The lay abstract featured today (for Augmenting Intelligence: The Convergence of ML/LLMs and Statistics by Joaquin Carbonara and Ernest Fokoue) is from Stat with the full article now available to read here.

How to Cite

Carbonara, J. and Fokoue, E. (2025), Augmenting Intelligence: The Convergence of ML/LLMs and Statistics. Stat, 14: e70043. https://doi.org/10.1002/sta4.70043

Lay Abstract

In this conceptual work, the authors envision a future where Large Language Models (LLMs) and Natural Language Processing (NLP) tools, such as Generative AI, act as transformative bridges between disciplines that have traditionally remained separate. These technologies promise to unite the rigorous, systematic methodologies inherent to fields like mathematical statistics with the nuanced complexity of human expression and understanding. This synergy reflects the potential for AI to expand the boundaries of what is possible in data-driven research and communication.

Statistical thinking, deeply rooted in mathematical theory, has long been central to the analysis and interpretation of data. In contrast, Generative AI and related NLP technologies are emerging as transformative tools for the field, offering entirely new paradigms for both research and application. The convergence of these disciplines has been accelerated by the widespread availability of Generative AI, beginning with its public release on November 30, 2022, its historically rapid adoption, and the extraordinary pace of advancements in algorithms and hardware. This convergence has been further underscored by the unprecedented recognition of Artificial Intelligence with the awarding of two Nobel Prizes in 2024.

In this work, the authors provide a broad yet detailed pathway for statisticians and practitioners to navigate this evolving landscape. The aim is to offer a clear perspective on the implications of these advancements, cutting through the hype to reveal what lies ahead for the integration of mathematical statistics and AI-driven NLP tools. By doing so, this work seeks to empower statisticians with the knowledge and insight needed to harness these technologies in meaningful and innovative ways.