Research vs. Reality in AI: Would You Trust Your Model with Your Life?
Big Data LDN 2019 | Research vs. Reality in AI: Would You Trust Your Model with Your Life? | Heather Gorr
There are many considerations before deploying deep learning models into the real world^ especially in safety-critical environments like automated driving^ smart medical devices^ aerospace^ and biomedical applications. A deep learning researcher can achieve 99% accuracy on a deep learning model^ but what about the edge cases? What if those edge cases represent someone s life? Is AI ready to move from research to reality? Model accuracy is only one part of a production-ready system^ which also includes: model justification and documentation^ rigorous testing^ use of specialized hardware (GPUs^ FPGAs^ cloud resources^ etc.)^ and collaboration between multiple people with various expertise related to the project and system. In this session^ Heather Gorr will discuss the importance of explainable models^ system design^ and testing before an AI system is production-ready.