Philosophy of Science seminars

Recent high-profile scientific achievements by machine learning (ML) and especially deep learning (DL) systems have reinvigorated interest in ML for automated scientific discovery (eg, Wang et al. 2023). Much of this work is motivated by the thought that DL methods might facilitate the efficient discovery of phenomena, hypotheses, or even models or theories more efficiently than traditional, theory-driven approaches to discovery. This talk considers some of the more specific obstacles to automated, DL-driven discovery in frontier science, focusing on gravitational-wave astrophysics (GWA) as a representative case study. In the first part of the talk, we argue that despite these efforts, prospects for DL-driven discovery in GWA remain uncertain. In the second part, we advocate a shift in focus towards the ways DL can be used to augment or enhance existing discovery methods, and the epistemic virtues and vices associated with these uses. We argue that the primary epistemic virtue of many such uses is to decrease opportunity costs associated with investigating puzzling or anomalous signals, and that the right framework for evaluating these uses comes from philosophical work on pursuitworthiness.

Matthew Cobb uses the history of heredity to question the idea that scientific revolutions are instantly recognisable. The lecture follows changing ideas about inheritance, Mendel’s experiments and their rediscovery, evidence for DNA’s genetic role, the double helix and genetic engineering. Even major discoveries did not immediately settle contemporary debates: protein-based accounts of inheritance persisted after 1953, and evidence for DNA’s role in multicellular organisms developed later. These examples show how retrospective accounts can compress gradual changes into apparently decisive moments.

In this presentation, we will discuss adaptations of historical examples of mathematical research to bring out some of the intuitive judgments that accompany the working practice of mathematicians when reasoning by analogy. The main epistemological claim that we will aim to illustrate is that a central part of mathematical training consists in developing a quasi-perceptual capacity to distinguish superficial from deep analogies. We think of this capacity as an instance of Hadamard’s (1954) discriminating faculty of the mathematical mind, whereby one is led to distinguish between mere “hookings” (77) and “relay-results” (80): on the one hand, suggestions or ‘hints’, useful to raise questions but not to back up conjectures; on the other, more significant discoveries, which can be used as an evidentiary source in further mathematical inquiry. In the second part of the presentation, we will present some recent applications of this epistemological framework to mathematics education projects for middle and high schools in Italy.

In this talk I will introduce the central notions of the theory of autonomy, as it is being currently developed in biology and cognitive science. The theory of autonomy puts forward the capacity of self-determination of organisms as whole systems, and constitutes thereby an alternative to more reductionist and mechanistic approaches. I will discuss how the theory of autonomy provides a justification for the scientific use of notions as function, norm, agency and teleology, whose epistemological legitimacy is highly debated. I will conclude by describing the difficult challenges that poses the transition from biological to cognitive autonomy.

The talk argues for a new approach to analysing analogical reasoning in scientific practice. Traditionally, philosophers of science tend to analyse analogical reasoning in either a top-down way or a bottom-up way. Examples of top-down approaches include Mary Hesse’s seminal work (1963) and Paul Bartha’s articulation model (2010), while most popular bottom-up approach is John Norton’s material approach (2018). I will address the problems of these traditional approaches and introduce an alternative approach, which is motivated by my exemplar-based approach to the history of science, defended in my recent book (2020).

Consciousness is a phenomenon unlike any other studied in natural science. Yet when building theories and designing experiments, we often proceed as if this were not the case. In this talk, I present two recent investigations of mine which explore the implications of consciousness' unique epistemic context for scientific theory building and experimental design. The first investigation is concerned with falsifications of theories of consciousness and identifies a rather deep problem in the usual scheme of testing theories. The second is an axiomatization and subsequent formalization of some of consciousness' more problematic epistemic features that allows to precisely quantify where the usual scientific methodology ceases to be applicable. For both cases, I indicate ways to resolve the problem.

One of the central challenges facing the science of consciousness is that of identifying ways of measuring consciousness. Can we go beyond our pre-theoretical ways of detecting consciousness and develop independently validated measures of consciousness. Some theorists think not, arguing that we cannot go beyond the everyday markers of consciousness with which we begin. Others are more optimistic, and hold that we will be able to develop independent measures of consciousness, or "consciousness meters". This talk discusses various ways in which one might attempt to validate a consciousness meter, arguing that the most promising approach involves treating consciousness as a natural kind.