Abstract
線上會議連結如下：
連結點我
Disasters like wildfires and wars are increasing in frequency and severity, creating environments where chaos reigns. In these moments, AI holds the potential to revolutionize disaster response—helping first responders stay safe, saving lives, and guiding critical decision-making. Yet current AI systems often fail when faced with the realities of such environments; they assume clean data from reliable sensors, predictable conditions, and well-defined tasks—assumptions that collapse in the face of noisy inputs, shifting contexts, and incomplete information.
In this talk, Ritwik will present a vision for building AI systems that thrive in these complex, high-stakes scenarios. He will explore the core challenges: working with gigapixel images that defy traditional compute paradigms, understanding data from non-visible modalities like synthetic aperture radar, integrating multimodal information from disparate sensors, and making sense of rapidly changing conditions. Tackling these challenges requires fundamentally rethinking AI architectures to account for scalability, adaptability, and robustness—whether by introducing physics-aware models, sensor-in-the-loop designs, or multimodal systems capable of reasoning over fragmented and noisy inputs.
Beyond technical challenges, Ritwik will discuss how AI policy must evolve to bridge the gap between civilian and military applications. By addressing regulatory bottlenecks, dual-use technologies can be deployed responsibly and equitably in both disaster response and defense scenarios. This dual approach—spanning foundational AI research and policy innovation—will help unlock the potential of AI in the world’s most chaotic environments.
連結點我
Disasters like wildfires and wars are increasing in frequency and severity, creating environments where chaos reigns. In these moments, AI holds the potential to revolutionize disaster response—helping first responders stay safe, saving lives, and guiding critical decision-making. Yet current AI systems often fail when faced with the realities of such environments; they assume clean data from reliable sensors, predictable conditions, and well-defined tasks—assumptions that collapse in the face of noisy inputs, shifting contexts, and incomplete information.
In this talk, Ritwik will present a vision for building AI systems that thrive in these complex, high-stakes scenarios. He will explore the core challenges: working with gigapixel images that defy traditional compute paradigms, understanding data from non-visible modalities like synthetic aperture radar, integrating multimodal information from disparate sensors, and making sense of rapidly changing conditions. Tackling these challenges requires fundamentally rethinking AI architectures to account for scalability, adaptability, and robustness—whether by introducing physics-aware models, sensor-in-the-loop designs, or multimodal systems capable of reasoning over fragmented and noisy inputs.
Beyond technical challenges, Ritwik will discuss how AI policy must evolve to bridge the gap between civilian and military applications. By addressing regulatory bottlenecks, dual-use technologies can be deployed responsibly and equitably in both disaster response and defense scenarios. This dual approach—spanning foundational AI research and policy innovation—will help unlock the potential of AI in the world’s most chaotic environments.
Bio
Dr. Ritwik Gupta is the Technical Director for Autonomy at the DoD's Defense Innovation Unit and an incoming Assistant Professor at the University of Maryland. His research focuses on physics-integrated computer vision for complex and chaotic environments, as well as the governance of dual-use AI in both civilian and military contexts. Ritwik’s work has found widespread use for tasks such as assessing building damage after the 2023 Turkey-Syria earthquake and detecting and interdicting criminals engaged in illegal activities on the ocean. His research has been widely covered in press outlets such as TIME, the Wall Street Journal, and CNN.
Ritwik is a graduate fellow with the Berkeley Risk and Security Lab, a research fellow at the Berkeley Human Rights Center, and an AI policy fellow at the Center for Security in Politics. He previously led a research lab focused on AI for humanitarian assistance and disaster response at Carnegie Mellon University and investigated real-time machine learning for the Apple Vision Pro.
Ritwik is a graduate fellow with the Berkeley Risk and Security Lab, a research fellow at the Berkeley Human Rights Center, and an AI policy fellow at the Center for Security in Politics. He previously led a research lab focused on AI for humanitarian assistance and disaster response at Carnegie Mellon University and investigated real-time machine learning for the Apple Vision Pro.