Raphaël Millière 2024 Early Career Fellow
Affiliation Assistant Professor, Macquarie University Hard Problem Solved the science and technological limitations and hard problems in current AI that are critical to enabling further breakthrough progress in AI leading to more powerful and useful AI capable of realizing the beneficial and exciting possibilities, including artificial general intelligence (AGI).

Raphaël Millière is a Lecturer (Assistant Professor) in Philosophy of Artificial Intelligence at Macquarie University in Sydney, Australia. Prior to joining Macquarie in 2023, he was the Robert A. Burt Presidential Scholar in Society and Neuroscience at Columbia University from 2020 to 2023.

His research interests lie at the intersection of philosophy, cognitive science, and artificial intelligence. His current work focuses on assessing the linguistic and reasoning abilities of large language models, drawing on philosophy and computer science to shed light on the potential of these models to advance our understanding of human cognition.

He completed his DPhil in philosophy at the University of Oxford in 2020. He also holds an MA in Philosophy from the Institut Jean Nicod at École Normale Supérieure and a BA in Philosophy from Sorbonne University. His research has been supported by awards such as the Ertegun Scholarship from the University of Oxford, the Jacobsen Studentship from the Royal Institute of Philosophy, and the Young Researcher Prize from Fondation des Treilles.His work has been featured in media outlets such as The Atlantic, Vox, and Wired.

AI2050 Project

This project aims to establish a rigorous framework for understanding how AI systems process information, by identifying and overcoming “interpretability illusions” that mislead us about their inner workings. Drawing on philosophy and computer science, it will develop conceptual foundations and methods to uncover the true features and causal structures underlying AI capabilities. The project will apply these insights to investigate how language models reason over variables. The ultimate goal is to guide the development of AI systems whose reasoning is transparent and reliable, which is an important step towards ensuring advanced AI remains safe and beneficial.