We review the main components of autonomous scientific discovery, and how they lead to
the concept of a Robot Scientist. This is a system which uses techniques from artificial …

Increasingly abundant trace data provide an opportunity for information systems researchers
to generate new theory. In this research commentary, we draw on the largely “manual” …

Modeling complex physical dynamics is a fundamental task in science and engineering.
Traditional physics-based models are sample efficient, and interpretable but often rely on …

Earth observation (EO) is a prime instrument for monitoring land and ocean processes,
studying the dynamics at work, and taking the pulse of our planet. This article gives a bird's …

We investigate opportunities and challenges for improving unsupervised machine learning
using four common strategies with a long history in physics: divide and conquer, Occam's …

New forms of digital trace data are becoming ubiquitous. Traditional methods of qualitative
research that aim at developing theory, however, are often overwhelmed by the sheer …

The availability of Big Data sets has led many organizations to shift their emphases from
supporting transaction-oriented data processing to supporting data-centric analytics and …

One of the field in which models of abstraction have been proposed is Artificial Intelligence
(AI). This chapter has two parts: one presents an overview of the formal models, either …

This article discusses the prospects for automated discovery of explanatory models directly
from geospatial data. Rather than taking an approach based on machine learning, which …

In this paper, I pose a new research challenge–to develop intelligent agents that exhibit
radical autonomy by responding to sudden, long-term changes in their environments. I …