In the present review, the merits and demerits of machine learning (ML) in materials science
are discussed, compared with first principles calculations (PDE (partial differential …

This paper proposes a new conceptual framework called Collective Predictive Coding as a
Model of Science (CPC-MS) to formalize and understand scientific activities. Building on the …

There has long been an impression that reliabilism implies externalism and that frequentist
statistics is externalist because it is reliabilist. I argue, however, that frequentist statistics can …

Otsuka argues for a correspondence between data science and traditional epistemology:
Bayesian statistics is internalist; classical (frequentist) statistics is externalist, owing to its …

This paper engages in a speculative exploration of the concept of an artificial agent capable
of conducting research. Initially, it examines how the act of research can be conceptually …

Uncertainty visualisation is quickly becomming a hot topic in information visualisation.
Exisiting reviews in the field take the definition and purpose of an uncertainty visualisation to …

Plants are capable of a range of complex interactions with the environment. Over the last
decade, some authors have used this as evidence to argue that plants are cognitive agents …

This paper considers the question of what kind of knowledge is produced by deep learning.
Ryle's concept of knowledge how is examined and is contrasted with knowledge with a …

This paper examines the connections among various approaches to understanding
concepts in philosophy, cognitive science, and machine learning, with a particular focus on …

Jun Otsuka's excellent book, Thinking about Statistics-the Philosophical Foundations
(Otsuka) is mostly organized around the idea that different statistical approaches can be …