人工智能如何为任何业务提供支持

empower /ɪmˈpaʊə(r)/ vt. 授权；给 (某人) ...的权力；使控制局势；增加 (某人的) 自主权

When I think about the rise of AI, I’m reminded by the rise of literacy. A few hundred years ago, many people in society thought that maybe not everyone needed to be able to read and write. Back then, many people were tending fields or herding sheep, so maybe there was less need for written communication. And all that was needed was for the high priests and priestesses and monks to be able to read the Holy Book, and the rest of us could just go to the temple or church or the holy building and sit and listen to the high priest and priestesses read to us. Fortunately, it was since figured out that we can build a much richer society if lots of people can read and write.
当我想到 AI (Artificial intelligence) 的崛起之时，我联想了读写能力的崛起。几百年前，社会上的很多人觉得也许不是每个人都得会读会写。那时候，很多人从事农业或者牧羊，对书面交流的需求没有那么多。只有主教和僧侣需要读得懂《圣经》和最高经典，其他人只要去寺庙、教堂或者圣所坐等主教读给我们听就行了。幸运的是，人们后来发现如果很多人能读能写，我们的社会会富裕得多。

Today, AI is in the hands of the high priests and priestesses. These are the highly skilled AI engineers, many of whom work in the big tech companies. And most people have access only to the AI that they build for them. I think that we can build a much richer society if we can enable everyone to help to write the future. But why is AI largely concentrated in the big tech companies? Because many of these AI projects have been expensive to build.
如今，AI 被掌握在“主教”手中。这些主教就是那些技术高超的 AI 工程师，其中很多就职于科技巨头公司。很多人只能接触到为他们设计的 AI。我认为，如果我们能让每个人参与谱写未来，我们就能创造一个更富裕的社会。但是为什么大部分 AI 技术都集中在科技巨头手中呢？因为开发这些 AI 项目太贵了。

They may require dozens of highly skilled engineers, and they may cost millions or tens of millions of dollars to build an AI system. And the large tech companies, particularly the ones with hundreds of millions or even billions of users, have been better than anyone else at making these investments pay off because, for them, a one-size-fits-all AI system, such as one that improves web search or that recommends better products for online shopping, can be applied to [these] very large numbers of users to generate a massive amount of revenue. But this recipe for AI does not work once you go outside the tech and internet sectors to other places where, for the most part, there are hardly any projects that apply to 100 million people or that generate comparable economics.
这些项目需要一大群技术高超的工程师，要开发一个 AI 系统可能要花上几百万几千万美元。这些大型科技公司，尤其是手握几亿几十亿用户的公司，最擅长套回这些投入，因为对于它们来说，一个普适的 AI 系统，比如优化搜索引擎或者为网购推荐更佳商品的系统，可以直接适用于庞大的用户群体，产生巨额收益。但是一旦你走出科技互联网行业，去向别的领域，这个 AI 的秘方可能就不会奏效，因为在大多数情况下，几乎没有一个项目可以覆盖一亿人，或产生相当的经济效益。

revenue /ˈrevənjuː/ n. 收入；收益；财政收入；税收收入

Let me illustrate an example. Many weekends, I drive a few minutes from my house to a local pizza store to buy a slice of Hawaiian pizza from the gentleman that owns this pizza store. And his pizza is great, but he always has a lot of cold pizzas sitting around, and every weekend some different flavor of pizza is out of stock. But when I watch him operate his store, I get excited, because by selling pizza, he is generating data. And this is data that he can take advantage of if he had access to AI.
我来举一个例子。我总会在周末从家里开车去当地一家披萨店向店主买一块夏威夷披萨。他的披萨很不错，但是总是有一大堆披萨滞销到冷掉，每个周末都会有几个口味的披萨缺货。但是当我看着他运营他的小店的时候，我激动万分，因为在他卖披萨的过程中，也产生了数据。如果他能用上 AI，就可以从这些数据中获益。

