Reducing Inspection Errors, Costs, and Risk with Hybrid AI
Jonathan Hou, President, Pleora Technologies
Manufacturers and brand owners navigating through the complexities of AI are left with a few key questions. How can AI reduce errors and automate manual tasks? Is algorithm training expensive or complicated? Can I keep existing infrastructure and processes? This session will discuss hybrid AI, a unique approach that merges the best of computer vision and machine learning to address these key concerns and allow manufacturers to deploy advanced end-to-end quality inspection. We will look at how AI addresses critical quality challenges in the consumer goods, print & packaging, and parts manufacturing markets to increase profitability and reduce risk. We will then outline hybrid AI algorithm training and deployment strategies for established automated inspection systems, and opportunities to add decision-support to manual tasks. We’ll close the session with a case study on how a consumer brand is deploying hybrid AI today while also preparing for more advanced Industry 4.0 and IIoT automation.
Jonathan Hou is President of Pleora Technologies, a leading supplier of AI, embedded, and sensor networking solutions for the industrial automation, security & defense, and medical imaging markets. In this role, Jonathan oversees Pleora’s research & development efforts and leads the company’s long-term technology vision. Jonathan joined Pleora as Chief Technology Officer in 2018. Previously he was Director of Technology with GlobalVision, where he helped develop new automated quality inspection solutions for print inspection applications. He has held positions in software & engineering management, applications engineering, and software development in the machine vision, video, graphics and networking industries. Jonathan has a Bachelor of Applied Sciences – Computer Engineering from the University of Waterloo in Waterloo, Canada, and a Master of Engineering from McGill University in Montreal, Canada.