Managing AI Effectively Certificate for Mark Roden

DETAILED AGENDA

Introduction to AI and Historical Development

• Evolution from traditional computing to AI
• Met Office case study: Transition from physical modeling to ML
• External developments in ML for weather modelling
• Challenges and opportunities in integrating ML with physics-based models
• Met Office perspectives on the transition (to be discussed by internal staff)
• Future directions for hybrid models and emerging techniques
• Basic concepts of machine learning and AI
• Hands-on: Introduction to Copilot with weather-related examples
• Interactive discussion: AI opportunities in meteorological services
   

15 minute break break

Ethical and Responsible Use of AI

• Fundamental AI ethics principles (personal data protection, bias mitigation, traceability)
• Specific considerations for weather forecasting and climate modelling
• Responsible AI development and deployment in environmental sciences
• Case studies and practical examples in meteorology
• Building an ethical AI culture within the organization
• Data protection and security with AI tools
• Responsible AI use in government organisations
• Workshop: Evaluating AI risks in Met Office contexts
   

45 minutes for lunch

Enablers of AI & Data Usage

• Understanding data quality and preparation
• Explainable AI techniques: 
• Interpretability vs. "black-box" approaches
• Methods for understanding AI decisions
• Practical session: Using Copilot effectively 
• Best practices for prompt writing
• Security considerations
• Output validation
   

15 minute break

Using AI in Your Organisation

• Project management workflows for AI initiatives
• Managing AI projects vs. traditional projects
• Discovery → Modeling → Evaluation → Delivery framework
• Supporting technical teams effectively
• Case studies: Successful AI implementations in weather services
   

Future Planning and Impact

• Human Plus Machine collaboration
• Continuing development through Communities of Practice (CoPs)
• On-the-job learning strategies
• Building AI-aware teams
• Q&A and resources sharing