穿越艺术世界的旅程，可帮助您了解AI (A journey through the world of Art to help you understand AI)

At first glance, Artificial Intelligence and Art have little in common — except for a few letters. Artificial Intelligence (AI) has been around for only a couple of decades, whereas some works of art have been telling stories and captivating viewers for tens of thousands of years. In this blog, we will use eight artworks to tell a completely different story: the fundamentals of AI.

乍一看，人工智能和艺术之间的共同点很少-除了几个字母。 人工智能(AI)诞生已有几十年了，而几百万年来，一些艺术品一直在讲故事和吸引观众。 在此博客中，我们将使用八件艺术品讲述一个完全不同的故事：人工智能的基础。

I’ve always been a firm believer that true experts are to be able to explain their field to anyone who is interested. And given the impact that AI has and will continue to have on our businesses and societies, we need different narratives to reach a wide range of audiences. At Prosus Group, we invest heavily into training ‘AI Translators’, to give everyone in the organization a basic level of AI understanding so that they are able to identify AI opportunities in their field, and know how to act on these opportunities. This is my version of that narrative to explain the basics of AI to a non-technical audience — leveraging my background in art history. When you’re interested in either art, art history or understanding the fundamentals of AI, keep on reading. Disclaimer: due to my roots, you might see a ‘bias’ in the dataset I’ve used towards Dutch and European art!

我一直坚信真正的专家应该能够向感兴趣的任何人解释他们的领域。 考虑到人工智能已经并将继续对我们的企业和社会产生影响，我们需要不同的叙述方式来吸引广泛的受众。 在Prosus Group ，我们投入大量资金来培训“ AI译者 ”，以使组织中的每个人都对AI有所了解，从而使他们能够识别自己领域的AI机会，并知道如何应对这些机会。 这是我对非技术观众讲解AI基础知识的那篇叙述的版本-利用我的艺术史背景。 如果您对艺术，艺术史或对AI的基本知识感兴趣，请继续阅读。 免责声明：由于我的血统，您可能会在我用于荷兰和欧洲艺术的数据集中看到“偏见”！

At Prosus Group, we invest heavily into training ‘AI Translators’, to give everyone in the organization a basic level of AI understanding so that they are able to identify AI opportunities in their field, and know how to act on these opportunities.

在Prosus Group，我们投入大量资金来培训“ AI翻译人员”，使组织中的每个人都对AI有了基本的了解，以便他们能够识别自己领域的AI机会，并知道如何应对这些机会。

1.看不见的增强 (1. Invisible enhancement)

17th century painter Johannes Vermeer has painted a number of masterpieces that still enchant us today, including The Milkmaid and The Girl with the Pearl Earring. His secret? Allegedly, he used an optical tool called a camera obscura: a small and dark room with a pinhole, through which a scene is projected onto the wall of the room — like a camera without a film. It still takes a skilled artist to transform this projection into an artwork. However, the camera obscura might have helped Vermeer to create aesthetically pleasing paintings.

17世纪画家约翰内斯·维米尔 ( Johannes Vermeer )绘制了许多今天仍令我们着迷的杰作，包括《挤奶女仆》和《 戴 珍珠耳环的女孩》 。 他的秘密？ 据称，他使用了一种称为照相机暗箱的光学工具：一个带有针Kong的黑暗小房间，通过它，场景被投射到房间的墙上，就像没有胶卷的照相机一样。 仍然需要熟练的艺术家将这种投影转换为艺术品。 但是， 摄像机的遮盖力可能有助于维米尔(Vermeer)创作出美学上令人愉悦的画作。

The same holds for Artificial Intelligence: AI applications are often running in the background to create invisible enhancements. In popular media, AI typically gets depicted as invasive, humanoid and unearthly, like an army of killer robots that will come to take over the world. In reality, AI is usually the opposite of foreign: it’s products that you are already familiar with, but better. It’s a car, but then self-driving. It’s a social media app, but more personalized. It’s farming, but smarter and more efficient.

