Consciousness, Meaning and the Future Phenomenology

Phenomenological states are generally considered sources of intrinsic motivation for autonomous biological agents. In this paper we will address the issue of exploiting these states for robust goal-directed systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent “understands” the informational flows entering the agent. This model of consciousness and understanding is based in the analysis and evaluation of phenomenological states along potential trajectories in the phase space of the agents. This implies that a possible strategy to follow in order to build autonomous but useful systems is to embed them with the particular, ad-hoc phenomenology that captures the requirements that define the system usefulness from a requirements-strict engineering viewpoint

Listening to Birds in the Anthropocene : The Anxious Semiotics of Sound in a Human-Dominated World

ACKNOWLEDGEMENTS Funding for much of the research on which this article is based was provided by the Arts and Humanities Research Council of the UK. I thank them and Tim Ingold for their support.Peer reviewedPublisher PD

The imperfect observer: Mind, machines, and materialism in the 21st century

The dualist / materialist debates about the nature of consciousness are based on the assumption that an entirely physical universe must ultimately be observable by humans (with infinitely advanced tools). Thus the dualists claim that anything unobservable must be non-physical, while the materialists argue that in theory nothing is unobservable. However, there may be fundamental limitations in the power of human observation, no matter how well aided, that greatly curtail our ability to know and observe even a fully physical universe. This paper presents arguments to support the model of an inherently limited observer and explores the consequences of this view

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

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems

From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)

This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness

Consciousness, Action Selection, Meaning and Phenomenic Anticipation

Phenomenal states are generally considered the ultimate sources of intrinsic motivation for autonomous biological agents. In this article, we will address the issue of the necessity of exploiting these states for the design and implementation of robust goal-directed artificial systems. We will provide an analysis of consciousness in terms of a precise definition of how an agent "understands" the informational flows entering the agent and its very own action possibilities. This abstract model of consciousness and understanding will be based in the analysis and evaluation of phenomenal states along potential future trajectories in the state space of the agents. This implies that a potential strategy to follow in order to build autonomous but still customer-useful systems is to embed them with the particular, ad hoc phenomenality that captures the system-external requirements that define the system usefulness from a customer-based, requirements-strict engineering viewpoint

Virtual Reality Aesthetics and Boundaries in New Media Art Practices

This dissertation maps out the epistemological and political coordinates of contemporary Virtual Reality (VR) aesthetics through a hybrid inquiry that combines conventional academic research practices with artistic experiments. Since its inception, both conceptually and technologically, VR has emerged as a model for a techno-utopic paradigm that seeks to construct an autonomous image not only from the mediation of artist, but also from the material, spatial, and by extension social and political determinations of reality. With the differences in the formal techniques and strategies of each instance of the media constellation that this teleological paradigm conglomerates such as cinema, early proto-cinematic devices, stereoscopic 3D, and cybernetics, the objective is always the same: to develop an immediate and autonomous interface shorn of limitations configured according to the subjective and bodily conditions of the viewer. In both practice and theory this dissertation attempts to problematize the question of autonomy and by extension heteronomy, which have been distributed in a binary opposition in 20th century artistic practices. I contend that aesthetic practices emerge within the dynamic and interlocked relation between heteronomy and autonomy. Neither artistic practices nor image technologies are autonomous from the political and historical context in which they became possible both technologically and conceptually. Moreover, I argue that artistic practices become critical insofar that the question of autonomy appears sensibly as a problem. Through a threefold inquiry on the question of autonomy and heteronomy, this dissertation has aimed to problematize the very context that made it possible. First, I problematized the autonomy of art purported to be the grounding gesture of the critical nature of research-creation; second, the autonomy purported to be inherent to VR as an immersive and interactive image technology was called into question; and third, as the extension of the second, I problematized the autonomy of the viewer and virtual images in the VR experience that constitutes the artistic experiment component of the dissertation