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Arka Daw, an Alvin M. Weinberg Fellow at ORNL, focuses on enhancing AI security by improving the transparency and trustworthiness of machine learning models. His journey into AI began during his undergraduate studies, where he recognized the potential of statistical models in making predictions. Today, he addresses critical challenges in AI, particularly in ensuring the robustness of systems used in vital sectors like climate modeling and national security.
Daw's research includes developing frameworks that demystify AI's 'black box' nature, making it easier for users to trust AI outputs. He also works on defense techniques to protect AI models from adversarial attacks, which can lead to significant errors in applications like autonomous driving. His diverse background and experiences highlight the importance of curiosity and adaptability in the evolving field of AI.
• Daw enhances AI security by improving model transparency and trustworthiness.
• Research focuses on making AI systems robust against adversarial attacks.
Machine learning involves algorithms that enable systems to learn from data and make predictions.
Adversarial attacks are subtle modifications to input data that can mislead AI systems into making errors.
Transparency in AI refers to the clarity of how AI models make decisions and predictions.
ORNL conducts advanced research in AI, focusing on security and transparency in machine learning applications.
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