Course Outline
Introduction to Generative AI for Government
- What is generative artificial intelligence (AI) and why is it important for government operations?
- Main types and techniques of generative AI relevant to public sector applications.
- Key challenges and limitations of generative AI in the context of government use.
Transformer Architecture and LLMs for Government
- What is a transformer architecture and how does it function in the context of language models?
- Main components and features of transformers that are essential for government applications.
- Utilizing transformers to develop large language models (LLMs) for government use.
Scaling Laws and Optimization for Government
- What are scaling laws and why are they significant for the development of LLMs in government?
- How do scaling laws relate to model size, data volume, computational resources, and inference requirements in governmental contexts?
- How can scaling laws be leveraged to optimize the performance and efficiency of LLMs for government operations?
Training and Fine-Tuning LLMs for Government
- Main steps and challenges involved in training LLMs from scratch for government applications.
- Benefits and drawbacks of fine-tuning LLMs for specific governmental tasks.
- Best practices and tools for training and fine-tuning LLMs to meet the needs of public sector operations.
Deploying and Using LLMs in Government
- Main considerations and challenges associated with deploying LLMs in government production environments.
- Common use cases and applications of LLMs across various governmental domains and industries.
- Integrating LLMs with other AI systems and platforms to enhance government services.
Ethics and Future of Generative AI for Government
- Ethical and social implications of generative AI and LLMs in the public sector.
- Potential risks and harms associated with generative AI and LLMs, such as bias, misinformation, and manipulation in government contexts.
- Strategies for responsible and beneficial use of generative AI and LLMs in governmental operations.
Summary and Next Steps for Government
Requirements
- An understanding of machine learning concepts, including supervised and unsupervised learning, loss functions, and data splitting techniques.
- Experience with Python programming and data manipulation for government applications.
- Basic knowledge of neural networks and natural language processing methodologies.
Audience
- Developers working in the public sector.
- Machine learning enthusiasts interested in government-related projects.
Testimonials (7)
Examples and links excel repository
Olga - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
a lot of examples and different tools to check
Bartosz - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Custom GPTs, prompt engineering
Marcin Stezowski - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Wide perspective
Artur - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Technical examples in conjunction with theory.
Marcin - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Mikołaj background outside IT enable presenting this topic from different angle - much needed for IT folks!
Grzegorz - GE HealthCare
Course - Generative AI with Large Language Models (LLMs)
Explanation form other than IT perspective. Adding value
