Ai :: Posit Open Source

Making the most of artificial and human intelligence for data science (Hadley Wickham, Joe Cheng)

Making the most of artificial and human intelligence for data science

Speaker(s): Hadley Wickham; Joe Cheng

Abstract:

This presentation explores the complex and often contradictory nature of large language models (LLMs) in data science, acknowledging the simultaneous excitement and apprehension that we feel toward these technologies. We’ll provide a practical framework to help you understand the LLM ecosystem (from foundation models and hosting to SDKs and applications) that supports our current philosophy: augmenting, not replacing human intelligence. The talk demonstrates how Posit is addressing this space through two complementary approaches: building SDKs and tools that help you create your own LLM-powered solutions, and developing integrated LLM capabilities directly into data science workflows through tools like Positron assistant and databot. We’ll showcase practical, immediately useful applications while addressing current limitations, providing you with both the emotional preparation and technical foundation needed to effectively leverage LLMs in their data science practice today. posit::conf(2025) Subscribe to posit::conf updates: https://posit.co/about/subscription-management/

Hadley Wickham, Joe Cheng

Fernanda Viegas & Martin Wattenberg | Data, visualization, and designing AI | RStudio (2020)

Recent progress in machine learning has raised a series of urgent questions: How can we train and debug deep learning models? How can we understand what is going on inside a neural network? And, perhaps most important, how can we design systems that serve people best? We’ll show a series of examples from the People+AI Research (PAIR) initiative at Google–ranging from data visualizations for researchers, to tools for medical practitioners, to guidelines for designers–that illustrate how thinking carefully about data can lead to better tools, more effective design, and help humans and AI work together.

About Fernanda: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather.

About Martin: Fernanda Viégas and Martin Wattenberg co-lead Google’s PAIR (People+AI Research) initiative, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. They are well known for their contributions to social and collaborative visualization, and the systems they’ve created are used daily by millions of people. Viégas and Wattenberg are also known for visualization-based artwork, which has been exhibited in venues such as the Museum of Modern Art in New York, London Institute of Contemporary Arts and the Whitney Museum of American Art. Their artwork has influenced contemporary design practice: for instance, the techniques in their wind map are now used by many major media companies to display the weather