人工智能也是如此：人工智能应用程序通常在后台运行以创建看不见的增强功能。 在流行媒体中，人工智能通常被描述为具有侵略性，类人生物且不为人所知，就像将要占领世界的杀手机器人大军一样。 实际上，人工智能通常与外国相反：它是您已经熟悉但更好的产品。 它是汽车，但随后自动驾驶。 这是一个社交媒体应用程序，但更加个性化。 它是农业，但更智能，更高效。

2.丢失的那一块 (2. The missing piece)

Not all works of art have endured the test of time. From the original decorations of the Egyptian Tomb Chapel of Nebamun, only fragments remain. They offer us glimpses of what it would have been like to walk into the ancient Tomb, but have missing pieces or gaps. We can try to guess what would have been depicted on those missing pieces, based on the rest of the artwork, or on other similar works of art.

并非所有艺术品都经受了时间的考验。 从Nebamun埃及墓教堂的原始装饰中，只剩下碎片。 他们为我们提供了走进古代坟墓的一瞥，但缺少碎片或空隙。 我们可以尝试根据其余的艺术品或其他类似的艺术品来猜测那些遗失的物品上所描绘的内容。

The most common application is AI is to make predictions, which you can think of as trying to fill in the missing piece of an artwork. In AI, you use the information that you have (about the past) in order to predict missing information (the future).

最常见的应用是AI进行预测，您可以将其视为试图填充艺术品的缺失部分。 在AI中，您使用所拥有的信息(关于过去)来预测丢失的信息(未来)。

For example, you can try to predict which song someone might like based on what you know about this person and what you know about other, similar people. This gets harder when you have less data about this person, or less data about similar people. In the terms of the artwork, filling in the gap is more difficult when you’re only looking at a very small piece of the total artwork or there are no other artworks out there that are similar. The more data you have, the more accurate your predictions become.

例如，您可以尝试根据您对该人的了解以及对其他类似人的了解来预测某人可能喜欢的歌曲。 当有关此人的数据较少或与类似人的数据较少时，这将变得更加困难。 就艺术品而言，当您仅查看全部艺术品中很小的一块或没有其他相似的艺术品时，填补空白就更加困难。 您拥有的数据越多，您的预测就越准确。

In AI, you use the information that you have (about past) in order to predict missing information (about future).

在AI中，您使用拥有的信息(关于过去)来预测缺失的信息(关于未来)。

3.简单的事情，重复 (3. Simple things, repeated)

Have you ever looked at a pointillist painting up close? It’s all in the name: artists like Georges Seurat, Paul Signac and Camille Pissarro experimented with paintings consisting of a collection of small dots. From a distance, these dots blend into colors and shapes. But the fundamental building blocks of this painting style are very simple: anyone can paint a dot.

您是否曾经看过近距离点画的点画师？ 一切皆有其名称：像乔治·修拉 ( Georges Seurat) ，保罗·西尼亚克 (Paul Signac)和卡米尔·毕沙罗(Camille Pissarro)这样的艺术家尝试了由小点组成的绘画。 从远处看，这些点融合为颜色和形状。 但是这种绘画风格的基本组成部分非常简单：任何人都可以画点。

At the risk of making AI seem very un-cool, you could say the same about AI algorithms. The mathematics behind AI algorithms are actually very simple: mostly addition and multiplication. The trick is that these steps are repeated many times — billions of times actually for very complex tasks. Luckily, we can use powerful computers to make those computations faster.

冒着使AI看起来非常酷的风险，您可以对AI算法说同样的话。 AI算法背后的数学实际上非常简单：主要是加法和乘法。 诀窍是这些步骤会重复很多次-对于非常复杂的任务实际上是数十亿次。 幸运的是，我们可以使用功能强大的计算机来加快计算速度。

4.允许复制 (4. Copying is allowed)

Think: Roman art. What do you see? Likely, you are thinking about marble statues. What if you were told that many of the best known Roman statues are actually copies of Greek statues? Would that make these artworks less impressive, or less effective in their function to make you admire them?

认为：罗马艺术。 你看到了什么？ 您可能正在考虑大理石雕像。 如果您被告知许多最著名的罗马雕像实际上是希腊雕像的副本该怎么办？ 这会使这些艺术品不那么令人印象深刻，或者使它们无法发挥功能使您欣赏它们吗？

Copying is also very common in the world of AI — though we call it open-source. Data scientists typically don’t write their own algorithms, especially when they work on applying AI in business. When done in the right way, re-using existing algorithms can be a fast-lane to creating impact through AI in your company.

