Edge AI and Vision at Scale: What’s Real, What’s Next, What’s Missing?


Date:  Thursday, May 22

Start Time: 10:25 am

End Time: 11:10 am

Edge AI and vision are no longer science projects—some applications, such as automotive safety systems, have already achieved massive scale. But for every success story, there are many more edge AI and computer vision products that have struggled to move beyond pilot deployments. So what’s holding them back?

Scaling edge AI involves far more than just getting a model to run on a device. Challenges range from physical installation and fleet management to model updates, data drift, hardware changes and supply chain disruptions. And as systems grow, so do the variations in environments, sensor quality and real-world conditions.

What does “scale” really mean in this space—and what does it take to get there? To explore these questions, we’ve assembled a panel of experts with firsthand experience deploying edge AI at scale. Join us for a candid and practical discussion of what’s real, what’s next and what’s still missing.

Track

Session Speakers

  • Sally Ward-Foxton covers AI technology and related issues for EETimes.com and all aspects of the European semiconductor industry for EE Times Europe magazine. Sally has spent more than 20 years writing about the electronics industry from London (UK). She has written for Electronic Design, ECN, Electronic Specifier Design, Components in Electronics and many more. She holds a master’s degree in Electrical and Electronic Engineering from the University of Cambridge.

  • Chen Wu is the Head of Perception at Waymo, a leader in developing safe and reliable self-driving technology. She leads a team of talented engineers and researchers who build cutting-edge machine learning techniques and transform them into real-time perception systems powering Waymo’s autonomous vehicles. Prior to Waymo, Chen’s career explored different applications of technology. She led camera technology development for Google Glass and applied ML for 3D video generation at YouTube. Chen holds a PhD and MS in Electrical Engineering from Stanford University and a BS in Control Theory from Tsinghua University (China).

  • Vikas Bhardwaj is Director, AI at Meta’s Reality Labs and is an industry veteran in AI and machine learning. He is at the forefront of developing cutting-edge AI technologies for wearables, including the innovative multimodal AI on Ray-Ban Meta. His team is actively advancing state-of-the-art edge AI and building applications at scale. Prior to working at Meta, Vikas played a key role in ​building AI chatbots to automate Amazon’s customer service.

  • Vaibhav Ghadiok is the Co-Founder and Chief Technology Officer of Hayden AI, where he has designed an award-winning spatial AI platform that has significantly improved transit times and on-time performance in major cities including New York, Los Angeles and Washington DC. He designed one of the first visually guided autonomous drones and demonstrated the first instance of autonomous aerial manipulation in the world. Vaibhav conceptualized and spearheaded a real-time collaborative HD mapping system for level 2+ vehicles from global automakers. His work has been featured in outlets such as CNN, Bloomberg, Politico, Aaj Tak, Forbes, Time, the Washington Post, the Hindustan Times, Yahoo! Finance and TechCrunch.

  • A ten-year Amazon veteran, Gérard Medioni is a member of the leadership team for Prime Video and Amazon MGM Studios, where he drives adoption of artificial intelligence in content understanding and applications. Prior to joining Prime Video, Gérard was responsible for leading AI and computer vision-based research efforts powering Amazon’s Just Walk Out technology, which provides checkout-free shopping experiences, and the Amazon One palm recognition service, which delivers a new means of identification for entry and payment. Gérard is Professor Emeritus of Computer Science at USC, where he served as the Chair of the CS Department from 2001 to 2007. He is a Fellow of AAAI, IEEE, ACM and NAI, and a member of the National Academy of Engineering.  


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Edge AI and Vision at Scale: What’s Real, What’s Next, What’s Missing?