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

During this 8-hour deep dive, you will be introduced to the key techniques, services, and trends that will help you understand foundation models from the ground up. This means breaking down theory, mathematics, and abstract concepts combined with hands-on exercises to gain functional intuition for practical application. Throughout the course, we focus on a wide spectrum of progressively complex generative AI techniques, giving you a strong base to understand, design, and apply your own models for the best performance. We’ll start with recapping foundation models, understanding where they come from, how they work, how they relate to generative AI, and what you can to do customize them. You’ll then learn about picking the right foundation model to suit your use case.

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.

Frontier ICTs such as Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) are expected to have a greater impact on sustainable development and are also recognized as an important source of concerns for socio-economic development (especially policymaking) in developing countries. Therefore, this module provides fundamental information, challenges/risks, best practices, policy recommendations, and international cooperation agendas related to frontier ICTs that digital leaders, in particular policymakers in developing countries, should consider when developing and applying the frontier ICTs to enable sustainable development through innovation in their public sector and society.