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Bayan Bruss, the VP of AI Foundations at Capital One, collaborates with academic researchers to apply cutting-edge AI in finance. He delves into the complexities of out-of-distribution (OOD) detection, emphasizing its significance for safety in AI applications. The discussion also covers the challenges of integrating AI research into corporate environments, the need for model explainability, and the cautious adoption of generative AI. Bruss highlights innovations and limitations in current AI models, underlining the importance of real-world testing to ensure robustness and reliability.
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Quick takeaways
- Out-of-distribution detection is a critical challenge in AI, requiring models to adapt to unseen data to avoid incorrect predictions.
- Capital One emphasizes the importance of AI explainability in high-stakes fields like banking to ensure stakeholder trust in algorithmic decisions.
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Understanding Out of Distribution Detection
Out of distribution (OOD) detection involves identifying data that does not conform to the distribution that a machine learning model was trained on. This situation arises when a model encounters unseen data that is different from its training examples, which can lead to incorrect predictions. For instance, a classifier trained to differentiate between cats and dogs may struggle when faced with entirely new animal species, such as parakeets. The challenge lies in creating models robust enough to handle such variations and potentially adapting or detecting failures in real-time.
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