Some people would prefer to trade the cost of dealing with more false alarms for enhanced privacy. But many people will be uncomfortable with this. In the alarm case, having cameras all over the house may be the best way of determining the presence of an unknown intruder. Such judgment can change the nature of the prediction machine you deploy. 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. This will depend on the situation and requires human judgment. 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. 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. 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. 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. A prediction machine can potentially tell you this - after all, an alarm with a simple movement sensor is already a sort of prediction machine. In the alarm case, you want to know whether an alarm is caused by an unknown person or not (true versus false alarm). We provide an example for the security alarm case.įirst, you specify what you are trying to predict. 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. However, always taking an action in response to an alarm signal means that when a threat is indeed present, the security company responds. 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. That is, something other than an unknown intruder (threat) triggered it. Over 97% of the time that a home security alarm goes off, it’s a false alarm. (It’s a real example, based on a product that Peloton is commercializing, called RSPNDR.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. Each space on the canvas contains one of the requirements for machine-assisted decision making, beginning with a prediction. 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. But how do you think through what it would take to incorporate a prediction machine into your decision-making process? And now machines can do it.īetter predictions matter when you make decisions in the face of uncertainty, as every business does, constantly. 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. Prediction is about using information you have to generate information you don’t have. 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?). AI makes prediction better, faster, and cheaper. We start with a simple insight: Recent developments in AI are about lowering the cost of prediction. 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. Much less has been written about how, exactly, companies should get started with it. 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.
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