AI systems are good at spotting patterns when given access to the right data, and perhaps an AI system could spot if Mediterranean pizzas sell really well on a Friday night, maybe it could suggest to him to make more of it on a Friday afternoon. Now you might say to me, “Hey, Andrew, this is a small pizza store. What’s the big deal?” And I say, to the gentleman that owns this pizza store, something that could help him improve his revenues by a few thousand dollars a year, that will be a huge deal to him. I know that there is a lot of hype about AI’s need for massive data sets, and having more data does help. But contrary to the hype, AI can often work just fine even on modest amounts of data, such as the data generated by a single pizza store.
如果输入了合适的数据，AI 系统就会很善于识别规律，也许能有一个 AI 系统识别出周五晚上地中海披萨卖得特别好，也许这就能告诉他周五下午多做一点地中海披萨。你有可能想这么对我说：“嘿，Andrew，这只是个小披萨店。有什么了不起的？”而我想说，对于店主来说，如果有什么可以帮他每年多赚几千美元，那就很了不起了。我知道，人们普遍认为 AI 需要大量数据集，有了更多数据确实会有帮助。但是如果没有大量数据，AI 通常也可以在只有少量数据的情况下正常运作，比如一家披萨店产生的数据。

So the real problem is not that there isn’t enough data from the pizza store. The real problem is that the small pizza store could never serve enough customers to justify the cost of hiring an AI team. I know that in the United States there are about half a million independent restaurants. And collectively, these restaurants do serve tens of millions of customers. But every restaurant is different with a different menu, different customers, different ways of recording sales that no one-size-fits-all AI would work for all of them. What would it be like if we could enable small businesses and especially local businesses to use AI?
真正的问题不是披萨店没有足够的数据。真正的问题是这小小的披萨店没有足够的客源平衡雇佣一组 AI 人员的支出。我知道美国有大约 50 万家独立餐厅。这些餐厅总计服务了几亿顾客。但是每一家餐厅都是不同的，有着不同的菜单，不同的顾客，不同的记账方式，没有一个通用的 AI 系统可以适用于全部的餐厅。如果我们可以让小型企业尤其是本土企业都能用上 AI，会怎么样呢？

Let’s take a look at what it might look like at a company that makes and sells T-shirts. I would love if an accountant working for the T-shirt company can use AI for demand forecasting. Say, figure out what funny memes to prints on T-shirts that would drive sales, by looking at what’s trending on social media. Or for product placement, why can’t a front-of-store manager take pictures of what the store looks like and show it to an AI and have an AI recommend where to place products to improve sales? Supply chain. Can an AI recommend to a buyer whether or not they should pay 20 dollars per yard for a piece of fabric now, or if they should keep looking because they might be able to find it cheaper elsewhere? Or quality control.
我们来看看 AI 应用于一家制造、销售 T 恤的公司会是什么样的情形。如果这家 T 恤公司的会计可以用 AI 预测需求，那就会很不错。比如，通过研究社交媒体上的潮流，锁定一些印在 T 恤上增加销量的好玩表情包。就上架策略而言，门店经理可以拍下店铺情况，提交给 AI，让 AI 推荐商品的摆放位置，提高销量。供应链。AI 是不是可以推荐买家是否应该以 20 美元一码的价格购入一块布料，还是应该货比三家，因为别家的价格有可能会更低廉呢？质量管理。

fabric /ˈfæbrɪk/ n. 织物；(建筑物的) 结构 (如墙、地面、屋顶)；布料

A quality inspector should be able to use AI to automatically scan pictures of the fabric they use to make T-shirts to check if there are any tears or discolorations in the cloth. Today, large tech companies routinely use AI to solve problems like these and to great effect. But a typical T-shirt company or a typical auto mechanic or retailer or school or local farm will be using AI for exactly zero of these applications today.
一名质检员应该能够使用 AI自动扫描 T 恤的面料照片，检查布料是否有裂缝或褪色。如今，AI 已经成为大型科技公司处理此类问题的常规手段，成果显著。但是现在没有一家普通的T 恤公司、普通的汽修店、零售店、学校、本地农场会用 AI 运营。

