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Course Outline

Foundations of AI Programming

  • Defining AI programming: key concepts and real-world examples
  • AI applications in the public sector: chatbots, summarization tools, and intelligent search
  • Comparing AI models with traditional programming logic

Introductory Python for AI

  • Developing your first Python scripts
  • Managing data structures and control flow logic
  • Essential libraries for AI programming: requests, pandas, and json

Leveraging AI APIs

  • Understanding APIs: secure access to AI models
  • Transmitting text and structured data to models
  • Utilizing APIs from providers such as OpenAI, Cohere, or Hugging Face

Building Simple AI Tools

  • Creating a document summarization tool
  • Prototyping a citizen service chatbot
  • Applying AI for automated labeling of public datasets

Assessing Outputs and Constraints

  • Understanding the probabilistic nature of AI behavior
  • Prompt engineering techniques for managing output quality
  • Conducting red-team exercises to identify bias and hallucinations in prototypes

Compliance, Ethics, and Responsible Development

  • Privacy and explainability mandates in government contexts
  • Advantages and disadvantages of open-source versus proprietary models
  • Checklists for safe experimentation and scaling solutions

Recap and Next Steps

Requirements

  • Foundational experience in working with spreadsheets or structured data
  • Familiarity with public sector service delivery or analytical tasks
  • No prior programming background is necessary, as introductory Python concepts will be covered

Target Audience

  • Public servants and analysts looking to integrate AI into their daily operations
  • Digital government professionals aiming to acquire hands-on AI integration skills
  • Government teams focused on innovation, transformation, and research
 14 Hours

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