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Landing AI launches new visual inspection platform for manufacturers
As companies manufacturer goods, human inspectors review them for defects. Think of a scratch on smartphone glass or a weakness in raw steel that could have an impact downstream when it gets turned into something else. Landing AI, the company started by former Google and Baidu AI guru Andrew Ng, wants to use AI technology to identify these defects, and today the company launched a new visual inspection platform called LandingLens.
“We’re announcing LandingLens, which is an end-to-end visual inspection platform to help manufacturers build and deploy visual inspection systems [using AI],” Ng told TechCrunch.
He says that company’s goal is to bring AI to manufacturing companies, but he couldn’t simply repackage what he he had learned at Google and Baidu, partly because it involved a different set of consumer use cases, and partly because there is just much less data to work with in a manufacturing setting.
Adding to the degree of difficulty here, each setting is unique, and there is no standard playbook you can necessarily apply across each vertical. This meant Landing AI had to come up with a general tool kit that each company could use for the unique requirements of their manufacturing process.
Ng says to put this advanced technology into the hands of these customers and apply AI to visual inspection, his company has created a visual interface where companies can work through a defined process to train models to understand each customer’s inspection needs.
The way it works is you take pictures of what a good finished product looks like, and what a defective product could look like. It’s not as easy as it might sound because human experts can disagree over what constitutes a defect.
The manufacturer creates what’s called a defect book where the inspector experts work together to determine what that defect looks like via a picture, and resolve disagreements when they happen. All this is done through the LandingLens interface.
Once inspectors have agreed upon a set of labels, they can begin iterating on a model in the Model Iteration Module where the company can train and run models to get to a state of agreed upon success where the AI is picking up the defects on a regular basis. As customers run these experiments, the software generates a report on the state of the model, and customers can refine the models as needed based on the information in the report.
Ng says that his company is trying to bring in sophisticated software to help solve a big problem for manufacturing customers. “The bottleneck [for them] is building the deep learning algorithm, really the machine learning software. They can take the picture and render judgment as to whether this part is okay, or whether it is defective, and that’s what our platform helps with,” he said.
He thinks this technology could ultimately help recast how goods are manufactured in the future. “I think deep learning is poised to transform how inspection is done, which is really the key step. Inspection is really the last line of defense against quality defects in manufacturing. So I’m excited to release this platform to help manufacturers do inspections more accurately,” he said.