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Artificial Intelligence

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Generative AI in Business: Applications, Challenges, Ethics, and Governance
June 3 & 5  12:45-4:15 PM    Details and Registration Link

Course Overview:

Open to all. No prior experience required

This course is designed to explore the emerging field of Generative AI (GenAI) with a focus on its application in data analytics. We will delve into the fundamental concepts of generative models, such as GPT, BERT and diffusion, and examine their capabilities in various domains including translation, classification, text and image generation, and more. Additionally, the course will cover critical discussions on the governance and ethics of GenAI systems, addressing the societal, ethical, and regulatory implications of these technologies.  

Course Outline:

  1. Introduction to Generative AI
    • Understanding GenAI
    • Evolution of AI models
    • Overview of Generative Pretrained Transformers (GPT) and their impact
  2. Foundational Concepts
    • Transformer models: Encoder, Decoder, and Attention Mechanisms
    • Difference between models: BERT vs. GPT
    • Introduction to tokenization
    • Vector databases
  3. Generative AI Applications
    • Text Generation
    • Summarization
    • Translation and Classification
    • AI in creative arts: from text to images
  4. Advanced Models and Platforms
    • Overview of current generative AI models (ChatGPT, GPT-4, Claude 3, Llama2, Bard/PaLM 2, etc.)
    • Introduction to Hugging Face and the Open LLM Leaderboard
    • Comparative analysis of GenAI platforms
  5. Prompt Engineering and Applications
    • Zero-shot and few-shot learning
    • Chain-of-thought (CoT) prompting and Tree of Thoughts (ToT)
    • Role-based and temperature settings in AI prompts
  6. Governance and Ethics of Generative AI Systems
    • Understanding the ethical implications of GenAI
    • Governance frameworks for AI
    • Privacy, bias, and fairness in AI models
    • Regulatory and societal impacts
    • Case studies on ethical dilemmas
  7. Demonstrations and Hands-on Sessions
    • Utilizing OpenAI's ChatGPT & Playground for advanced data analysis
    • Integrating GenAI in workflow (Copilot - Excel, Word, PowerPoint)
    • Building custom GPT models for specific tasks
  8. Business Use-Case Examples
    • Discussion and demonstration of projects that apply generative AI techniques to real-world data analytics problems
    • Practical implications of GenAI technologies
  9. Conclusion and Future Directions
    • Reflection on the potentials and limitations of GenAI
    • Discussion on the future trends in AI technologies

 

Instructor

Jeffrey Shaffer headshot

Tableau Zen Master Jeffrey Shaffer is an expert in applying data visualization to create insights and competitive advantage. Mr. Shaffer is Adjunct Assistant Professor at the University of Cincinnati in the Carl H. Lindner College of Business where he teaches Data Visualization in the graduate course series for Data Analytics. He is a regular speaker at conferences, symposiums, universities and corporate training programs on the topic of data visualization, he writes for the data visualization blog at Data + Science and he was a finalist in the 2011 Tableau Interactive Visualization Competition. Mr. Shaffer also teaches data visualization at the KPMG Advisory University. Mr. Shaffer is Vice President of Information Technology and Analytics at Unifund. He joined Unifund in 1996 and has been instrumental in the creation and development of the complex systems, analytics and business intelligence platform at Unifund.Replace with your text