Recent developments in AI are about lowering the cost of prediction. Better predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. But how do you think through what it would take to incorporate a prediction machine into your decision-making process? In teaching this subject to MBA graduates at the University of Toronto’s Rotman School of Management, the authors have introduced a simple decision-making tool: the AI Canvas. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction. To explain how the AI Canvas works, the authors use an example crafted during one of their AI strategy workshops: home security.

人工智能最新的發展是關于降低預測成本。當你在面對不確定性時做出決策時，更好的預測很重要，就像每個企業都在不斷地做的那樣。但是，你如何考慮將預測機器整合到你的決策過程中需要做些什么?在多倫多大學(University of Toronto)羅特曼管理學院(Rotman School of Management)向MBA畢業生講授這一主題時，兩位作者介紹了一種簡單的決策工具:人工智能畫布(AI Canvas)。畫布上的每個空間都包含機器輔助決策的一個要求，從預測開始。為了解釋AI Canvas是如何工作的，作者使用了一個在AI戰略研討會上制作的例子:家庭安全。

There is no shortage of hot takes regarding the significant impact that artificial intelligence (AI) is going to have on business in the near future. Much less has been written about how, exactly, companies should get started with it. In our research and in our book, we begin by distilling AI down to its very simplest economics, and we offer one approach to taking that first step.

We start with a simple insight: Recent developments in AI are about lowering the cost of prediction. AI makes prediction better, faster, and cheaper. Not only can you more easily predict the future (What’s the weather going to be like next week?), but you can also predict the present (what is the English translation of this Spanish website?). Prediction is about using information you have to generate information you don’t have. Anywhere you have lots of information (data) and want to filter, squeeze, or sort it into insights that will facilitate decision making, prediction will help get that done. And now machines can do it.

Better predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. But how do you think through what it would take to incorporate a prediction machine into your decision-making process?

In teaching this subject to MBA graduates at the University of Toronto’s Rotman School of Management, we have introduced a simple decision-making tool: the AI Canvas. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction.

關于人工智能(AI)在不久的將來將對商業產生的重大影響，熱門話題層出不窮。關于公司應該如何開始使用它的文章卻少得多。在我們的研究和書中，我們首先將人工智能提煉為最簡單的經濟學原理，并提供了邁出第一步的方法。

我們從一個簡單的觀點開始:人工智能最近的發展是關于降低預測成本。人工智能使預測變得更好、更快、更便宜。你不僅可以更容易地預測未來(下周天氣怎么樣?)，而且你也可以預測現在(這個西班牙語網站的英文翻譯是什么?)預測就是利用你擁有的信息來生成你沒有的信息。當你有大量的信息(數據)并想要過濾、壓縮或分類成有助于決策制定的見解時，預測將有助于完成這項工作。現在機器可以做到了。

當你在面對不確定性時做出決定時，更好的預測很重要，就像每個企業都在不斷地做的那樣。但是，你如何考慮將預測機器整合到你的決策過程中需要做些什么?

在多倫多大學(University of Toronto)羅特曼管理學院(Rotman School of Management)向MBA畢業生講授這一課程時，我們引入了一種簡單的決策工具:人工智能畫布(AI Canvas)。畫布上的每個空間都包含機器輔助決策的一個要求，從預測開始。

Use it to think through how Al could help with business decisions.

用它來思考 Al 如何幫助做出業務決策。

The Al Canvas: An Example using Al to Improve Home Security

Al Canvas:一個使用Al改善家庭安全的例子

PREDICTION

What do you need to know to make the decision?

你需要知道什么才能做出決定？

Predict whether an alarm is caused by an unknown person vs. something else (i.e.,true vs. false).

預測警報是由陌生人還是其他東西引起的（即真還是假）。

JUDGMENT

How do you value different outcomes and errors?

你如何評價不同的結果和錯誤?

Compare the cost of responding to a false alarm to the cost of not responding to a true alarm.

比較響應虛假警報的成本與不響應真實警報的成本。

ACTION

What are you trying to do?

你想做什么?

Dispatch a security response or not when an alarm is triggered.

觸發警報時是否發送安全響應。

OUTCOME

What are your metrics for task success?

你衡量任務成功的標準是什么?

Observe whether the action taken in response to the triggered alarm was correct.

觀察針對觸發警報所采取的措施是否正確。

INPUT

What data do you need to run the predictive algorithm?

運行預測算法需要什么樣的數據?

Sensor in puts from movement, heat, camera, and contextual data at each point in time when the alarm is on;these data are used to operate the Al.

傳感器輸入從運動，熱量，攝像機和上下文數據在每個時間點的警報，這些數據用于操作Al。

TRAINING

What data do you need to train the predictive algorithm?

