人工智能如何為任何業務提供支持

empower /?m?pa??(r)/ vt. 授權；給 (某人) ...的權力；使控制局勢；增加 (某人的) 自主權

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.
當我想到 AI (Artificial intelligence) 的崛起之時，我聯想了讀寫能力的崛起。幾百年前，社會上的很多人覺得也許不是每個人都得會讀會寫。那時候，很多人從事農業或者牧羊，對書面交流的需求沒有那么多。只有主教和僧侶需要讀得懂《圣經》和最高經典，其他人只要去寺廟、教堂或者圣所坐等主教讀給我們聽就行了。幸運的是，人們后來發現如果很多人能讀能寫，我們的社會會富裕得多。

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。這些主教就是那些技術高超的 AI 工程師，其中很多就職于科技巨頭公司。很多人只能接觸到為他們設計的 AI。我認為，如果我們能讓每個人參與譜寫未來，我們就能創造一個更富裕的社會。但是為什么大部分 AI 技術都集中在科技巨頭手中呢？因為開發這些 AI 項目太貴了。

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.
這些項目需要一大群技術高超的工程師，要開發一個 AI 系統可能要花上幾百萬幾千萬美元。這些大型科技公司，尤其是手握幾億幾十億用戶的公司，最擅長套回這些投入，因為對于它們來說，一個普適的 AI 系統，比如優化搜索引擎或者為網購推薦更佳商品的系統，可以直接適用于龐大的用戶群體，產生巨額收益。但是一旦你走出科技互聯網行業，去向別的領域，這個 AI 的秘方可能就不會奏效，因為在大多數情況下，幾乎沒有一個項目可以覆蓋一億人，或產生相當的經濟效益。

revenue /?rev?nju?/ n. 收入；收益；財政收入；稅收收入

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.
我來舉一個例子。我總會在周末從家里開車去當地一家披薩店向店主買一塊夏威夷披薩。他的披薩很不錯，但是總是有一大堆披薩滯銷到冷掉，每個周末都會有幾個口味的披薩缺貨。但是當我看著他運營他的小店的時候，我激動萬分，因為在他賣披薩的過程中，也產生了數據。如果他能用上 AI，就可以從這些數據中獲益。

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.
如果輸入了合適的數據，AI 系統就會很善于識別規律，也許能有一個 AI 系統識別出周五晚上地中海披薩賣得特別好，也許這就能告訴他周五下午多做一點地中海披薩。你有可能想這么對我說：“嘿，Andrew，這只是個小披薩店。有什么了不起的？”而我想說，對于店主來說，如果有什么可以幫他每年多賺幾千美元，那就很了不起了。我知道，人們普遍認為 AI 需要大量數據集，有了更多數據確實會有幫助。但是如果沒有大量數據，AI 通常也可以在只有少量數據的情況下正常運作，比如一家披薩店產生的數據。

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?
真正的問題不是披薩店沒有足夠的數據。真正的問題是這小小的披薩店沒有足夠的客源平衡雇傭一組 AI 人員的支出。我知道美國有大約 50 萬家獨立餐廳。這些餐廳總計服務了幾億顧客。但是每一家餐廳都是不同的，有著不同的菜單，不同的顧客，不同的記賬方式，沒有一個通用的 AI 系統可以適用于全部的餐廳。如果我們可以讓小型企業尤其是本土企業都能用上 AI，會怎么樣呢？

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.
我們來看看 AI 應用于一家制造、銷售 T 恤的公司會是什么樣的情形。如果這家 T 恤公司的會計可以用 AI 預測需求，那就會很不錯。比如，通過研究社交媒體上的潮流，鎖定一些印在 T 恤上增加銷量的好玩表情包。就上架策略而言，門店經理可以拍下店鋪情況，提交給 AI，讓 AI 推薦商品的擺放位置，提高銷量。供應鏈。AI 是不是可以推薦買家是否應該以 20 美元一碼的價格購入一塊布料，還是應該貨比三家，因為別家的價格有可能會更低廉呢？質量管理。

fabric /?f?br?k/ n. 織物；(建筑物的) 結構 (如墻、地面、屋頂)；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.
一名質檢員應該能夠使用 AI自動掃描 T 恤的面料照片，檢查布料是否有裂縫或褪色。如今，AI 已經成為大型科技公司處理此類問題的常規手段，成果顯著。但是現在沒有一家普通的T 恤公司、普通的汽修店、零售店、學校、本地農場會用 AI 運營。

