Adam Burns, Vice President of Network and Edge and Director of EdgeAI Development Tools at Intel, discusses the challenges of using computer vision in manufacturing, the differences in computer vision application in retail and healthcare, customization capabilities and flexibility in computer vision for manufacturing, blending sensor data in computer vision for deeper insights, and tailoring sensor systems and infrastructure in manufacturing.
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Quick takeaways
Customization of predictive models is crucial in manufacturing for improving yield and detecting defects.
Computer vision in retail and healthcare enhances customer/patient experiences by optimizing processes and aiding in disease diagnosis.
Deep dives
Challenges of Computer Vision in Manufacturing
One of the biggest challenges in manufacturing is customizing predictive models to address specific use cases. Each manufacturing deployment requires unique customization based on the desired outcome, whether it's improving yield or detecting defects. Continuous access to data and effective tools for customization are crucial in the manufacturing space, particularly for implementing predictive maintenance to ensure maximum uptime and throughput.
Computer Vision in Retail and Healthcare
In both retail and healthcare, computer vision is used to automate tasks and enhance customer or patient experiences. In retail, computer vision helps improve customer experiences by optimizing food preparation and ensuring ingredients are available based on demand. Healthcare professionals also benefit from computer vision, as it aids in the diagnosis of diseases. By analyzing thousands of medical images, AI systems can filter out potential positive results, saving time and improving efficiency.
Drivers of Computer Vision Expansion
Several factors have contributed to the rapid advancement of computer vision. Processing real-time data has significantly increased the value of AI predictions, allowing for immediate insights and reactions. The flexibility and accuracy of AI models have also improved, making it easier to map them to specific needs. Additionally, tools for customization have become more accessible, enabling fine-tuning of models with less data and resources. These factors, combined with continuous advancements in computer vision technology, have propelled its expansion in various sectors.
Today’s episode is the second in a special series we’re calling ‘Beyond GPU,’ taking a look at edge AI computing challenges and solutions with help from guests at leading vendors and superscale global tech brands leading the most advanced hardware platform teams on the planet. Today’s guest is Adam Burns, Vice President of Network and Edge and Director of EdgeAI Development Tools at Intel. Adam joins us on today’s program to discuss the challenges across sectors in leveraging computer vision and the infrastructure necessary to take advantage of emerging use cases in edge computing. Visit emerj.com/beg1 to learn more about the practical steps for AI deployment for non-technical professionals.
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