Role of Augmented Intelligence in Quality Inspection – Metrology and Quality News

Augmented intelligence and artificial intelligence are both terms that are often used interchangeably. However, the difference between the two has important implications for our understanding and expectations of technology. Here explains Dr. Gilad Wainreb, leader of the algorithms team at software company Lean AI, sharing his understanding of the concept of augmented intelligence in quality inspection and computer vision.

As technology advances and the use of deep learning in the field of quality assurance becomes more common, we often find ourselves on the train of the latest buzzword. For example, a few years ago there was a lot of hype surrounding the term “autonomous” machine vision systems, but the promise of full autonomy never materialised.

While the difference between augmented intelligence and artificial intelligence may be subtle, I believe it’s important to be precise with the language we use, especially when making promises to our customers. Of course this is still a work in progress and we need to update our lexicon to reflect the latest technologies, but I think some definition unraveling is a worthwhile exercise.

Comparison of artificial and augmented intelligence

The application of artificial intelligence has been extensively researched in numerous fields including quality inspection. Augmented Intelligence is relatively little researched.

Augmented intelligence is similar to artificial intelligence in that it uses machine learning algorithms to improve error detection. However, instead of replacing human intelligence, augmented intelligence aims to build on it. So there is a methodological difference. Both artificial and augmented intelligence share a common goal of improving quality checking, but these machine learning techniques have different methods of achieving the intended results.

Advanced intelligence in quality inspection

The key point about augmented intelligence is that it retains an important role in human intelligence. Another way to describe this approach refers to it as semi-supervised. With human supervision and input, the model augments human intelligence but does not completely replace it.

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More precisely, there is a role for the quality manager within the framework of the visual inspection. While artificial intelligence might try to replace it entirely, augmented intelligence requires input from the quality manager. They provide the feedback to the model that ensures optimization and continuous learning.

By maintaining the Quality Manager’s role in this way, their unique knowledge of the product is leveraged. It also gives them the necessary guarantee that the deep learning system ultimately remains under their control. Artificial intelligence is often left with a black box, a system that is opaque to the customer, so when mistakes are common they cannot understand why.

Although involving them in the process is key to ensuring a workable solution, another benefit of a semi-supervised model is the reduction in the time required for training. The model needs the QA manager’s feedback, but can learn with the minimal number of examples. It only requires the assistance of the QA manager in labeling representative samples.

With machine learning and human insight working side by side, the process of delivering a workable solution is faster and easier to develop. While developing fully autonomous systems designed to replace human labor took months of work, a semi-supervised system that delivers augmented intelligence is much faster to deploy.

In many industries, the terms artificial intelligence and augmented intelligence are used interchangeably. However, there is a subtle difference between the two. Augmented intelligence proponents believe that deep learning systems are at their best when they augment human intelligence, rather than attempting and failing to fully replace it. As we review quality, we begin to see how this approach offers a viable solution and greater return on investment.

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