訓練預測算法需要哪些數據?

Historical sensor data matched with historical outcome data(actual intruder vs. false alarm);these data are used to train the Al before deployed.

歷史傳感器數據與歷史結果數據相匹配(實際入侵者vs.誤報);這些數據用于在部署之前訓練 AI。

FEEDBACK

How can you use the outcomes to improve the algorithm?

如何使用結果來改進算法?

Sensor data matched with data collected from outcomes (verified intruders vs. verified false alarms); these data are used to update the model,continuously improving the Al while it is operating.

傳感器數據與從結果中收集的數據相匹配(經過驗證的入侵者vs.經過驗證的誤報);利用這些數據對模型進行更新，在模型運行過程中不斷改進AI。

To explain how the AI Canvas works, we’ll use an example crafted during one of our AI strategy workshops by Craig Campbell, CEO of Peloton Innovations, a venture tackling the security industry with AI. (It’s a real example, based on a product that Peloton is commercializing, called RSPNDR.ai.)

Over 97% of the time that a home security alarm goes off, it’s a false alarm. That is, something other than an unknown intruder (threat) triggered it. This requires security companies to make a decision as to what to do: Dispatch police or a guard? Phone the homeowner? Ignore it? If the security company decides to take action, more than 90 out of 100 times, it will turn out that the action was wasted. However, always taking an action in response to an alarm signal means that when a threat is indeed present, the security company responds.

How can you decide whether employing a prediction machine will improve matters? The AI Canvas is a simple tool that helps you organize what you need to know into seven categories in order to systematically make that assessment. We provide an example for the security alarm case.

為了解釋AI Canvas是如何工作的，我們將使用一個由Peloton Innovations首席執行官Craig Campbell在人工智能戰略研討會上制作的例子，該公司是一家利用人工智能解決安全行業問題的企業。(這是一個真實的例子，它基于Peloton正在商業化的一款名為RSPNDR.ai的產品。)

超超過 97% 的家庭安全警報響起是誤報。也就是說，不是未知的入侵者（威脅）觸發了它。這就需要安保公司做出決定:派遣警察還是警衛?給房主打電話嗎?忽略它嗎?如果安保公司決定采取行動，100次中有90次以上，結果是行動白白浪費了。然而，總是對警報信號采取行動意味著當威脅確實存在時，安全公司會做出反應。

你如何決定使用預測機器是否會改善情況?AI Canvas是一個簡單的工具，它可以幫助你將你需要知道的內容組織成7個類別，以便系統地進行評估。本文以安防報警案例為例。

First, you specify what you are trying to predict. In the alarm case, you want to know whether an alarm is caused by an unknown person or not (true versus false alarm). A prediction machine can potentially tell you this — after all, an alarm with a simple movement sensor is already a sort of prediction machine. With machine learning, you can take a richer range of sensor inputs to determine what you really want to predict: whether the movement was caused specifically by an unknown person. With the right sensors — say, a camera in the home to identify known faces or pets, a door key that recognizes when someone is present, and so on — today’s AI techniques can provide a more nuanced prediction. The prediction is no longer “movement = alarm” but, for example, “movement + unrecognized face = alarm.” This more sophisticated prediction reduces the number of false alarms, making the decision to send a response, as opposed to trying to contact the owner first, an easier one.

首先，您指定您試圖要預測的內容。在警報的情況下，您想知道警報是否由未知人員引起的(真警報與假警報)。一個預測機器可能會告訴你這一點——畢竟，一個帶有簡單運動傳感器的警報已經是一種預測機器了。通過機器學習，您可以采用更豐富的傳感器輸入來確定您真正想要預測的內容：運動是否是由不未知的人專門引起的。有了合適的傳感器——比如，在家里安裝一個可以識別已知面孔或寵物的攝像頭，一把可以識別某人何時在場的門鑰匙等等——今天的人工智能技術可以提供更細微的預測。預測不再是“運動=警報”，而是，例如，“運動+無法識別的面孔=警報”。這種更復雜的預測減少了誤報的數量，使發送響應的決定比嘗試首先聯系所有者更容易。

No prediction is 100% accurate. So, in order to determine the value of investing in better prediction, you need to know the cost of a false alarm, as compared with the cost of dismissing an alarm when it is true. This will depend on the situation and requires human judgment. How costly is a response phone call to verify what is happening? How expensive is it to dispatch a security guard in response to an alarm? How much is it worth to respond quickly? How costly is it to not respond if it turns out that there was an intruder in the home? There are many factors to consider; determining their relative weights requires judgment.

