A practical introduction to artificial intelligence designed for government employees, covering core concepts, real-world applications, and the opportunities and challenges of AI.
This course is designed to introduce government workers to the world of Artificial Intelligence (AI). This course provides foundational knowledge of AI technologies like machine learning, deep learning, and large language models, and understand how AI can be used to enhance productivity, decision-making, and problem-solving. Participants will gain a clear understanding of how AI can enhance efficiency, decision-making, and productivity while also addressing the ethical, legal, and regulatory aspects of AI use.
Target Audience
This course is designed for government employees across all agencies and job functions who are new to artificial intelligence.
This course includes:
- 4 hours of live, project-based training from experts
- Proprietary workbook included
- Verified digital certificate of completion
- Learn at an accredited institution
- Credits: 4.0 CPEs
- Small class sizes
What You'll Learn at a Glance
- Define artificial intelligence (AI) and understand its key terminology.
- Explain the architecture of AI systems, focusing on language models and neural networks.
- Engage in meaningful conversations with AI to get the output you need.
- Understand how AI can be applied in government-related tasks, such as data analysis, decision-making, and policy research.
- Evaluate the potential implications of AI technologies on privacy and data security, especially concerning sensitive government data.
Course Syllabus
Module 1: Introduction to AI
- What is AI?
- Key components: Machine learning, neural networks, and deep learning
- Everyday examples of AI (e.g., virtual assistants, recommendation systems, self-driving cars)
- How AI works: Algorithms, data, and computing power
- Introduction to machine learning and neural networks
Module 2: AI Technologies, Architecture, & Usage
- AI architecture: Input, hidden layers, and outputs
- Deep learning vs. machine learning
- Introduction to language models and their applications in natural language processing
- Large language models (LLMs) and their evolution (e.g., GPT)
- Getting started using AI tools
Module 3: Ethical, Legal, and Regulatory Considerations for AI
- Data privacy and AI: Understanding PII and PHI
- Legal implications of AI-generated content (copyright, liability, licensing)
- Ethical challenges: AI biases, transparency, and accountability
- Compliance with regulations: GDPR, CCPA, and other legal frameworks
- Data security and confidentiality in government AI applications
