The Seven Patterns Of AI
Source | www.forbes.com | Kathleen Walch
From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for AI are many. However, regardless of the application of AI, there is commonality to all these applications. Those who have implemented hundreds or even thousands of AI projects realize that despite all this diversity in application, AI use cases fall into one or more of seven common patterns. The seven patterns are: hyperpersonalization, autonomous systems, predictive analytics and decision support, conversational/human interactions, patterns and anomalies, recognition systems, and goal-driven systems. Any customized approach to AI is going to require its own programming and pattern, but no matter what combination these trends are used in, they all follow their own pretty standard set of rules. These seven patterns are then applied individually or in various combinations depending on the specific solution to which AI Is being applied.
The Hyperpersonalization Pattern: Treat each customer as an individual
The hyperpersonalization pattern is defined as using machine learning to develop a profile of each individual, and then having that profile learn and adapt over time for a wide variety of purposes including displaying relevant content, recommend relevant products, provide personalized recommendations and so on. The objective of this pattern is to treat each individual as an individual.