沒有預測是100%準確的。因此，為了確定投資于更好的預測的價值，您需要知道誤報的成本，以及在警報為真時解除警報的成本相比。這將取決于情況并需要人為判斷。一個驗證正在發生的事情的響應電話的成本是多少?在接到警報后派遣一名保安要花多少錢?快速響應值多少錢?如果事實證明家里有入侵者，不回應的代價有多大？有很多因素需要考慮；確定它們的相對權重需要判斷。

Such judgment can change the nature of the prediction machine you deploy. In the alarm case, having cameras all over the house may be the best way of determining the presence of an unknown intruder. But many people will be uncomfortable with this. Some people would prefer to trade the cost of dealing with more false alarms for enhanced privacy. Judgment sometimes requires determining the relative value of factors that are difficult to quantify and thus compare. While the cost of false alarms may be easy to quantify, the value of privacy is not.

Next, you identify the action that is dependent on the predictions generated. This may be a simple “dispatch/don’t dispatch” decision, or it may be more nuanced. Perhaps the options for action include not just dispatching someone but also enabling immediate remote monitoring of who is in the home or some form of contact with the home owner.

這種判斷可以改變您部署的預測機器的性質。在報警的情況下，在房子里到處安裝攝像頭可能是確定未知入侵者存在的最好方法。但是很多人會對此感到不舒服。有些人寧愿用處理更多誤報的成本來換取增強的隱私。判斷有時需要確定難以量化和比較的因素的相對價值。雖然誤報的成本可能很容易量化，但隱私的價值卻并非如此。

接下來，您將識別依賴于生成的預測的操作。這可能是一個簡單的“派遣/不派遣”決定，或者可能更細微。也許行動的選擇不僅包括派遣人員，還包括即時遠程監控誰在家中，或與房主進行某種形式的聯系。

An action leads to an outcome. For example, the security company dispatched a security guard (action), and the guard discovered an intruder (outcome). In other words, looking back, we are able to see for each decision whether the right response occurred. Knowing this is important for evaluating whether there is scope to improve predictions over time. If you do not know what outcome you want, improvement is difficult, if not impossible.

一個行動導致一個結果。例如，保安公司派遣了一個保安人員(動作)，而該保安發現了一個入侵者(結果)。換句話說，回顧過去，我們能夠看到每一個決定是否發生了正確的反應。了了解這一點對于評估隨著時間的推移是否有改進預測的空間很重要。如果你不知道你想要什么結果，改進是困難的，如果不是不可能的話。

The top row of the canvas — prediction, judgment, action, and outcome — describes the critical aspects of a decision. On the bottom row of the canvas are three final considerations. They all relate to data. To generate a useful prediction, you need to know what is going on at the time a decision needs to be made — in this case, when an alarm is triggered. In our example, this includes motion data and image data collected at the home in real time. That is your basic input data.

畫布的最上面一行——預測、判斷、行動和結果——描述了決策的關鍵方面。在畫布的最后一排是最后三個考慮因素。它們都與數據有關。為了生成有用的預測，您需要知道在需要做出決策時(在本例中是在觸發警報時)發生了什么。在我們的例子中，這包括在家中實時收集的運動數據和圖像數據。這是基本的輸入數據。

But to develop the prediction machine in the first place, you need to train a machine learning model. Training data matches historical sensor data with prior outcomes to calibrate the algorithms at the heart of the prediction machine. In this case, imagine a giant spreadsheet where each row is a time the alarm went off, whether there was in fact an intruder, and a bunch of other data like time of day and location. The richer and more varied that training data, the better your predictions will be out of the gate. If that data is not available, then you might have to deploy a mediocre prediction machine and wait for it to improve over time.

但是首先要開發預測機器，你需要訓練一個機器學習模型。訓練數據將歷史傳感器數據與之前的結果進行匹配，以校準預測機器的核心算法。在這種情況下，想象一個巨大的電子表格，其中每一行是警報響起的時間，是否真的有入侵者，以及一堆其他數據，如一天中的時間和位置。訓練數據越豐富、越多樣化，你的預測效果就越好。如果該數據不可用，那么您可能不得不部署一個普通的預測機器，并等待它隨著時間的推移而改進。

Those improvements come from feedback data. This is data that you collect when the prediction machine is operating in real situations. Feedback data is often generated from a richer set of environments than training data. In our example, you may correlate outcomes with data collected from sensors through windows, which affect how movements are detected and how cameras capture a facial image — perhaps more realistic than the data used for training. So, you can improve the accuracy of predictions further with continual training using feedback data. Sometimes feedback data will be tailored to an individual home. Other times, it might aggregate data from many homes.

