Third-party cyber evaluations involving OpenAI models
openai.com·14h ago
Data Science
Towards AI·21d ago·5 min read
Learning to Say “I Don’t Know”: The First Floor of Awareness
TL;DR
AI models often exhibit overconfidence, failing to recognize their limitations. By implementing techniques like FGSM training and defensive distillation, researchers improved model awareness.
✦ Why It Matters
Implement FGSM and PGD training in your AI models to enhance their ability to recognize limitations and reduce vulnerabilities.
Key Takeaways
- Use Monte Carlo Dropout for uncertainty estimation during inference.
- Train Deep Ensembles to measure model disagreement effectively.
- Implement Out-of-Distribution Detection to identify unfamiliar inputs.
- Establish a decision gate to defer predictions when uncertainty is high.
How It Works
Monte Carlo Dropout involves running multiple forward passes with dropout enabled to estimate uncertainty through prediction variance. Deep Ensembles train several models with different initializations and measure their disagreement to gauge uncertainty.
Out-of-Distribution Detection calculates the distance of an input from the training data's feature space to identify unfamiliar inputs.