Artificial Intelligence (AI) is a term that is used extremely loosely, and, in many cases, it is not used in the correct way. Due to the rapid technological growth we are experiencing, the majority of companies want to say that they are using AI to better their business, however, they do not have a clear enough understanding of what AI is, how it can be utilized, and most importantly, how to bring it to production and put value back in to their business. I am going to briefly describe 7 patterns of AI which will hopefully allow you to gain a better understanding of how AI could possibly be utilized within your organization.
This pattern is commonly used to create unique profiles for individual people. People commonly think of hypersonalization in the retail, e-commerce, and advertising space. However, there are several additional applications for hyperpersoanlization such as finance, fitness, and healthcare. The whole purpose of hyperpersonalization is to avoid creating generalized groups of people, and instead, create a customized profile for each individual person. In healthcare, that might mean a unique health plan tailored to a patient’s exact needs. If you work at a bank, you might want to target credit card promotions toward people that have a checking account but do not have a credit card. In retail, it might be purchase recommendations and ads that are tailored to a specific buyer. In short, hyperpersonalization allows for more meaningful and impactful interactions with both potential customers, and current customers.
Recognition helps identify objects in unstructured data. A common application for Recognition is computer vision (image recognition). Two common use cases for computer vision are self-driving cars and safety within manufacturing plants. Within the manufacturing industry, computer vision is used to monitor the factory floor, helping to ensure that workers are wearing all required safety equipment. This helps reduce the number of preventable accidents (i.e. making sure all workers are wearing hard hats). Regarding self-driving cars, companies use multiple cameras that acquire images from the environment so that their self-driving cars can detect objects, lane markings, signs and traffic signals to safely drive.
Predictive Analytics & Decision Support
Predictive analytics & decision support is extremely powerful. In its simplest form, predictive analytics helps companies make better decisions. A few ways this can be used is to 1) help forecast your business more effectively, 2) help predict failures and 3) predicting buying behaviors.These are just a few of the ways that predictive analytics can be utilized. Something to keep in mind is you can use predicative analytics without using AI/ML. To be classified as ML, your platform must be adaptive and learn/make better decisions over time. To be clear, predictive analytics does not make decisions for you, it simply provides metrics allowing you to make the best decisions possible.
Autonomous systems have the capability to drastically increase efficiencies, decrease labor costs and save a significant amount of time. By integrating autonomous systems into your business, you can have a machine do many of the remedial tasks that take large amounts of time which will ultimately allow you to focus on forward thinking objectives that will help grow the business.
Pattern & Anomaly Detection
Pattern and anomaly detection are relevant in almost all fields. The point of pattern & anomaly detection is to flag things that stand out. Three common applications for this are fraud detection, risk analysis, and pattern recognition. Machines are not only much quicker than humans when it comes to pattern & anomaly detection, they are also much more accurate, resulting in quicker and more precise results.
Conversational / Human Interaction
Conversational / human interaction is described as machines interacting with humans just as humans would interact with humans. Some use cases for conversational AI are: 1) customer service and 2) data collection. If someone puts a product in their shopping cart but does not purchase it, you might use a bot to follow up and recommend similar products.
Simply put, a goal driven system learns through trial and error and is meant to help you find the optimal solution to a problem.
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