It’s no secret that AI systems are being used in more and more high-stakes applications – self-driving cars, robotic surgery, and more. As AI advances, it’s becoming critical to ensure that AI systems navigate real-world anomalies successfully.
In this session, Shweta Shrivastava, Senior Director of Product Management for Behavior at Waymo, Vinesh Sukumar, Senior Director of Product Management at Qualcomm, and Dr. Itai Orr, VP of Technology at Autobrains Technologies, discuss what it takes to identify and solve data edge cases to maximize AI performance and achieve widespread adoption.
Here are 3 key takeaways from the session:
- Edge cases vary across domains. An edge case in fintech and how it is handled can be different from how it is handled in the autonomous vehicle domain.
- Edge cases can be categorized into three categories –
- Edge cases that have been in existence for quite some time
- Edge cases for which you physically know what the problem statement is
- Edge cases that you believe might arise in the future
- “Long tail will always exist. It can get into newer types of rare cases but it will never go away,” says Shwetha Shrivastava, Sr. Director of Product Management for Behavior at Waymo. Hence, the primary strategy is to ensure the AI system is able to handle the various situations that can happen in a diverse range of environments.