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Towards stability and robustness (Practical AI #141)
9 out of 10 AI projects don’t end up creating value in production. Why? At least partly because these projects utilize unstable models and drifting data. In this episode, Roey from BeyondMinds gives us some insights on how to filter garbage input, detect risky output, and generally develop more robust AI systems.
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Featuring:
- Roey Mechrez – Twitter, LinkedIn
- Chris Benson – Twitter, GitHub, LinkedIn, Website
- Daniel Whitenack – Twitter, GitHub, Website
Show Notes:
- NVIDIA GTC21 Talk on “Addressing Garbage In Garbage Out”
- BeyondMinds
Something missing or broken? PRs welcome!