Towards a Hylomorphic Solution to the Grounding Problem

Concrete particular objects (e.g., living organisms) figure saliently in our everyday experience as well as our in our scientific theorizing about the world. A hylomorphic analysis of concrete particular objects holds that these entities are, in some sense, compounds of matter (hūlē) and form (morphē or eidos). The Grounding Problem asks why an object and its matter (e.g., a statue and the clay that constitutes it) can apparently differ with respect to certain of their properties (e.g., the clay’s ability to survive being squashed, as compared to the statue’s inability to do so), even though they are otherwise so much alike. In this paper, I argue that a hylomorphic analysis of concrete particular objects, in conjunction with a non-modal conception of essence of the type encountered for example in the works of Aristotle and Kit Fine, has the resources to yield a solution to the Grounding Problem

Spacetime Emergence in Quantum Gravity: Functionalism and the Hard Problem

Spacetime functionalism is the view that spacetime is a functional structure implemented by a more fundamental ontology. Lam and Wüthrich have recently argued that spacetime functionalism helps to solve the epistemological problem of empirical coherence in quantum gravity and suggested that it also (dis)solves the hard problem of spacetime, namely the problem of offering a picture consistent with the emergence of spacetime from a non-spatio-temporal structure. First, I will deny that spacetime functionalism solves the hard problem by showing that it comes in various species, each entailing a different attitude towards, or answer to, the hard problem. Second, I will argue that the existence of an explanatory gap, which grounds the hard problem, has not been correctly taken into account in the literature

Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Replies

This paper responds to the contributions by Alexander Bird, Nathan Wildman, David Yates, Jennifer McKitrick, Giacomo Giannini & Matthew Tugby, and Jennifer Wang. I react to their comments on my 2015 book Potentiality: From Dispositions to Modality, and in doing so expands on some of the arguments and ideas of the book

Ontological dependence in a spacetime-world

Priority Monism (hereafter, ‘Monism’), as defined by Jonathan Schaffer (Philos Rev 119:131–176, 2010), has a number of components. It is the view that: the cosmos exists; the cosmos is a maximal actual concrete object, of which all actual concrete objects are parts; the cosmos is basic—there is no object upon which the cosmos depends, ontologically; ontological dependence is a primitive and unanalysable relation. In a recent attack, Lowe (Spinoza on monism. Palgave Macmillan, London, pp 92–122, 2012) has offered a series of arguments to show that Monism fails. He offers up four tranches of argument, with different focuses. These focal points are: (1) being a concrete object; (2) aggregation and dependence; (3) analyses of ontological dependence; (4) Schaffer’s no-overlap principle. These are all technical notions, but each figures at the heart of a cluster of arguments that Lowe puts forward. To respond, I work through each tranche of argument in turn. Before that, in the first section, I offer a cursory statement of Monism, as Schaffer presents it in his 2010 paper, Monism: The Priority of the Whole. I then respond to each of Lowe’s criticisms in turn, deploying material from Schaffer’s 2009 paper Spacetime: the One Substance, as well as various pieces of conceptual machinery from Lowe’s own works (The possibility of metaphysics. Clarendon, Oxford, 1998, 2010) to deflect Lowe’s (Spinoza on monism. Palgave Macmillan, London, pp 92–122, 2012) attacks. In the process of defending Monism from Lowe (Spinoza on monism. Palgave Macmillan, London, pp 92–122, 2012), I end up offering some subtle refinements to Schaffer’s (Philos Rev 119:131–176, 2010) view and explain how the resulting ‘hybrid’ view fares in the wider dialectic

Rational Choice, Scientific Method and Social Scientism

The eighteenth-century introduction of the scientific method of the natural sciences to the study of social phenomena draws a line between moral philosophy Ethat aspect of ancient and medieval philosophy that dealt with social issues Eand the social sciences as known today. From the onset, the emerging social science, or rather, its epistemological orientation to ‘social scientism,Ewas vigorously challenged by many critics who saw it as a reductionist and mechanistic understanding of human beings and their society. In recent times, this criticism has narrowed down to the critique of the rationalist assumptions or rational choice theory on which much of social scientism is built. Critics of the natural science ideal in the social sciences argue that the subject matter of the social sciences Ehuman beings, their society and interactions Eis so complex and different a system that subjecting it to the crucible of the scientific method of the natural, positivist sciences not only limits its understanding but leaves it with an abrasive and distorting impact. In the same manner, critiques of rational choice theory argue that it is a reductionism that does not account for a significant proportion of human actions and motives. What seems to be advocated for is a sort of social science method that addresses the shortcomings of the scientific method applied to social phenomena and employs a more robust model of human action that supersedes the rational choice model. This paper however posits that rationalist assumptions or rational choice theory is not peculiar to social scientism but lies at the foundation of modern and contemporary science and its method. We trace out the centrality of individual rationality assumptions in the general epistemology of the scientific method and social scienticism within the context of the centuries-old debate on the limitations of the scientific method in the social sciences. Our thesis hints at the impossibility of a modern and contemporary scientific model of either nature (physics) or society that does not assume individualist or subjective rationality.Scientific Method, Social Scientism, Rational Choice