The present perspective outlines how epistemically baseless and ethically pernicious
paradigms are recycled back into the scientific literature via machine learning (ML) and …

One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …

Mistakes in machine learning practice are commonplace and can result in loss of confidence
in the findings and products of machine learning. This tutorial outlines common mistakes that …

General-purpose artificial intelligence (AI) systems are built on massive swathes of public
web data, assembled into corpora such as C4, RefinedWeb, and Dolma. To our knowledge …

Mistakes in machine learning practice are commonplace, and can result in a loss of
confidence in the findings and products of machine learning. This guide outlines common …

The race to train language models on vast, diverse, and inconsistently documented datasets
has raised pressing concerns about the legal and ethical risks for practitioners. To remedy …

The use of machine learning for predicting ecotoxicological outcomes is promising, but
underutilized. The curation of data with informative features requires both expertise in …

Replication and the reported crises impacting many fields of research have become a focal
point for the sciences. This has led to reforms in publishing, methodological design and …

Foundation model development attracts a rapidly expanding body of contributors, scientists,
and applications. To help shape responsible development practices, we introduce the …

Machine learning (ML) has shown great promise in genetics and genomics where large and
complex datasets have the potential to provide insight into many aspects of disease risk …