這些改進來自于反饋數據。這是預測機在真實情況下運行時所收集的數據。反饋數據通常是從比訓練數據更豐富的環境中生成的。在我們的例子中，您可以將結果與通過窗戶從傳感器收集的數據相關聯，這些數據影響著如何檢測運動以及相機如何捕捉面部圖像——可能比用于訓練的數據更真實。因此，您可以通過使用反饋數據的持續訓練進一步提高預測的準確性。有時反饋數據將針對個人家庭進行定制。其他時候，它可能會收集許多家庭的數據。

Clarifying these seven factors for each critical decision throughout your organization will help you get started on identifying opportunities for AIs to either reduce costs or enhance performance. Here we discussed a decision associated with a specific situation. To get started with AI, your challenge is to identify the key decisions in your organization where the outcome hinges on uncertainty. Filling out the AI Canvas won’t tell you whether you should make your own AI or buy one from a vendor, but it will help you clarify what the AI will contribute (the prediction), how it will interface with humans (judgment), how it will be used to influence decisions (action), how you will measure success (outcome), and the types of data that will be required to train, operate, and improve the AI.

The potential is enormous. For example, alarms communicate predictions to a remote agent. Part of the reason for this approach is that there are so many false signals. But just think: If our prediction machine became so good that there were no false alarms, then is dispatch still the right response? One can imagine alternative responses, such as an on-site intruder capture system (as in cartoons!), which could be more feasible with significantly more-accurate and high-fidelity predictions. More generally, better predictions will create opportunities for entirely new ways to approach security, potentially predicting the intent of intruders before they even enter.

明確整個組織中每個關鍵決策的這七個因素，將有助于您開始識別AI降低成本或提高性能的機會。這里我們討論了一個與特定情況相關的決策。要開始使用AI，您面臨的挑戰是確定您的組織中的關鍵決策，而這些決策的結果取決于不確定性。填寫AI Canvas上不會告訴你您是應該制作自己的 AI 還是從供應商處購買 AI，但它會幫助你闡明 AI 將做出什么貢獻（預測），它將如何與人類交互（判斷），它將如何用于影響決策（行動），如何衡量成功（結果），以及訓練、操作和改進 AI 所需的數據類型。

潛力是巨大的。例如，警報將預測信息傳遞給遠程代理。這種方法的部分原因是有太多的錯誤信號。但請想一想:如果我們的預測機器變得如此強大，以至于沒有誤報，那么調度仍然是正確的響應嗎?可以想象另一種響應方式，比如現場入侵者捕獲系統(就像卡通里的那樣!)，如果預測更加準確和高保真，這可能更可行。更一般地說，更更好的預測將為接近安全的全新方法創造機會，甚至可能在入侵者進入之前預測他們的意圖。

Ajay Agrawal is the Geoffrey Taber Chair in Entrepreneurship and Innovation at the University of Toronto’s Rotman School of Management. He is the founder of the Creative Destruction Lab, co-founder of The Next AI, and co-founder of Kindred. He is the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

Joshua Gans is the Jeffrey S. Skoll Chair in Technical Innovation and Entrepreneurship at the Rotman School of Management, University of Toronto, and the chief economist at the Creative Destruction Lab. He is the author of The Disruption Dilemma (MIT Press, March 2016) and a co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

Avi Goldfarb is the Rotman Chair in Artificial Intelligence and Healthcare at the Rotman School of Management, University of Toronto. He is also the chief data scientist at the Creative Destruction Lab and the co-author of Prediction Machines: The Simple Economics of Artificial Intelligence (Harvard Business Review Press, April 2018).

Ajay Agrawal是多倫多大學羅特曼管理學院創業與創新領域的Geoffrey Taber主席。他是Creative Destruction Lab的創始人，The Next AI的聯合創始人，以及Kindred的聯合創始人。他是《預測機器:人工智能的簡單經濟學》(哈佛商業評論出版社，2018年4月)的合著者。

本文作者喬舒亞?甘斯是多倫多大學羅特曼管理學院(Rotman School of Management, University of Toronto)技術創新與創業杰弗里?s?斯科爾(Jeffrey S. Skoll)主席，同時也是創造性破壞實驗室(Creative Destruction Lab)的首席經濟學家。他是《顛覆困境》(the Disruption Dilemma)一書的作者(麻省理工學院出版社，2016年3月)，《預測機器:人工智能的簡單經濟學》(Harvard Business Review Press, 2018年4月)的合著者。

阿維·戈德法布(Avi Goldfarb)是多倫多大學(University of Toronto)羅特曼管理學院(Rotman School of Management)人工智能和醫療保健領域的羅特曼主席。他還是創造性破壞實驗室(Creative Destruction Lab)的首席數據科學家，《預測機器:人工智能的簡單經濟學》(Prediction Machines: the Simple Economics of Artificial Intelligence，哈佛商業評論出版社，2018年4月)的合著者。