The Doppler Quarterly Summer 2017 - Page 41

1. Binary predictions answer questions that elicit a yes or no response. For example, “Does the order contain data that the artificial intelligence applica- tion has previously flagged as fraudulent?” Or, “Based upon data that comes from an AI-enabled recommendation engine, will a customer be likely to buy an ‘up sell’ product?” We leverage more applications for these types of predictions than for the other types, because the responses are far less complex: yes or no. Thus, these types of artificial intelligence use cases often find themselves in typical business processes, such as order processing, credit check systems and recommenda- tion engines used to suggest videos, music or other products to users based upo n gathered data and learned responses. We found that finances and man- ufacturing verticals tend to benefit most from this aspect of artificial intelli- gence (Figure 2). 2. Category predictions involve looking at a data set and then, based upon learned information, placing that information into a particular category. This is useful when very different types of data are being analyzed, and categories need to be applied so the data can be better understood and processed. For instance, insurance companies place different instances of claims in spe- cific categories, based upon what they’ve learned over the years. An example would be to define the likely cause of an accident, even if such information is not a part of the data. Thus, the AI system can make assignments such as “alco- hol likely involved,” “likely fraudulent,” or “likely weather related,” based upon past learning, such as the time of day that the accident occurred, as well as location, the type of damage done, age of driver, etc. Category predictions have many different types of applications, such as when we need to place additional meaning around data, but direct correlation data is not in the existing database. Finance, manufacturing and retail are all verticals that can use this category prediction type of technology. We found that the finance and healthcare verticals can especially benefit from this aspect of artificial intelligence (Figure 2). For instance, a financial user may need to classify transactions into categories, such as “likely fraudulent” or “likely non-compliant.” Or, hospitals may need to categorize test results that come back from the lab. 3. Value predictions are more complex, but also more insightful. They actually tell you quantitatively about likely outcomes from data culled through using learning models to find patterns. Let’s say we want to find out how many units of a product are likely to sell in the next month. This can be good information to know. It can permit tighter man- ufacturing planning. It can tell you if additional revenue needs to be generated to meet quarterly objectives. It can even reduce the travel costs of sales people following up on leads. We found that all verticals can benefit from this aspect of artificial intelligence (see Figure 2), but especially manufacturing, for production optimization, and government, for defense operations such as threat assessments. SUMMER 2017 | THE DOPPLER | 39