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arXiv2012

Computing Nature: A Network of Networks of Concurrent Information Processes

This text presents the research field of natural/unconventional computing as it appears in the book COMPUTING NATURE. The articles discussed consist a selection of works from the Symposium on Natural Computing at AISB-IACAP (British Society for the Study of Artificial Intelligence and the Simulation of Behaviour and The International Association for Computing and Philosophy) World Congress 2012, held at the University of Birmingham, celebrating Turing centenary. The COMPUTING NATURE is about nature considered as the totality of physical existence, the universe. By physical we mean all phenomena, objects and processes, that are possible to detect either directly by our senses or via instruments. Historically, there have been many ways of describing the universe (cosmic egg, cosmic tree, theistic universe, mechanistic universe) while a particularly prominent contemporary approach is computational universe, as discussed in this article.

cs.GL↗
arXiv2012

NanoInfoBio: A case-study in interdisciplinary research

A significant amount of high-impact contemporary scientific research occurs where biology, computer science, engineering and chemistry converge. Although programmes have been put in place to support such work, the complex dynamics of interdisciplinarity are still poorly understood. In this paper we highlight potential barriers to effective research across disciplines, and suggest, using a case study, possible mechanisms for removing these impediments.

cs.GL↗
arXiv2013

The Recomputation Manifesto

Replication of scientific experiments is critical to the advance of science. Unfortunately, the discipline of Computer Science has never treated replication seriously, even though computers are very good at doing the same thing over and over again. Not only are experiments rarely replicated, they are rarely even replicable in a meaningful way. Scientists are being encouraged to make their source code available, but this is only a small step. Even in the happy event that source code can be built and run successfully, running code is a long way away from being able to replicate the experiment that code was used for. I propose that the discipline of Computer Science must embrace replication of experiments as standard practice. I propose that the only credible technique to make experiments truly replicable is to provide copies of virtual machines in which the experiments are validated to run. I propose that tools and repositories should be made available to make this happen. I propose to be one of those who makes it happen.

cs.GL↗
arXiv2013

The Mathematician's Bias - and the Return to Embodied Computation

There are growing uncertainties surrounding the classical model of computation established by Gödel, Church, Kleene, Turing and others in the 1930s onwards. The mismatch between the Turing machine conception, and the experiences of those more practically engaged in computing, has parallels with the wider one between science and those working creatively or intuitively out in the 'real' world. The scientific outlook is more flexible and basic than some understand or want to admit. The science is subject to limitations which threaten careers. We look at embodiment and disembodiment of computation as the key to the mismatch, and find Turing had the right idea all along - amongst a productive confusion of ideas about computation in the real and the abstract worlds.

math.LO↗
arXiv2013

The Ghost in the Quantum Turing Machine

In honor of Alan Turing's hundredth birthday, I unwisely set out some thoughts about one of Turing's obsessions throughout his life, the question of physics and free will. I focus relatively narrowly on a notion that I call "Knightian freedom": a certain kind of in-principle physical unpredictability that goes beyond probabilistic unpredictability. Other, more metaphysical aspects of free will I regard as possibly outside the scope of science. I examine a viewpoint, suggested independently by Carl Hoefer, Cristi Stoica, and even Turing himself, that tries to find scope for "freedom" in the universe's boundary conditions rather than in the dynamical laws. Taking this viewpoint seriously leads to many interesting conceptual problems. I investigate how far one can go toward solving those problems, and along the way, encounter (among other things) the No-Cloning Theorem, the measurement problem, decoherence, chaos, the arrow of time, the holographic principle, Newcomb's paradox, Boltzmann brains, algorithmic information theory, and the Common Prior Assumption. I also compare the viewpoint explored here to the more radical speculations of Roger Penrose. The result of all this is an unusual perspective on time, quantum mechanics, and causation, of which I myself remain skeptical, but which has several appealing features. Among other things, it suggests interesting empirical questions in neuroscience, physics, and cosmology; and takes a millennia-old philosophical debate into some underexplored territory.

