Machine Learning and the Future of Realism

The preceding three decades have seen the emergence, rise, and proliferation of machine learning (ML). From half-recognised beginnings in perceptrons, neural nets, and decision trees, algorithms that extract correlations (that is, patterns) from a set of data points have broken free from their origin in computational cognition to embrace all forms of problem solving, from voice recognition to medical diagnosis to automated scientific research and driverless cars, and it is now widely opined that the real industrial revolution lies less in mobile phone and similar than in the maturation and universal application of ML. Among the consequences just might be the triumph of anti-realism over realism

Re-modelling scientific change: complex systems frames innovative problem solving

Complex systems are used, studied and instantiated in science, with what con-sequences? To be clear and systematic in response it is necessary to distin-guish the consequences, (i) for science, of science using and studying complex systems, (ii) for philosophy of science, of science using and studying complex systems, (iii) for philosophy of science, of philosophy of science modelling sci-ence as a complex system. Each of these is explored in turn, especially (iii). While (iii) has been least studied, it will be shown how modelling science as a complex process may change our conception of science and thereby query what a philosophy of science adequate to this complexity might look like

Bostonia: The Boston University Alumni Magazine. Volume 16

Founded in 1900, Bostonia magazine is Boston University's main alumni publication, which covers alumni and student life, as well as university activities, events, and programs

Expertise, a Framework for our Most Characteristic Asset and Most Basic Inequality

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

Review of Patrick Heelan, Space-Perception and the Philosophy of Science

Heelan has taken a rich philosophical framework and within its categories woven a marvellously detailed and wondrously wide tapestry. That tapestry includes an exciting illumination of Western art and pictorial understanding generally; the sweep of history, scientific and cultural; the enterprise of science and the nature and roles of technology in both science and culture. Heelan\u27s book then has interest at several different levels; in ascending order: there are the specific theses about vision and about science; there is the connecting of philosophy of visual art and philosophy of science; there is Heelan\u27s attempt to set both of these latter fruitfully into an hermeneutic/phenomenological framework. And like any ancient tapestry, it is a book to be savoured for its miniature illuminations, elegant connections across seemingly unrelated weave and surprising reversals of figure and ground, as much as for systematic philosophical argument. I have put the matter this way because, belonging to the Anglo-American analytical tradition as I do, I confess to a certain suspicion of the primacy of first-person, intentional categories which characterise the continental hermeneutic/phenomenological traditions at bottom

Philosophy of complex systems

The domain of nonlinear dynamical systems and its mathematical underpinnings has been developing exponentially for a century, the last 35 years seeing an outpouring of new ideas and applications and a concomitant confluence with ideas of complex systems and their applications from irreversible thermodynamics. A few examples are in meteorology, ecological dynamics, and social and economic dynamics. These new ideas have profound implications for our understanding and practice in domains involving complexity, predictability and determinism, equilibrium, control, planning, individuality, responsibility and so on. Our intention is to draw together in this volume, we believe for the first time, a comprehensive picture of the manifold philosophically interesting impacts of recent developments in understanding nonlinear systems and the unique aspects of their complexity. The book will focus specifically on the philosophical concepts, principles, judgments and problems distinctly raised by work in the domain of complex nonlinear dynamical systems, especially in recent years. This title offers: comprehensive coverage of all main theories in the philosophy of Complex Systems; clearly written expositions of fundamental ideas and concepts; and, definitive discussions by leading researchers in the field. Summaries of leading-edge research in related fields are also included

Georg Simmel and naturalist interactivist epistemology of science

In 1895 sociologist and philosopher Georg Simmel published a paper: ‘On a connection of selection theory to epistemology’. It was focussed on the question of how behavioural success and the evolution of the cognitive capacities that underlie it are to be related to knowing and truth. Subsequently, Simmel’s ideas were largely lost, but recently (2002) an English translation was published by Coleman in this journal. While Coleman’s contextual remarks are solely concerned with a preceding evolutionary epistemology, it will be argued here that Simmel pursues a more unorthodox, more radically biologically based and pragmatist, approach to epistemology in which the presumption of a wholly interests-independent truth is abandoned, concepts are accepted as species-specific and truth tied intimately to practical success. Moreover, Simmel’s position, shorn of one too-radical commitment, shares its key commitments with the recently developed interactivist–constructivist framework for understanding biological cognition and naturalistic epistemology. There Simmel’s position can be given a natural, integrated, three-fold elaboration in interactivist re-analysis, unified evolutionary epistemology and learnable normativity

On fundamental implications of systems and synthetic biology

Systems and synthetic biology promise to revolutionize our understanding of biology, blur the boundaries between the living and the engineered in a vital new bioengineering, and transform our daily relationship to the living world. Their emergence thus deserves to be understood in a wider intellectual perspective. Close attention to their relationship to the larger scientific intellectual frameworks within which they function reveals that systems and synthetic biology raise fundamental challenges to scientific orthodoxy, but stand in the vanguard of an emerging new complex dynamical systems paradigm now sweeping across science

Rationality as effective organisation of interaction and its naturalist framework

The point of this paper is to provide a principled framework for a naturalistic, interactivist-constructivist model of rational capacity and a sketch of the model itself, indicating its merits. Being naturalistic, it takes its orientation from scientific understanding. In particular, it adopts the developing interactivist-constructivist understanding of the functional capacities of biological organisms as a useful naturalistic platform for constructing such higher order capacities as reason and cognition. Further, both the framework and model are marked by the finitude and fallibility that science attributes to organisms, with their radical consequences, and also by the individual and collective capacities to improve their performances that learning organisms display. Part A prepares the ground for the exposition through a critique of the dominant Western analytic tradition in rationalising science, followed by a brief exposition of the naturalist framework that will be employed to frame the construction. This results in two sets of guidelines for constructing an alternative. Part B provides the new conception of reason as a rich complex of processes of improvement against epistemic values, and argues its merits. It closes with an account of normativity and our similarly developing rational knowledge of it, including (reflexively) of reason itself