By David White, Cadence
EETimes (May 3, 2019)
In the second of a two-part series, an AI expert at Cadence discusses the challenges applying to EDA tools an emerging model for machine learning in decision-support systems.
In an earlier article, I presented a common framework for adaptive decision processes discussed at a NATO science and board meeting I attended. Here, I will discuss common challenges shared across several industries that presented there and that we continue to discuss and work on today.
I participated in a panel on the topic where members represented logistics, operations, transportation, and surveillance as well as electronics design. The number of challenges we shared was amazing.
In real-time continuous learning, unobservable factors may exist in the use model or environment or observable factors may change over time. The uncertainty requires an ability to detect anomalies and adapt quickly.
However, we must ensure systems adapt in a stable way and we know when things change. So, we need formal verification processes to ensure stable learning and robust reactions to unexpected inputs.
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