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Do You Have A Vision For AI?

Nov. 19, 2020
AI harnesses tremendous potential for manufacturers, assuming they are ready to go through the process.

As the digital economy continues to intensify, AI has finally moved beyond being a futuristic buzzword. Its applications have rapidly become crucial difference makers for a wide array of organizations willing to take the plunge. As such, the maturing technology is rightfully gaining significant attention. After all, AI offers manufacturers a lot of promise – but that does not mean it’s easy to develop, deploy or scale a model.

According to 2020 State of AI-Based Machine Vision Report recently released by Landing AI and the Association for Advancing Automation (A3), confidence in AI, especially within inspection, continues to climb. Specifically, many companies see AI as the tool for increasing the value of their inspection processes with 55% of respondents saying their overall opinion on the effectiveness of AI is either high or very high.

Digging deeper, 26% of respondents are already using AI whereas 41% note future plans to embrace the growing technology. Of those who are using AI, improved accuracy is the top benefit (62%), followed by being able to automate inspection tasks that had to be performed manually (48%). AI was also cited as particularly effective in dealing with variations in products being inspected (45%).

Having spent a significant amount of time helping manufacturers solve complex visual inspection projects, Landing AI understands what goes into building successful models including the challenges associated with scaling and deploy these machine learning models. This is what ultimately prompted Landing AI to develop and launch a platform to facilitate the process. Landing AI is focused on helping manufacturers realize the promise despite the challenges. “Presenting manufacturers with a platform that just works ensures a much higher chance of success within a shorter development time than was previously possible,” says Landing AI CEO and founder Andrew Ng.

A lot of AI decision making, and AI modeling is by skill and intuition, explains Ng. “It turns out when you train a model, it rarely works the first time. With the platform's workflow, you have the opportunity to go back, and you improve the data or change the hyper-parameters,” he says. “Success is about driving throughs the analytics to help the development process become more systematic to more predictably make progress in improving.”

Of course, one of the biggest challenges, model customization, is an issue the entire field of AI faces. "Each semiconductor maker, auto manufacturer and appliance manufacturer uses a unique AI model. And, of course, creating custom models for 10,000 different factory manufactured products is unrealistic," he says. As such, Landing AI's platform enables manufacturers with very basic knowledge of machine learning to create custom models to inspect aspects the company cares about. Beyond creation, having a platform is really important for the manufacturing company to be empowered to update the model as changes surface.

Also, when scaling from a proof of concept on one or two lines to plant-wide deployment, manufacturers need the systems, processes and monitoring to flag when the lighting changes, or when the color of the substrate changes. “Just because it works in your laptop, doesn't mean it will work on the line,” he says.

“One of the things we've found is that for practical manufacturing deployment settings, the most important thing is to get the data right,” says Ng. “This often means training the model to understand what is actually important about a part and work beyond the type of inconsistency that can occur from one inspector to the next.”

Simply put, training the data is ultimately what improves the AI performance. The Landing AI platform accomplishes this by providing a step-by-step workflow focused on building, debugging, deploying and then monitoring and retaining these AI models.   

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