3 citations · 3 across the 5 of their papers we have counts for
6 papers
Reinforcement Learning with Comparative Evidence for Social Intelligence
Keane Ong, Yuriel Ryan, Sabri Boughorbel +9
Developing socially intelligent AI remains heavily dependent on human-annotated data, limiting the scale and breadth of social understanding models can acquire. Methods that derive…
Dynamics-Aware Weighting for Deep Learning Forecasts of Chaotic Systems
Zhou Fang, Gianmarco Mengaldo
Deep learning surrogates have become powerful tools for simulating and forecasting complex dynamical systems, yet their utility remains limited by catastrophic error accumulation d…
Explainable AI: Learning from the Learners
Ricardo Vinuesa, Steven L. Brunton, Gianmarco Mengaldo
Artificial intelligence now outperforms humans in several scientific and engineering tasks, yet its internal representations often remain opaque. In this Perspective, we argue that…
AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions
Stephen G. Dale, Nikita Kazeev, Alastair J. A. Price +65
Artificial intelligence and machine learning are reshaping how we approach scientific discovery, not by replacing established methods but by extending what researchers can probe, p…
Dynamical errors in machine learning forecasts
Zhou Fang, Gianmarco Mengaldo
In machine learning forecasting, standard error metrics such as mean absolute error (MAE) and mean squared error (MSE) quantify discrepancies between predictions and target values.…
Explain the Black Box for the Sake of Science: the Scientific Method in the Era of Generative Artificial Intelligence
Gianmarco Mengaldo
The scientific method is the cornerstone of human progress across all branches of the natural and applied sciences, from understanding the human body to explaining how the universe…