quant-ph↗
arXiv2013

Rethinking Abstractions for Big Data: Why, Where, How, and What

Big data refers to large and complex data sets that, under existing approaches, exceed the capacity and capability of current compute platforms, systems software, analytical tools and human understanding. Numerous lessons on the scalability of big data can already be found in asymptotic analysis of algorithms and from the high-performance computing (HPC) and applications communities. However, scale is only one aspect of current big data trends; fundamentally, current and emerging problems in big data are a result of unprecedented complexity--in the structure of the data and how to analyze it, in dealing with unreliability and redundancy, in addressing the human factors of comprehending complex data sets, in formulating meaningful analyses, and in managing the dense, power-hungry data centers that house big data. The computer science solution to complexity is finding the right abstractions, those that hide as much triviality as possible while revealing the essence of the problem that is being addressed. The "big data challenge" has disrupted computer science by stressing to the very limits the familiar abstractions which define the relevant subfields in data analysis, data management and the underlying parallel systems. As a result, not enough of these challenges are revealed by isolating abstractions in a traditional software stack or standard algorithmic and analytical techniques, and attempts to address complexity either oversimplify or require low-level management of details. The authors believe that the abstractions for big data need to be rethought, and this reorganization needs to evolve and be sustained through continued cross-disciplinary collaboration.

cs.GL↗
arXiv2013

Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?

Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply knowledge, in particular when we are using them for training and teaching, or that we want to gain new knowledge, for example when doing analysis or conducting virtual experiments. This paper looks at the history of science to give a context to better cope with the question, how we can gain knowledge from simulation. It addresses aspects of computability and the general underlying mathematics, and applies the findings to validation and verification and development of federations. As simulations are understood as computable executable hypotheses, validation can be understood as hypothesis testing and theory building. The mathematical framework allows furthermore addressing some challenges when developing federations and the potential introduction of contradictions when composing different theories, as they are represented by the federated simulation systems.

cs.GL↗
arXiv2014

Software Carpentry: Lessons Learned

Over the last 15 years, Software Carpentry has evolved from a week-long training course at the US national laboratories into a worldwide volunteer effort to raise standards in scientific computing. This article explains what we have learned along the way the challenges we now face, and our plans for the future.

cs.GL↗
arXiv2013

Typologies of Computation and Computational Models

We need much better understanding of information processing and computation as its primary form. Future progress of new computational devices capable of dealing with problems of big data, internet of things, semantic web, cognitive robotics and neuroinformatics depends on the adequate models of computation. In this article we first present the current state of the art through systematization of existing models and mechanisms, and outline basic structural framework of computation. We argue that defining computation as information processing, and given that there is no information without (physical) representation, the dynamics of information on the fundamental level is physical/ intrinsic/ natural computation. As a special case, intrinsic computation is used for designed computation in computing machinery. Intrinsic natural computation occurs on variety of levels of physical processes, containing the levels of computation of living organisms (including highly intelligent animals) as well as designed computational devices. The present article offers a typology of current models of computation and indicates future paths for the advancement of the field; both by the development of new computational models and by learning from nature how to better compute using different mechanisms of intrinsic computation.

cs.GL↗
arXiv2014

Levels of Abstraction and the Apparent Contradictory Philosophical Legacy of Turing and Shannon

In a recent article, Luciano Floridi explains his view of Turing's legacy in connection to the philosophy of information. I will very briefly survey one of Turing's other contributions to the philosophy of information and computation, including similarities to Shannon's own methodological approach to information through communication, showing how crucial they are and have been as methodological strategies to understanding key aspects of these concepts. While Floridi's concept of Levels of Abstraction is related to the novel methodology of Turing's imitation game for tackling the question of machine intelligence, Turing's other main contribution to the philosophy of information runs contrary to it. Indeed, the seminal concept of computation universality strongly suggests the deletion of fundamental differences among seemingly different levels of description. How might we reconcile these apparently contradictory contributions? I will argue that Turing's contribution should prompt us to plot some directions for a philosophy of information and computation, one that closely parallels the most important developments in computer science, one that understands the profound implications of the works of Turing, Shannon and others.

