This self-paced online course is divided into three sections with a total of 11 concise topics, each designed to build progressively on your understanding of innovation in aviation. Starting with core innovation concepts, the course then explores key enablers and practical strategies to overcome common barriers. Each topic takes 10 minutes to complete, offering flexible, anytime learning.

This course gives aspiring young entrepreneurs a clear understanding of the path of entrepreneurship, the risks and rewards of being an entrepreneur, as well as the business start-up process. Young men and women will benefit from enhancing their entrepreneurial mindset and developing skills to identify and act on opportunities.

AI-powered robots, and in particular humanoid and semi-humanoid robots, are increasingly recognised as a foundational and emerging technology with the potential to profoundly transform work, learning, and social systems. This course is part of the broader reflection on the nexus between emerging technologies and decent work: using human-machine collaboration as a guiding lens, the course provides a space to learn about the status of humanoid robots today, their opportunities, risks, and ethical dimensions to act fast and responsibly as its impact on the world of work and learning accelerates. Grounded in the UN and ILO human-centred approach to technology, the course invites participants to explore how humanoid and semi-humanoid robots can be harnessed, rather than merely adopted, to support social justice, decent work, and sustainable development.

By taking this course, you'll learn to: - Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment - Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases - Use empirical scaling laws to optimize the model's objective function across dataset size, compute budget, and inference requirements - Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project - Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners