MBZUAI faculty Kun Zhang is researching methods to improve the reliability of generative AI, particularly in healthcare applications. Current generative AI models often act as "black boxes," making it difficult to understand why a specific result was produced. Zhang's research focuses on incorporating causal relationships into AI systems to ensure more accurate and meaningful information. Why it matters: Improving the trustworthiness of generative AI is crucial for sensitive sectors like healthcare and ensuring responsible AI deployment across the region.
Xiuying Chen from KAUST presented her work on improving the trustworthiness of AI-generated text, focusing on accuracy and robustness. Her research analyzes causes of hallucination in language models related to semantic understanding and neglect of input knowledge, and proposes solutions. She also demonstrated vulnerabilities of language models to noise and enhances robustness using augmentation techniques. Why it matters: Improving the reliability of AI-generated text is crucial for its deployment in sensitive domains like healthcare and scientific discovery, where accuracy is paramount.
Dr. Xinwei Sun from Microsoft Research Asia presented research on trustworthy AI, focusing on statistical learning with theoretical guarantees. The work covers methods for sparse recovery with false-discovery rate analysis and causal inference tools for robustness and explainability. Consistency and identifiability were addressed theoretically, with applications shown in medical imaging analysis. Why it matters: The research contributes to addressing key limitations of current AI models regarding explainability, reproducibility, robustness, and fairness, which are crucial for real-world applications in sensitive fields like healthcare.
Hassan Sajjad from Dalhousie University presented research on exploring the latent space of AI models to assess their safety and trustworthiness. He discussed use cases where analyzing latent space helps understand the robustness-generalization tradeoff in adversarial training and evaluate language comprehension. Sajjad's work aims to build better AI models and increase trust in their capabilities by looking at model internals. Why it matters: Intrinsic evaluation of model internals will become important to improving AI safety and robustness.
Patrick van der Smagt, Director of AI Research at Volkswagen Group, discussed the use of generative machine learning models for predicting and controlling complex stochastic systems in robotics. The talk highlighted examples in robotics and beyond and addressed the challenges of achieving quality and trust in AI systems. He also mentioned his involvement in a European industry initiative on trust in AI and his membership in the AI Council of the State of Bavaria. Why it matters: Understanding control in robotics, along with trust in AI, are key issues for further development of autonomous systems, especially in industrial applications within the GCC region.