KEYNOTE: Data-Centric AI: Using Small Sets of Data to Solve Real-World Applications with Deep Learning

Jul 19, 2022
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Landing AI
Teledyne DALSA, Machine Vision OEM Components
About This Webinar
By harnessing the power of “good data” over “big data,” manufacturers with limited data sets can harness the power of deep learning by using machine vision for quality inspection applications. Many AI models rely on huge data sets like those amassed by consumer internet companies such as Google, Facebook, and Amazon. These models get data every second from millions of consumers. But manufacturers don’t always have huge data sets to feed AI engines to target specific issues. They may have limited data sets to train AI models to do what manufacturers need, like spot defects in products before they get too far down the production line. Using data-centric AI, 50 good images can as effectively detect manufacturing defects as 500 bad images.

***This presentation premiered during the 2022 Vision Spectra Conference. For more information on Photonics Media conferences, visit

About the presenter:
Andrew NgAndrew Ng is CEO and founder of Landing AI, which makes building and deploying AI solutions in manufacturing fast and easy. He pioneered the “data-centric” movement to unleash the power of AI and machine vision using limited data sets. Ng is a globally recognized AI visionary, formerly head of Google Brain and chief scientist at Baidu, where he led 1300 people in the company’s AI Group. Ng co-founded Coursera, the world’s leading online learning platform with more than 82 million learners, and he remains active in the classroom as an adjunct professor at Stanford University. Ng has authored or co-authored over 200 research papers on machine learning, robotics, and related fields. In 2013, he was named to the “Time 100” list of the most influential persons. He holds degrees from Carnegie Mellon University, MIT, and the University of California, Berkeley.
artificial intelligencemachine visionVision Spectradata centric AIdeep learning
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