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 恤制造商的情況都是截然不同的，沒有一個通用的 AI 系統可以適用于全部商家。其實，如果不看互聯網和科技領域，去看一些別的領域，就算是一些大公司，比如醫藥公司、汽車制造商、醫院，都會飽受這個問題的困擾。這就是 AI 的長尾效應。你可以把所有已有和潛在的 AI 項目以價值降序排列后作圖，就會得到這樣一張圖。

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.
也許最有價值的 AI 系統決定了在網上給人們展示什么廣告。也許第二有價值的系統是網絡搜索引擎，第三有價值的系統是網購商品推薦系統。但是如果你看向曲線的右側，就會看到像 T 恤商品陳列、T 恤需求預測和披薩店需求預測這樣的項目。每一個這樣的項目都需要定制。就算是 T 恤需求預測，如果它由社交媒體上的流行表情包決定，也與披薩店需求預測是兩種涇渭分明的項目，披薩店的預測由銷售數據決定。

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.
如今成千上萬的項目就處于這個無人問津的分布長尾上，但是它們的合計價值是不可小覷的。我們該如何讓小型企業和個人有能力搭建對他們十分重要的 AI 系統呢？在過去的幾十年中，如果你想搭建一個 AI 系統，你需要做這些事。你需要寫長篇累牘的代碼。雖然我覺得人人都該學寫代碼，線上和線下教育也確實讓學習編程的人數達到了高峰，不幸的是，不是人人都有時間學習編程。但是，我們現在有了一個全新的方式，創造 AI 系統，讓更多人參與編程。

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.
就像紙筆是比石板和鑿子先進得多的科技，在普及讀寫的過程中功不可沒，現在也有一些新的 AI 開發平臺不再讓你寫一大堆代碼，而是只讓你提供數據。這對大規模人群來說更容易實現。現在有很多公司在做這樣的平臺。我的團隊也在做這類平臺，我來給大家介紹其中一個。舉個例子，檢測員需要 AI 的幫助檢測布料瑕疵。檢測員可以拍下布料的照片，上傳到這樣的平臺上，然后他們可以用矩形做標記，告訴 AI 布料裂縫長什么樣。

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.
他們也可以通過標記矩形，告訴 AI 布料褪色長什么樣。這些圖片與檢測員標記的綠色和粉色矩形框就是檢測員創建的數據，告訴 AI 如何檢測裂縫和褪色。AI 檢查了數據之后，我們會發現，AI 已經讀取了足夠的裂縫圖片，但是沒有足夠的褪色圖片。這就類似于一個初級檢測員已經學會了如何準確地識別裂縫，但是還得再磨練一下對褪色的判斷。

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?”
這個檢測員可以回去再拍幾張褪色的照片，提交給 AI，加深它對褪色的理解。通過調整輸入 AI 的數據，你可以讓 AI 變得更聰明。檢測員使用這樣容易操作的平臺，在幾小時至幾天內，再采購一套合適的攝影設備，就能在搭建起一個定制化 AI 系統，檢測工廠中所有 T 恤面料上的瑕疵、裂縫和褪色情況。你可能又想說：“嘿，安德魯，這就是一家工廠，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.
我想告訴你，對那個減負的檢測員來說，這很了不起，同樣，這項技術可以讓一名烘焙師使用 AI檢查手中蛋糕的質量，讓一名有機農場主檢查蔬菜的質量，讓一個家具制造商檢查木材原料的質量。這類平臺也許還需要一些時間將操作難易度調節至適用于每一個披薩店店主。但是很多平臺都在進步，有些平臺只需要少量培訓，就已經對如今懂技術的人來說非常有幫助了。

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.
這也就意味著，我們不需要再依賴于主教為所有人編寫 AI 系統，我們的每位會計、每位門店經理、每位買家、每位質檢員都有能力搭建自己的 AI 系統。我希望披薩店店主和其他像他這樣的小型企業主都可以用上這項技術，因為 AI 創造著巨大財富，也將在未來持續創造巨大財富。只有讓人人都有機會用上 AI，我們才能將這樣的財富播撒到社會的每個角落。幾百年前。我覺得幾乎沒有人懂得普及讀寫的重要性。

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.
我認為現在幾乎沒有人懂得讓每個人有機會用上 AI 的重要性。大多數人沒有機會搭建 AI 系統，但是未來不一定會是如此。在接下來的 AI 時代中，我們會讓每一個人有能力為自己搭建 AI 系統，我覺得這就是我們振奮人心的未來。謝謝。

References

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