Solving Quality and Maintenance Problems with AI

Chapter 2. Complementary Learning and Intel Saffron AI

Complementary Learning as the Future of Predictive Quality and Maintenance Solutions

Because none of the types of artificial intelligence (AI) can solve all problems, applying them simultaneously is the key to success. This need for a combined approach is giving rise to cognitive computing as a basis for complementary learning. This is what DARPA’s John Launchbury refers to as the “contextual adaptation systems” in the third wave of AI.

Strengths and weaknesses of different AI approaches are giving rise to complementary learning because solving a challenging problem often requires solving underlying subproblems effectively, which calls for different models or approaches.

To understand how machine learning, deep learning, and cognitive computing-based AI can work together in a predictive quality and maintenance (PQM) solution, it’s important to understand that a comprehensive AI-based PQM solution needs to solve two types of problems: surveillance and prescriptive.

Surveillance use cases involve scenarios in which businesses need to recognize problems by observation. By detecting patterns and alerting businesses, the surveillance approach to AI allows companies to act quickly when something out of the ordinary is detected in their equipment or other assets. For example, manufacturers want to understand what the sensor data coming in from the factory floor via the Internet of Things (IoT) is telling them. In the past, they ...