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Operator learning is a new frontier in computational learning-based approaches for autonomy that enables us to work directly with function-to-function mappings.

The Chishiki project adopts four strategies to build a sustainable, diverse, and integrated community of CI professionals for AI in CEE by (1) fostering collaboration between CIPs and domain experts through initiatives such as research summits, graduate and undergraduate fellowships, joint research initiatives, and industrial partnerships; (2) offering personalized and scalable learning environments powered by AI; (3) developing innovative AI-enabled CI architectures for reproducible and efficient workflows; and (4) creating a diverse, sustainable CIP community through engagement with historically underrepresented institutions through recruitment and research initiatives.

The project addresses the challenge of "safe and efficient growth in global operations, with the expected outcome of an algorithmic foundation that will help realize increasingly autonomous and collaborative air traffic management for all classes of airspace and vehicles and support scalable and efficient operations that rapidly adapt to meet changing demands and to respond to system disruptions.