复制在AI世界中也很常见-尽管我们称其为开源 。 数据科学家通常不编写自己的算法，尤其是当他们致力于将AI应用到业务中时。 如果以正确的方式完成操作，则重新使用现有算法可以成为在您的公司中通过AI产生影响的快速通道。

What is different and unique in each application, is the data that is used to train the algorithm as well as the process of bringing that unique data into the right shape for an opensource algorithm. Data scientists tweak certain model parameters to make it suitable for their application and their data. That wasn’t very different for the Roman artists. You often see a tree trunks appearing to support the statue: the original (and lighter) Greek bronze statues didn’t need these fortifications.

在每个应用程序中，不同的和唯一的是用于训练算法的数据，以及将这些唯一的数据转换为适合开源算法的正确形状的过程。 数据科学家调整某些模型参数，使其适合其应用程序和数据。 对于罗马艺术家而言，这并没有太大不同。 您经常会看到一个树干出现来支撑雕像：原始的(和较轻的)希腊青铜雕像不需要这些防御工事。

5.不要像梵高 (5. Don’t be like Van Gogh)

Vincent van Gogh is not only famous for his paintings, but also for his tragic life story. The current fame of his works forms a stark contrast with his fame when he was alive. He only sold a few paintings during his lifetime and constantly struggled with poverty, despite his efforts to find potential buyers. It was Van Gogh’s family that successfully promoted his works and managed to exhibit them after Van Gogh’s death.

文森特·梵高 ( Vincent Van Gogh )不仅以他的画而闻名，而且以其悲惨的人生故事而闻名。 他目前的名声与他在世时的名气形成鲜明对比。 尽管他一直在努力寻找潜在的买家，但他一生只卖出了几幅画，并且一直在贫困中挣扎。 凡高的家人成功地推广了他的作品，并在凡高去世后设法将它们展示出来。

The relationship between a data scientist and the rest of the organization that she works in, should be the opposite of Van Gogh’s solo journey to find a buyer for his work. Data scientists shouldn’t be working in isolation on algorithms that no-one understands or appreciates, but instead work in close cooperation with the rest of the organization. Internal subject matter experts, trained as AI translators, help identify opportunities for predictions or AI-driven automation. Product or project managers lead teams that integrate AI tools to create business or customer impact. Software engineers help to scale AI algorithms to work real-time and thousands or millions of times per second. Creating impact through AI takes much more than an algorithm — even though that’s typically the part of the field that gets most attention. It takes a full organization to efficiently and effectively identify customer and business problems — and build solutions that work.

数据科学家与她所工作的组织中其他成员之间的关系，应该与梵高为自己的工作寻找买家的独行旅程相反。 数据科学家不应该孤立地研究没有人理解或赞赏的算法，而应该与组织的其他成员紧密合作。 内部主题专家，受过AI翻译员的培训，可帮助您发现预测或AI驱动的自动化机会。 产品或项目经理领导团队，整合AI工具以创造业务或客户影响。 软件工程师可帮助扩展AI算法，使其实时工作，每秒可进行数千或数百万次。 通过AI创造影响力不仅仅需要算法，即使这通常是引起最多关注的领域。 它需要一个完整的组织来有效地识别客户和业务问题，并建立有效的解决方案。

It takes a full organization to efficiently and effectively identify customer and business problems — and build solutions that work.

它需要一个完整的组织来有效地识别客户和业务问题，并建立有效的解决方案。

6.变革至关重要 (6. Change is essential)

If you were to travel back in time to the moment that your favorite work of art was painted, you might be very surprised by what you see (by many things, obviously, though I’m talking about the painting here). Over the years, many of the world’s most famous works of art have been partially or fully overpainted.

如果您时光倒流回到最喜欢的艺术品被绘画的那一刻，您可能会对看到的东西感到惊讶(很明显，尽管有很多事情，尽管我在这里谈论的是绘画)。 多年来，世界上许多最著名的艺术品都被部分或全部粉刷了。

The most obvious reason for overpainting is to repair damages and discolorings. Like paintings, AI models need to be kept in good condition. They are adjusted and ‘restored’ regularly, for example because the underlying customer behavior has changed. This typically means that models need to be re-learn based on more recent data, which is called re-training.

过度喷漆的最明显原因是修复损坏和褪色。 就像绘画一样，AI模型需要保持良好状态。 他们会定期进行调整和“恢复”，例如，因为潜在的客户行为已更改。 这通常意味着需要根据最新数据重新学习模型，这称为重新训练。

Some overpaintings are arguably no improvements. Over time, one of Rembrandt’s self-portraits accumulated a big hat and long curls. This is also a risk for AI models: after retraining, you need to check whether the model still produces the results you initially intended.