Every T-shirt maker is sufficiently different from every other T-shirt maker that there is no one-size-fits-all AI that will work for all of them. And in fact, once you go outside the internet and tech sectors in other industries, even large companies such as the pharmaceutical companies, the car makers, the hospitals, also struggle with this. This is the long-tail problem of AI. If you were to take all current and potential AI projects and sort them in decreasing order of value and plot them, you get a graph that looks like this.
每一家 T 恤制造商的情况都是截然不同的，没有一个通用的 AI 系统可以适用于全部商家。其实，如果不看互联网和科技领域，去看一些别的领域，就算是一些大公司，比如医药公司、汽车制造商、医院，都会饱受这个问题的困扰。这就是 AI 的长尾效应。你可以把所有已有和潜在的 AI 项目以价值降序排列后作图，就会得到这样一张图。

Maybe the single most valuable AI system is something that decides what ads to show people on the internet. Maybe the second most valuable is a web search engine, maybe the third most valuable is an online shopping product recommendation system. But when you go to the right of this curve, you then get projects like T-shirt product placement or T-shirt demand forecasting or pizzeria demand forecasting. And each of these is a unique project that needs to be custom-built. Even T-shirt demand forecasting, if it depends on trending memes on social media, is a very different project than pizzeria demand forecasting, if that depends on the pizzeria sales data.
也许最有价值的 AI 系统决定了在网上给人们展示什么广告。也许第二有价值的系统是网络搜索引擎，第三有价值的系统是网购商品推荐系统。但是如果你看向曲线的右侧，就会看到像 T 恤商品陈列、T 恤需求预测和披萨店需求预测这样的项目。每一个这样的项目都需要定制。就算是 T 恤需求预测，如果它由社交媒体上的流行表情包决定，也与披萨店需求预测是两种泾渭分明的项目，披萨店的预测由销售数据决定。

So today there are millions of projects sitting on the tail of this distribution that no one is working on, but whose aggregate value is massive. So how can we enable small businesses and individuals to build AI systems that matter to them? For most of the last few decades, if you wanted to build an AI system, this is what you have to do. You have to write pages and pages of code. And while I would love for everyone to learn to code, and in fact, online education and also offline education are helping more people than ever learn to code, unfortunately, not everyone has the time to do this. But there is an emerging new way to build AI systems that will let more people participate.
如今成千上万的项目就处于这个无人问津的分布长尾上，但是它们的合计价值是不可小觑的。我们该如何让小型企业和个人有能力搭建对他们十分重要的 AI 系统呢？在过去的几十年中，如果你想搭建一个 AI 系统，你需要做这些事。你需要写长篇累牍的代码。虽然我觉得人人都该学写代码，线上和线下教育也确实让学习编程的人数达到了高峰，不幸的是，不是人人都有时间学习编程。但是，我们现在有了一个全新的方式，创造 AI 系统，让更多人参与编程。

Just as pen and paper, which are a vastly superior technology to stone tablet and chisel, were instrumental to widespread literacy, there are emerging new AI development platforms that shift the focus from asking you to write lots of code to asking you to focus on providing data. And this turns out to be much easier for a lot of people to do. Today, there are multiple companies working on platforms like these. Let me illustrate a few of the concepts using one that my team has been building. Take the example of an inspector wanting AI to help detect defects in fabric. An inspector can take pictures of the fabric and upload it to a platform like this, and they can go in to show the AI what tears in the fabric look like by drawing rectangles.
就像纸笔是比石板和凿子先进得多的科技，在普及读写的过程中功不可没，现在也有一些新的 AI 开发平台不再让你写一大堆代码，而是只让你提供数据。这对大规模人群来说更容易实现。现在有很多公司在做这样的平台。我的团队也在做这类平台，我来给大家介绍其中一个。举个例子，检测员需要 AI 的帮助检测布料瑕疵。检测员可以拍下布料的照片，上传到这样的平台上，然后他们可以用矩形做标记，告诉 AI 布料裂缝长什么样。

