In recent years, artificial neural networks (ANNs) and deep learning have become
increasingly popular across a wide range of scientific and technical fields, including fluid …

Compute, data, and algorithmic advances are the three fundamental factors that drive
progress in modern Machine Learning (ML). In this paper we study trends in the most readily …

Artificial intelligence (AI) is bringing new possibilities to numerous fields. There have been a
lot of discussions about the development of AI technologies and the challenges caused by …

Learning strategic robot behavior—like that required in pursuit-evasion interactions—under
real-world constraints is extremely challenging. It requires exploiting the dynamics of the …

Abstract Large Language Models (LLMs) have taken the field of AI by storm, but their
adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work …

As the apparent intelligence of artificial neural networks (ANNs) advances, they are
increasingly likened to the functional networks and information processing capabilities of the …

As cybersecurity and AI become increasingly important, introducing these subjects to
younger learners is critical. However, limited attention spans pose challenges for primary …

Liquid–liquid extraction technology exploits the relative ability of solutes to distribute
between immiscible liquid phases in contact to recover target components from primary feed …

Multi-agent reinforcement learning (MARL) has received increasing attention for its
applications in various domains. Researchers have paid much attention on its partially …

This paper is the fourth installment in a series on “AI safety,” an area of machine learning
research that aims to identify causes of unintended behavior in machine learning systems …