可以说有些粉刷没有任何改善。 随着时间的流逝，伦勃朗的自画像之一积累了大礼帽和长卷发 。 这对于AI模型也是有风险的：重新训练后，您需要检查模型是否仍然产生您最初想要的结果。

7.范式转变 (7. The paradigm shift)

For the majority of the 19th century, artists were painting realistic, life-like scenes. Fast-forward to the early 20th century and you see a wide range of abstract, impressionist and cubist artists in style. What happened in the meantime that caused this shift away from realistic art? Photography.

在19世纪的大部分时间里，艺术家都在画逼真的逼真的场景。 快进到20世纪初期，您会看到各种各样的抽象，印象派和立体派艺术家。 同时发生了什么，导致这种脱离现实艺术的转变？ 摄影。

These major changes in the artistic movements cannot be attributed to photography alone. But what’s clear is that there’s no return to the era before photography — realistic paintings as ‘visual records’ no longer have the same appeal as they used to, before photography was invented.

艺术运动中的这些重大变化不能仅归因于摄影。 但显而易见的是，摄影术再也没有回到时代了-作为“视觉记录”的现实绘画已不再具有摄影术发明之前的吸引力。

We cannot attribute the current high time of AI to the abundance of data and advances in computation power and AI algorithms alone. Though again what is clear, is that there’s no return to an era without AI. Changes such as personalization and automation are here to stay — and will only become more common over time.

我们不能将AI当前的高时间归因于大量数据以及仅凭计算能力和AI算法的进步。 再次清楚的是，没有人工智能将无法回到这个时代。 个性化和自动化之类的更改将保留下来，并且随着时间的推移只会变得越来越普遍。

There’s no return to an era without AI.

没有人工智能，这是没有回归的时代的。

8.部分科学，另一部分… (8. Part science, part…)

Some artworks trigger a feeling of ‘I could have done that’ — or in more extreme cases, ‘my dog could have painted this’. Without insulting your artistic skills, I would dare to argue that this is typically not true. What has made some of these seemingly simple artworks so successful and famous is a complex dynamic involving timing, reputation and originality.

一些艺术品引发一种“我本可以做到的”的感觉，或者在更极端的情况下，“我的狗本可以画出来的”。 在不侮辱您的艺术技巧的情况下，我敢说这通常是不正确的。 这些看似简单的艺术品之所以如此成功和成名，是因为涉及时间，声誉和原创性的复杂动态。

As someone who appreciates the value of making AI sound simple, I have to acknowledge that not everything about AI actually is simple. We’re not yet at the point that just anyone can build an AI model, even though companies are investing in democratizing AI. At Prosus Group, we are also working hard to scale AI knowledge and we have developed amazing educational programs in-house for different roles and levels — from product managers to executives. Until we do get to the stage where anyone can train an AI algorithm, our AI teams will continue to balance science, entrepreneurship… and a bit of art.

作为一个欣赏使AI听起来简单的价值的人，我必须承认，并不是所有关于AI的东西实际上都是简单的。 尽管公司正在投资使AI民主化，但我们目前还没有人可以建立AI模型。 在Prosus Group，我们也在努力扩展AI知识，并且我们针对各种角色和级别(从产品经理到执行官)内部开发了惊人的教育计划。 在我们到达任何人都可以训练AI算法的阶段之前，我们的AI团队将继续在科学，企业家精神和一些艺术之间取得平衡。

I’d like to thank Nishikant Dhanuka and Zulkuf Genc, my colleagues from the Prosus AI team, for their suggestions and help in editing. If you’re interested in reading more on the topic of Art and AI, I can recommend sources such as this blog. There are also many great free resources available to continue learning about AI, such as the Elements of AI course. Also if there are any further questions or suggestions, feel free to reach out to us at datascience@prosus.com. Enjoy your learning journey!

我要感谢 Prosus AI团队的同事 Nishikant Dhanuka 和 Zulkuf Genc ，他们的建议和编辑帮助。 如果您有兴趣阅读有关艺术和人工智能的更多信息，我可以推荐此 博客之类的资源 。 还有很多免费的免费资源可用于继续学习AI，例如 AI元素 课程。 另外，如果还有其他问题或建议，请随时通过datascience@prosus.com与我们联系。 祝您学习愉快！