my answer to that is because I think that it can help augment our creativity as there's value in creating combinations of known elements to form new ones.
那么，人工智能（AI）又如何呢？AI可以帮助我们超越人类的视觉吗？我在过去的五年里一直在用AI工作，根据我的经验，它能看到的内容取决于输入的数据。但是你可能会想，好吧，如果AI不能想象出新东西，艺术家为什么要用它呢？我的答案是，我认为AI可以增强我们的创造力，通过组合既有的元素，创造新元素，它就产生了价值。
And this boundary of what we can imagine based on what we have experienced is the place that I have been exploring. For me, it started with jellyfish on a screen at an aquarium and wearing those old 3D glasses, which I hope you remember, the ones with the blue and red lens. And this experience made me want to recreate their textures. But not just that, I also wanted to create new jellyfish that I hadn't seen before, like these.
基于我们的经历形成的想象力的边界是我探索的领域。对我来说，一切始于水族馆屏幕上的水母，我戴着那种老式3D眼镜，但愿你还有印象，镜片一片蓝一片红的那种。这个体验让我想重现它们的质感。但是不仅如此，我还想创造我从未见过的新型水母，就像这种。
And what started with jellyfish, very quickly escalated to other sea creatures like sea anemone, coral and fish. And then from there came amphibians, birds and insects. And this became a series called "Neural Zoo". But when you look closely, what do you see? There's no single creature in these images. And AI augments my creative process by allowing me to distill and recombine textures. And that's something that would otherwise take me months to draw by hand. Plus I'm actually terrible at drawing.
我从水母开始，迅速扩展到了其他海洋生物，比如海葵、珊瑚和鱼类。然后是两栖动物、鸟类和昆虫。它们组成了一个系列，名为《神经动物园》（NeuralZoo）。但是如果你仔细看看，你会看见什么呢？这些图片里的都不是单一生物。AI增强了我发挥创意的过程，让我可以提取、重组它们的质感。如果我要手绘，可能要花上好几个月。而且我画画水平太差了。
So you could say, in a way, what I'm doing is a contemporary version of something that humans have already been doing for a long time, even before cameras existed. In medieval times, people went on expeditions, and when they came back they would share about what they saw to an illustrator.
你可以这么说，从某种角度来看，我在做的是早在相机出现之前，人类就一直在做的事，但是是它的现代版本。中世纪，人们踏上远征，回来之后他们会向画师描述他们的见闻。
And the illustrator, having never seen what was being described, would end up drawing based on the creatures that they had previously seen and in the process creating hybrid animals of some sort. So an explorer might describe a beaver, but having never seen one, the illustrator might give it the head of a rodent, the body of a dog and a fish-like tail.
画师从未见过描绘之物，于是他们会按照他们之前见过的生物绘制，再自创一些合成动物。也许出行者描述了一只海狸，但是画师从没见过海狸，于是画了啮齿动物的头部，狗的身子和类似鱼类的尾巴。
In the series "Artificial Natural History", I took thousands of illustrations from a natural history archives, and I fed them to a neural network to generate new versions of them. But up until now, all my work was done in 2D. And with the help of my studio partner, Feileacan McCormick, we decided to train a neural network on a data set of 3D scanned beetles.
我在《人工自然历史》（ArtificialNaturalHistory）系列中从自然历史档案中提取了几千张插图，输入神经网络，产生新版本的插图。但是时至今日，我的所有作品都是以2D的形式完成的。在我的工作室伙伴菲力肯·麦考密克（FeileacanMcCormick）的帮助下，我们打算基于经3D扫描的甲虫的数据集训练一个神经网络。
But I must warn you that our first results were extremely blurry, and they looked like the blobs you see here. And this could be due to many reasons, but one of them being that there aren't really a lot of openly available data sets of 3D insects. And also we were repurposing a neural network that normally gets used to generate images to generate 3D. So believe it or not, these are very exciting blobs to us.
但是我要提醒你的是我们的第一批结果非常模糊，如图所示的一坨。导致这种情况的原因可能有很多，但是其中一个原因是公开可用的3D昆虫数据集比较有限。而且我们也改变了神经网络的功能，由常见的生成图片转向了生成3D结果。无论如何，它们都是令我们激动的一坨物体。
But with time and some very hacky solutions like data augmentation, where we threw in ants and other beetle-like insects to enhance the data set, we ended up getting this, which we've been told they look like grilled chicken. But hungry for more, we pushed our technique, and eventually they ended up looking like this.
