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The Seven Patterns of AI

From autonomous vehicles, predictive analytics applications, facial recognition, to chatbots, virtual assistants, cognitive automation, and fraud detection, the use cases for Artificial Intelligence (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.

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While AI is still in the early majority phase of adoption, it’s clear that the identification and use of these patterns will help organizations realize their AI project goals more quickly, with less re-inventing of the wheel, and with much better chances of success. These patterns of AI applications are also supported by AI World Society (AIWS), which always promote AI technology by collaborating among corporations, think tanks, universities, non-profits, and other entities that share its commitment to the constructive and development of AI.