AI and the Future of Research: Stakeholders, Process, and Practices

Abstract

In the last 10 years colossal cloud infrastructure investments behind the rise of near ubiquitous global mobile technologies have trickled down to scientific research through innovative infrastructure including cloud compute and storage, I/O tools, data analysis and modeling frameworks, which in turn have generated broad and expanding communities of users and supporters. Arguably, the recent success of Large Language Models were catalyzed by the resulting technological innovations of 1) open and accessible massive data, and 2) re-executable discovery pipelines for model estimation and prediction. These changes are deeply disruptive to the research community since they open new paths to knowledge creation that were previously inaccessible and largely culturally unknown.
The scientific community is faced with the challenge of responding to changes in research modalities due to these technological innovations. Research is now conducted as an “Olympics” of benchmarked competitions between machine learning models leveraged by the opaque results of Large Language Models, access to massive data, and redeployment of complex scientific discovery workflows. In this plenary I provide a roadmap of changes and responses by various stakeholders in the research community to ensure that scientific results remain reliable and reproducible, and secure within a position of trust in the broader society.