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📖 Course Outline

Generative AI for Research: Applications and Implications

Welcome to the Course!

Hey folks, and welcome! Here is the outline for the coming weeks. I've got plenty of ideas for each week, but I also want this to be a co-creation experience with all of you. Each week we will have some pre-reading/watching, then we will have the class which will include discussions and some activities, and then there will be some more detailed reading to do after the class and assignments. This is a pilot for the course, so I am very much open to ideas as we go along.

Weekly Structure

📖 Pre-Class Reading materials and videos to prepare for discussion
💬 In-Class Interactive discussions, activities, and collaborative learning
📝 Post-Class Deeper reading and practical assignments to apply concepts

12-Week Journey

Week 1

Foundations of Generative AI

  • Historical development and recent breakthroughs
  • Distinctions between different AI paradigms
  • Introduction to generative AI architectures (transformers, diffusion models)
Week 2

Technical Underpinnings of Modern AI Systems

  • How large language models work
  • Training, fine-tuning, and inference processes
  • Computational resources and infrastructure requirements
Week 3

Environmental Implications of AI

  • Energy consumption of large model training and deployment
  • Carbon footprint calculations
  • Sustainable AI practices and green computing
Week 4

Ethical Frameworks for AI in Research

  • Transparency and attribution
  • Privacy considerations
  • Bias, fairness, and representation
  • Academic integrity when using AI tools
Week 5

AI for Research Ideation

  • Using AI for hypothesis generation
  • Exploring research questions and new perspectives
  • Techniques for creative prompting and idea development
Week 6

AI-Assisted Literature Reviews

  • Strategies for effective literature searching
  • Summarization and synthesis of research papers
  • Citation management and tracking
Week 7

Data Exploration and Analysis with AI

  • AI tools for data preprocessing and cleaning
  • Pattern recognition and anomaly detection
  • Limitations of AI in data analysis
Week 8

Advanced Research Capabilities

  • Knowledge retrieval techniques
  • Implementing deep research functionality
  • Context windows and information retrieval limitations
Week 9

AI as a Scientific Writing Assistant

  • Drafting and editing with AI
  • Technical writing enhancement
  • Maintaining voice and academic standards
Week 10

Limitations and Pitfalls

  • Hallucinations and factual reliability
  • Domain-specific challenges in scientific disciplines
  • Critical evaluation of AI-generated content
Week 11

Future Trends in AI for Research

  • Emerging models and capabilities
  • Multimodal AI in scientific contexts
  • The evolving regulatory landscape
Week 12

Integrative Workshop

  • Students present AI-enhanced research projects
  • Peer review and feedback
  • Reflection on course learnings and applications

🌟 This is a collaborative journey!

As a pilot course, your feedback, ideas, and suggestions throughout the semester will help shape not only your learning experience but also future iterations of this course. Let's explore the frontiers of AI in research together!