In what follows, I appeal to Charles Babbage’s discussion of the division of mental labor to provide evidence that—at least with respect to the social acquisition, storage, retrieval, and transmission of knowledge—epistemologists have, for a broad range of phenomena of crucial importance to actual knowers in their epistemic practices in everyday life, failed adequately to appreciate the significance of socially distributed cognition. If the discussion here is successful, I will have demonstrated that a particular presumption widely held within the contemporary (...) discussion of the epistemology of testimony—a presumption that I will term the personalist requirement—fails to account for those very practices of knowers that I detail here. I will then conclude by suggesting that an alternate account of testimonial warrant, one that has heretofore been underappreciated, ought to be given more serious consideration—in particular because it is well suited to account for those actual practices of knowers that the personalist requirement leaves unrecognized. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data are as important as, (...) and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Artificial Intelligence (AI) is a core area of Cognitive Science, yet today few AI researchers attend the Cognitive Science Society meetings. This essay examines why, how AI has changed over the last 30 years, and some emerging areas of potential interest where AI and the Society can go together in the next 30 years, if they choose.

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

This essay provides a framework of concepts and principles suitable for systematic discussion of issues surrounding expertise. Expertise creates inequality. Its multiple benefits and the creativity of technology lead to a society replete with expertises. The basic binds of expertise derive from the desire of non-experts to be able to both enjoy what expertise offers and insure that it is exercised in the social interest. This involves trusting the exercise of expertise, involuntarily or voluntarily. A healthy society provides various means (...) to move trust from involuntary to voluntary. The social means for achieving this are laid out. The purpose of this short essay is to briefly lay out a conceptual framework within which to construct, clarify, evaluate and apply expertises. It is not to promote some particular notion of expertise over others, or to review the vast literatures, such as that on trust in science, that make up the domain. A few notes on one work towards this essay’s close may indicate what a major, and expert, process this would be. (shrink)

International Studies in the Philosophy of Science, Volume 25, Issue 4, Page 405-411, December 2011.

There is a social or collective sense of ‘knowledge’, as used, for example, in the phrase ‘the growth of scientific knowledge’. In this paper I show that social knowledge does not supervene on facts about what individuals know, nor even what they believe or intend, or any combination of these or other mental states. Instead I develop the idea that social knowing is an analogue to individual knowing, where the analogy focuses on the functional role of social and individual knowing.

Science advances by means of argument and debate. Based on a formal model of complex argumentation, this article assesses the interplay between evidential and inferential drivers in scientific controversy, and explains, in particular, why both evidence accumulation and argumentation are veritistically valuable. By improving the conditions for applying veritistic indicators , novel evidence and arguments allow us to distinguish true from false hypotheses more reliably. Because such veritistic indicators also underpin inductive reasoning, evidence accumulation and argumentation enhance the reliability of (...) inductive inference, for example, inference to the best explanation. 1 Introduction2 Theory of Dialectical Structures3 Debate Simulations4 From Evidence and Arguments to Truth: The First Route5 From Evidence to Truth: The Second Route6 Conclusion. (shrink)

The topic of this book is the thinking in which we engage when we reflectively decide what to do, and when we reflectively reach conclusions as to the correct answers to questions. The main objective is to identify a way of looking at ourselves and at our deliberations that is adequate to our lives. It must accommodate both our conception of ourselves as free, rational and self-directed subjects, and our feeling that we deliberate freely. It must also identify a place (...) for us that will feel like home, doing justice to our status as subjects, within the world as we relate to it when we practise the natural sciences. The central claims are not about how we are, but about how we should look at ourselves. A key task is to show that this limited ambition, which is forced on us by the need to avoid metaphysical implausibility, nonetheless allows us to develop a position that has sufficient strength to do its work. The aim is to show something that is all too easily taken for granted. This is that we can limit ourselves to a strictly naturalistic ontology, while still having access to a generous idiom that allows us to speak of ourselves as free in the exercise of our rationality. (shrink)

BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses (...) machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL. (shrink)