X-Rayted Files: X-Ray and AI—A Match Made In Heaven

Will I lose my job to artificial intelligence (AI)? That’s a question we often get when giving presentations about AI. Gartner’s Technology Hype Cycle puts many of the AI and machine learning (ML) applications around the “peak of inflated expectations,” or headed into the “trough of disillusionment.” Meanwhile, very few have made it onto the “slope of enlightenment,” let alone the “plateau of productivity” (Figure 1).

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We have been working with AI for a while now, where we’ve seen real applications and successes in X-ray inspection. We’ve also seen a lot of failures. AI is changing the way we think about X-ray inspection. Things we would never dream of doing just a few years ago are now a reality by combining AI and X-ray inspection. We have experienced a wide range of real-life cases, where our team of scientists has used AI to solve the most challenging applications in X-ray inspection. Beyond X-ray inspection, AI is forever changing the way in which we manufacture and inspect anything.

One reason why AI is in the “trough of disillusionment” is the ridiculously high peak of inflated expectations. Science fiction has painted a picture of AI systems that are more powerful than human intelligence, capable of running complex ecosystems and even entire societies. The truth about AI is quite different and gives us a reason not to fear, but to embrace AI.

AI is at its best is when it is applied to tasks that exhibit certain traits. These are typically narrow in their application but substantial in terms of the data utilized, particularly in the learning process. To put that into context, the Tesla fleet delivers driving data from every connected vehicle in use. This data is more than one million hours of driving, which is greater than a single person would experience in a lifetime.

Another example is the creation of a supercomputer to beat the world masters at the board game Go, which most people agree is the most difficult game to play due to its incredibly high number of possible combinations. The AI system developed by DeepMind Technologies (now Google) could assimilate thousands of games and learn from them, resulting in an amazing level of narrow intelligence that could outplay even the most experienced human. This is entirely different from human intelligence, which is much broader and complete and, contrary to sci-fi movies, broad and general intelligence in AI is a very far away.

Narrow, Broad, and General AI

Narrow AI is when large sets of data are used to teach an AI system or a computer a simple task. The computer will seem extremely intelligent in its specialist subject, but dumb in just about everything else. This can, and has, produced amazing results in tasks like early disease diagnosis or predicting trends in supply and demand.

Broad intelligence is less simple and requires the AI to perform a set of tasks, learning broader skills but still within set parameters. An example of a broader system might be Amazon’s Alexa or Apple’s Siri, with thousands of skills within limited parameters.

General intelligence is much more human and, currently, is completely out of reach for AI. This level of cognitive thinking is the stuff of future dystopias, run by computers that manage humanity with a rigorous set of objectives, resulting in decisions that seem cold and heartless.

The Application of AI in Inspection

Inspection is a task that relies on viewing multiple images and analyzing the data to make decisions or to provide feedback to other parts of the manufacturing ecosystem. This is where AI can really thrive and deliver! It is a quick and relentless learner that can process data faster and more accurately than any human.

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Take, for example, finished goods inspection of a medical-surgical tray. Once the tray is sealed and sterilized, there is no way of seeing inside to ensure that every part is present and correct (Figure 2). In this instance, an X-ray image can be used to see the contents of the package, and AI can be used to interrogate that image and ascertain if all parts or present and correct (Figure 3).

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The result is a fast, intelligent process that ensures that no partially complete packs leave production. This can be applied to just about any packaged product. This is also a simple process for teaching the AI what a good pack looks like and what should be in that pack before asking it to rapidly check every pack for those parts.

Beyond these narrow applications, we can teach our AI systems to do more than one task, broadening their intelligence. This broader level of AI or expertise starts to make the system behave more like a human. As we add more skills, this broader AI system will operate with greater speed and consistency, becoming more like a superhuman inspector.

In other words, given the current limitations of AI, your job is safe—for now.

Dr. Bill Cardoso is CEO of Creative Electron.

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2020

X-Rayted Files: X-Ray and AI—A Match Made In Heaven

02-05-2020

Dr. Bill Cardoso has been working with AI for a while now and seen real application and success in X-ray inspection, as well as failures. In this column, he shares how AI is changing the way we think about X-ray inspection.

View Story

X-Rayted Files: When SMT Lines Develop Line Conscience

01-22-2020

Bill Cardoso explores the history of Henry Ford's assembly line, how this may be the end of the automation era, and the future of autonomous systems, AI, machine learning, etc.

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Back

2019

X-Rayted Files: X-Ray and AI—A Match Made In Heaven

02-05-2020

Dr. Bill Cardoso has been working with AI for a while now and seen real application and success in X-ray inspection, as well as failures. In this column, he shares how AI is changing the way we think about X-ray inspection.

View Story

X-Rayted Files: When SMT Lines Develop Line Conscience

01-22-2020

Bill Cardoso explores the history of Henry Ford's assembly line, how this may be the end of the automation era, and the future of autonomous systems, AI, machine learning, etc.

View Story
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