Joseph Corneli

We think that the future of research can be improved by becoming “even more open”. We mapped existing open tools and workflows across the research lifecycle, identifying gaps in the availability of tools or practices. We collected over 100 tools and created a prototype listing them. We have formulated an ambitious plan of future work, including a position paper (working title: “10 Simple Things Needed for The Future of Science”) and a grant proposal (planned for submission to OSCARS in May 2025).

We think that the future of research can be improved by becoming “even more open”. We mapped existing open tools and workflows across the research lifecycle, identifying gaps in the availability of tools or practices. We collected over 100 tools and created a prototype listing them. We have formulated an ambitious plan of future work, including a position paper (working title: “10 Simple Things Needed for The Future of Science”) and a grant proposal (planned for submission to OSCARS in May 2025).

VirtuosA (‘virtuous algorithm’) is introduced, a model in which artificial intelligence (AI) systems learn ethical behaviour based on a framework adapted from Christian philosopher Dallas Willard and brought together with associated neurobiological structures and broader systems thinking. To make the inquiry concrete, the authors present a simple example scenario that illustrates how a robot might acquire behaviour akin to the virtue of kindness that can be attributed to humans. References to philosophical work by Peter Sloterdijk help contextualise Willard’s virtue ethics framework. The VirtuosA architecture can be implemented using state‐of‐the‐art computing practices and plausibly redescribes several concrete scenarios implemented from the computing literature and exhibits broad coverage relative to other work in ethical AI. Strategies are described for using the model for systems evaluation —particularly the role of ‘embedded evaluation’ within the system—and its broader application as a meta‐ethical device is discussed.

To adequately model mathematical arguments the analyst must be able to represent the mathematical objects under discussion and the relationships between them, as well as inferences drawn about these objects and relationships as the discourse unfolds. We introduce a framework with these properties, which has been used to analyse mathematical dialogues and expository texts. The framework can recover salient elements of discourse at, and within, the sentence level, as well as the way mathematical content connects to form larger argumentative structures. We show how the framework might be used to support computational reasoning, and argue that it provides a more natural way to examine the process of proving theorems than do Lamport's structured proofs.