Speakers
Description
This session offers a practical and thought-provoking exploration of how generative AI and large language models are transforming the daily work of statisticians, data scientists, and educators.
We start with a concise “kaleidoscope” of real examples illustrating what modern general-purpose AI tools can achieve in practice, with demonstrations that show how complex tasks can now be approached through simple prompts. Examples include interactive maps, image-based data interpretation and data visualization, highlighting AI’s growing ability to extract insights from non-tabular data and its implications for both teaching and applied work.
The second contribution brings a personal perspective on using generative AI as a “virtual team.” Drawing on experience from industry and entrepreneurship, it shows how AI can accelerate tasks such as generating R code, building interfaces, and creating simulations for exploratory purposes. These capabilities enable rapid prototyping, while also requiring careful validation and critical thinking to ensure reliable outcomes.
The session concludes with an organizational viewpoint on prioritizing AI initiatives. It addresses how to distinguish meaningful applications from low-impact experimentation, avoid tool proliferation, and implement governance approaches that balance decentralization with coordinated, value-driven development.
An open discussion will follow, inviting participants to engage, share experiences, and reflect on the role of AI in practice.
| Special/ Invited session | AI tools for data science/research/teaching |
|---|---|
| Classification | Mainly application |
| Keywords | Generative AI, Large Language Models (LLMs), Applied Statistics, AI in Practice, AI Governance |