Manufacturers can unearth valuable insights from test data from various sources by employing statistical algorithms and machine learning to establish patterns and predict future outcomes and trends.

In the machine vision marketplace the term “AI” typically refers to deep learning platforms that enable industrial automation and inspection. To appreciate the value proposition of AI in this context, it’s helpful to understand how the technology has evolved over the past several decades.

How you can identify opportunities for AI in your machine vision applications.

The emergence of autonomous mobile robots (AMR), which can independently transport materials around a facility using built-in Lidar scanners and 3D cameras, can make automating tasks faster, less expensive and easier to deploy overall.

In all types of industries, machine learning (ML) tools are finding the needle in the haystack of data, augmenting quality and safety professionals with a new kind of intelligence that can unlock hidden data patterns that are impossible for the human mind or eye to absorb.

Deep learning has become a useful tool in the integration and implementation of machine vision systems in the automation environment.

Artificial intelligence, machine learning, and deep learning are interrelated concepts involved with computer-based learning from vast amounts of data – and then making predictions based on that information. This article will show how these technologies can provide good alternatives to traditional image processing, and how software works to make this happen. 

“There is no reason and no way that a human mind can keep up with an artificial intelligence machine by 2035,” predicted the techno-futurist philosopher Gray Scott. But the truth is more nuanced: automation will create as many opportunities for humans as it reduces. Here’s how manufacturers can greatly enhance their processes—and address the U.S.’s skills shortage.

A study by McKinsey & Company found that AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection.

Artificial intelligence (AI) is one of the most hyped technologies of recent years, and while it promises new cost and process benefits for inspection applications, deployment remains a challenge.