The Innovation and Digital Transformation course delves into digital transformation in tourism, focusing on customer experience and operational efficiency. Participants will gain insights into digital technologies, innovation, and the steps necessary to drive transformation. A journey through key technologies, innovation, and case studies, this online course provides a comprehensive understanding of digital transformation. Students will engage in videos, readings, and assignments, preparing to implement effective digital strategies and build sustainable tourism ecosystems for long-term success.

This course offers an overview of the different aspects that involves video game development and learning: the skills that can be developed, the connections to the STEAM industry (Science, Technology, Engineering, Arts, and Mathematics), and how they can be used as an ally in the classroom. It also offers practical lessons on how to get started with students and how to develop a game. It is designed to empower teachers with new ways of teaching using digital technologies, and no previous experience with video games or game design is required. The course is built on the pillars of a Gender Transformative Education, and uses the concept of Playing, Learning, Earning, where students are given the opportunity to move from just playing to learning skills and earning a passport to a future career.

A free, self-paced online module in two parts that helps UN personnel recognize and reduce gender bias in generative AI, using examples from UN operations and the Women, Peace and Security agenda. Part 1 explains in plain language what AI and generative AI are and how they can produce biased content; Part 2 shows how these tools can introduce bias into otherwise neutral content and offers practical steps to catch and reduce it in everyday work.

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