随着时间的推移和一些另辟蹊径的产品的出现，如数据增强，如果我们输入蚂蚁或者其他类似甲虫的昆虫增强了数据集，我们会得到这样的结果，有人说它们长得像烤鸡。但是我们的野心不止于此，我们改进了技术，最终得到了这样的结果。
We use something called 3D style transfer to map textures onto them, and we also trained a natural language model to generate scientific-like names and anatomical deions. And eventually we even found a network architecture that could handle 3D meshes. So they ended up looking like this.
我们用了一项叫做“3D风格迁移”的技术把纹理附着到输出结果上，我们还训练了一个自然语言模型，生成科研风的名字和生物结构描述。我们最后甚至找到了一个网络体系结构处理3D网格。最后的结果是这样的。
And for us, this became a way of creating kind of a speculative study --A speculative study of creatures that never existed, kind of like a speculative biology. But I didn't want to talk about AI and its potential unless it brought me closer to a real species. Which of these do you think is easier to find data about online? Yeah, well, as you guessed correctly, the red panda.
对我们来说，这已经类似于一种推测性研究……关于不存在的生物的推测性研究，类似猜想生物。但是我并不想谈论AI和它的潜力，除非AI产生的结果接近真实存在的物种。图上的两种生物，你觉得哪个更容易在网上找到数据？你猜的没错，小熊猫。
And this maybe could be due to many reasons, but one of them being how cute they are, which means we photograph and talk about them a lot, unlike the boreal felt lichen. But both of them are classified as endangered. So I wanted to bring visibility to other endangered species that don't get the same amount of digital representation as a cute, fluffy red panda.
可能会有很多原因，但是其中一个原因就是它们太可爱了，所以我们总是会给它们拍照，讨论它们，北方毡状地衣就没这种待遇了。但是这两种生物都被定为了频危物种，我想请大家都去关注一下其他的濒危物种，它们不一定会像毛茸茸的可爱小熊猫那样拥有那么高的数字化曝光度。
And to do this, we trained an AI on millions of images of the natural world, and then we prompted with text to generate some of these creatures. So when prompted with a text, "an image of a critically endangered spider, the peacock tarantula" and its scientific name, our model generated this. And here's an image of the real peacock tarantula, which is a wonderful spider endemic to India.
为了达成这个目标，我们用自然界的百万张图片训练了AI，然后我们用文字提示，生成一些生物。如果我们输入这样的提示：“蓝宝石华丽雨林——一种极度濒危的蜘蛛的图片”和它的学名，我们的模型会输出这样的结果。这是一张真实的蓝宝石华丽雨林的照片，它是一种分布于印度本土的华丽蜘蛛。
But when prompted with a text "an image of a critically endangered bird, the mangrove finch," our model generated this. And here's a photo of the real mangrove finch.
如果我们输入这样的提示：“红树林雀——一种极度濒危的鸟类的图片”，我们的模型会生成这样的结果。这是真实的红树林雀的照片。
Both these creatures exist in the wild, but the accuracy of each generated image is fully dependent on the data available. These chimeras of our everyday data to me are a different way of how the future could be. Not in a literal sense, perhaps, but in the sense that through practicing the expanding of our own imagination about the ecosystems we are a part of, we might just be better equipped to recognize new opportunities and potential. Knowing that there's a boundary to our imagination doesn't have to feel limiting. On the contrary, it can help motivate us to expand that boundary further and to seek out colors and things we haven't yet seen and perhaps enrich our imagination as a result.
这两种动物都在自然界中真实存在，但是生成图片的准确度完全取决于可用的数据。由我们日常数据产生的“怪物”对我来说是对未来的另一种畅想。也许不是字面意思，但是通过扩展我们对我们所处生态环境的想象，我们也许更有可能会发现新机会和新潜力。清楚地知道我们的想象力是有界限的，并不代表我们得束手束脚。相反地,这可以鼓励我们拓展边界，寻找我们没有见过的颜色和事物，也许最终会丰富我们的想象。
So thank you.
谢谢你。
来源：TED演讲
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