16 research outputs found
The Natural Science of Computing
As unconventional computing comes of age, we believe a revolution is needed in our view of computer science
Analysis of how a consciousness-lacking mechanistic observer perceives real events and those simulated by a computer
Physical theories must stem from observation. The possibility that perceived
events are simulated, not real, raises a crucial dilemma about the credibility
of known physics, known as the simulation hypothesis. To analyze this
hypothesis in deterministic and mechanistic terms, an observer is conceptually
developed, whose internal state depends on external events. It is demonstrated
that the observer cannot distinguish between simulated and real events. It is
argued that if the observer could observe its entire dynamics, it could
formulate the necessary principles of relativity to distinguish real from
non-real physical motions. Finally, an attempt is made to apply the same
considerations developed for the mechanistic observer to the human being
Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines
The emerging contemporary natural philosophy provides a common ground for the integrative view of the natural, the artificial, and the human-social knowledge and practices. Learning process is central for acquiring, maintaining, and managing knowledge, both theoretical and practical. This paper explores the relationships between the present advances in understanding of learning in the sciences of the artificial (deep learning, robotics), natural sciences (neuroscience, cognitive science, biology), and philosophy (philosophy of computing, philosophy of mind, natural philosophy). The question is, what at this stage of the development the inspiration from nature, specifically its computational models such as info-computation through morphological computing, can contribute to machine learning and artificial intelligence, and how much on the other hand models and experiments in machine learning and robotics can motivate, justify, and inform research in computational cognitive science, neurosciences, and computing nature. We propose that one contribution can be understanding of the mechanisms of ‘learning to learn’, as a step towards deep learning with symbolic layer of computation/information processing in a framework linking connectionism with symbolism. As all natural systems possessing intelligence are cognitive systems, we describe the evolutionary arguments for the necessity of learning to learn for a system to reach human-level intelligence through evolution and development. The paper thus presents a contribution to the epistemology of the contemporary philosophy of nature
Computers in Abstraction/Representation Theory
Recently, Horsman et al. (2014) have proposed a new framework, Abstraction/Representation (AR) theory, for understanding and evaluating claims about unconventional or non-standard computation. Among its attractive features, the theory in particular implies a novel account of what is means to be a computer. After expounding on this account, I compare it with other accounts of concrete computation, finding that it does not quite fit in the standard categorization: while it is most similar to some semantic accounts, it is not itself a semantic account. Then I evaluate it according to the six desiderata for accounts of concrete computation proposed by Piccinini (2015). Finding that it does not clearly satisfy some of them, I propose a modification, which I call Agential AR theory, that does, yielding an account that could be a serious competitor to other leading account of concrete computation
Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
The acceleration race of digital computing technologies seems to be steering
toward impasses -- technological, economical and environmental -- a condition
that has spurred research efforts in alternative, "neuromorphic" (brain-like)
computing technologies. Furthermore, since decades the idea of exploiting
nonlinear physical phenomena "directly" for non-digital computing has been
explored under names like "unconventional computing", "natural computing",
"physical computing", or "in-materio computing". This has been taking place in
niches which are small compared to other sectors of computer science. In this
paper I stake out the grounds of how a general concept of "computing" can be
developed which comprises digital, neuromorphic, unconventional and possible
future "computing" paradigms. The main contribution of this paper is a
wide-scope survey of existing formal conceptualizations of "computing". The
survey inspects approaches rooted in three different kinds of background
mathematics: discrete-symbolic formalisms, probabilistic modeling, and
dynamical-systems oriented views. It turns out that different choices of
background mathematics lead to decisively different understandings of what
"computing" is. Across all of this diversity, a unifying coordinate system for
theorizing about "computing" can be distilled. Within these coordinates I
locate anchor points for a foundational formal theory of a future
computing-engineering discipline that includes, but will reach beyond, digital
and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now
(March 2021) been published as "Toward a generalized theory comprising
digital, neuromorphic, and unconventional computing" in the new open-access
journal Neuromorphic Computing and Engineerin
Computational Dynamics of Natural Information Morphology, Discretely Continuous
This paper presents a theoretical study of the binary oppositions underlying the mechanisms of natural computation understood as dynamical processes on natural information morphologies. Of special interest are the oppositions of discrete vs. continuous, structure vs. process, and differentiation vs. integration. The framework used is that of computing nature, where all natural processes at different levels of organisation are computations over informational structures. The interactions at different levels of granularity/organisation in nature, and the character of the phenomena that unfold through those interactions, are modeled from the perspective of an observing agent. This brings us to the movement from binary oppositions to dynamic networks built upon mutually related binary oppositions, where each node has several properties