And they can also go in to show the AI what discoloration on the fabric looks like by drawing rectangles. So these pictures, together with the green and pink rectangles that the inspector’s drawn, are data created by the inspector to explain to AI how to find tears and discoloration. After the AI examines this data, we may find that it has seen enough pictures of tears, but not yet enough pictures of discolorations. This is akin to if a junior inspector had learned to reliably spot tears, but still needs to further hone their judgment about discolorations.
他们也可以通过标记矩形，告诉 AI 布料褪色长什么样。这些图片与检测员标记的绿色和粉色矩形框就是检测员创建的数据，告诉 AI 如何检测裂缝和褪色。AI 检查了数据之后，我们会发现，AI 已经读取了足够的裂缝图片，但是没有足够的褪色图片。这就类似于一个初级检测员已经学会了如何准确地识别裂缝，但是还得再磨练一下对褪色的判断。

So the inspector can go back and take more pictures of discolorations to show to the AI, to help it deepen this understanding. By adjusting the data you give to the AI, you can help the AI get smarter. So an inspector using an accessible platform like this can, in a few hours to a few days, and with purchasing a suitable camera set up, be able to build a custom AI system to detect defects, tears and discolorations in all the fabric being used to make T-shirts throughout the factory. And once again, you may say, “Hey, Andrew, this is one factory. Why is this a big deal?”
这个检测员可以回去再拍几张褪色的照片，提交给 AI，加深它对褪色的理解。通过调整输入 AI 的数据，你可以让 AI 变得更聪明。检测员使用这样容易操作的平台，在几小时至几天内，再采购一套合适的摄影设备，就能在搭建起一个定制化 AI 系统，检测工厂中所有 T 恤面料上的瑕疵、裂缝和褪色情况。你可能又想说：“嘿，安德鲁，这就是一家工厂，有什么了不起的？”

And I say to you, this is a big deal to that inspector whose life this makes easier and equally, this type of technology can empower a baker to use AI to check for the quality of the cakes they’re making, or an organic farmer to check the quality of the vegetables, or a furniture maker to check the quality of the wood they’re using. Platforms like these will probably still need a few more years before they’re easy enough to use for every pizzeria owner. But many of these platforms are coming along, and some of them are getting to be quite useful to someone that is tech savvy today, with just a bit of training.
我想告诉你，对那个减负的检测员来说，这很了不起，同样，这项技术可以让一名烘焙师使用 AI检查手中蛋糕的质量，让一名有机农场主检查蔬菜的质量，让一个家具制造商检查木材原料的质量。这类平台也许还需要一些时间将操作难易度调节至适用于每一个披萨店店主。但是很多平台都在进步，有些平台只需要少量培训，就已经对如今懂技术的人来说非常有帮助了。

But what this means is that, rather than relying on the high priests and priestesses to write AI systems for everyone else, we can start to empower every accountant, every store manager, every buyer and every quality inspector to build their own AI systems. I hope that the pizzeria owner and many other small business owners like him will also take advantage of this technology because AI is creating tremendous wealth and will continue to create tremendous wealth. And it’s only by democratizing access to AI that we can ensure that this wealth is spread far and wide across society. Hundreds of years ago. I think hardly anyone understood the impact that widespread literacy will have.
这也就意味着，我们不需要再依赖于主教为所有人编写 AI 系统，我们的每位会计、每位门店经理、每位买家、每位质检员都有能力搭建自己的 AI 系统。我希望披萨店店主和其他像他这样的小型企业主都可以用上这项技术，因为 AI 创造着巨大财富，也将在未来持续创造巨大财富。只有让人人都有机会用上 AI，我们才能将这样的财富播撒到社会的每个角落。几百年前。我觉得几乎没有人懂得普及读写的重要性。

Today, I think hardly anyone understands the impact that democratizing access to AI will have. Building AI systems has been out of reach for most people, but that does not have to be the case. In the coming era for AI, we’ll empower everyone to build AI systems for themselves, and I think that will be incredibly exciting future. Thank you very much.
我认为现在几乎没有人懂得让每个人有机会用上 AI 的重要性。大多数人没有机会搭建 AI 系统，但是未来不一定会是如此。在接下来的 AI 时代中，我们会让每一个人有能力为自己搭建 AI 系统，我觉得这就是我们振奋人心的未来。谢谢。

References

[1] Yongqiang Cheng, https://yongqiang.blog.csdn.net/