cs.GL↗
arXiv2015

The Karlskrona manifesto for sustainability design

Sustainability is a central concern for our society, and software systems increasingly play a central role in it. As designers of software technology, we cause change and are responsible for the effects of our design choices. We recognize that there is a rapidly increasing awareness of the fundamental need and desire for a more sustainable world, and there is a lot of genuine goodwill. However, this alone will be ineffective unless we come to understand and address our persistent misperceptions. The Karlskrona Manifesto for Sustainability Design aims to initiate a much needed conversation in and beyond the software community by highlighting such perceptions and proposing a set of fundamental principles for sustainability design.

cs.SE↗
arXiv2015

A Preliminary Review of Influential Works in Data-Driven Discovery

The Gordon and Betty Moore Foundation ran an Investigator Competition as part of its Data-Driven Discovery Initiative in 2014. We received about 1,100 applications and each applicant had the opportunity to list up to five influential works in the general field of "Big Data" for scientific discovery. We collected nearly 5,000 references and 53 works were cited at least six times. This paper contains our preliminary findings.

cs.DL↗
arXiv2015

Writing and Publishing Scientific Articles in Computer Science

Over 15 years of teaching, advising students and coordinating scientific research activities and projects in computer science, we have observed the difficulties of students to write scientific papers to present the results of their research practices. In addition, they repeatedly have doubts about the publishing process. In this article we propose a conceptual framework to support the writing and publishing of scientific papers in computer science, providing a kind of guide for computer science students to effectively present the results of their research practices, particularly for experimental research.

cs.GL↗
arXiv2015

The Machine as Data: A Computational View of Emergence and Definability

Turing's (1936) paper on computable numbers has played its role in underpinning different perspectives on the world of information. On the one hand, it encourages a digital ontology, with a perceived flatness of computational structure comprehensively hosting causality at the physical level and beyond. On the other (the main point of Turing's paper), it can give an insight into the way in which higher order information arises and leads to loss of computational control - while demonstrating how the control can be re-established, in special circumstances, via suitable type reductions. We examine the classical computational framework more closely than is usual, drawing out lessons for the wider application of information-theoretical approaches to characterizing the real world. The problem which arises across a range of contexts is the characterizing of the balance of power between the complexity of informational structure (with emergence, chaos, randomness and 'big data' prominently on the scene) and the means available (simulation, codes, statistical sampling, human intuition, semantic constructs) to bring this information back into the computational fold. We proceed via appropriate mathematical modelling to a more coherent view of the computational structure of information, relevant to a wide spectrum of areas of investigation.

math.LO↗
arXiv2016

Bouncing Towers move faster than Hanoi Towers, but still require exponential time

The problem of the Hanoi Tower is a classic exercise in recursive programming: the solution has a simple recursive definition, and its complexity and the matching lower bound are the solution of a simple recursive function (the solution is so easy that most students memorize it and regurgitate it at exams without truly understanding it). We describe how some very minor changes in the rules of the Hanoi Tower yield various increases of complexity in the solution, so that they require a deeper analysis than the classical Hanoi Tower problem while still yielding exponential solutions. In particular, we analyze the problem fo the Bouncing Tower, where just changing the insertion and extraction position from the top to the middle of the tower results in a surprising increase of complexity in the solution: such a tower of $n$ disks can be optimally moved in $\sqrt{3}^n$ moves for $n$ even (i.e. less than a Hanoi Tower of same height), via $5$ recursive functions (or, equivalently, one recursion function with $5$ states).

cs.GL↗
arXiv2016

Life, The Mind, and Everything

Incompleteness theorems of Godel, Turing, Chaitin, and Algorithmic Information Theory have profound epistemological implications. Incompleteness limits our ability to ever understand every observable phenomenon in the universe. Incompleteness limits the ability of evolutionary processes from finding optimal solutions. Incompleteness limits the detectability of machine consciousness. This is an effort to convey these thoughts and results in a somewhat entertaining manner.

cs.GL↗
arXiv2016

Philosophical Solution to P=?NP: P is Equal to NP

The P=?NP problem is philosophically solved by showing P is equal to NP in the random access with unit multiply (MRAM) model. It is shown that the MRAM model empirically best models computation hardness. The P=?NP problem is shown to be a scientific rather than a mathematical problem. The assumptions involved in the current definition of the P?=NP problem as a problem involving non deterministic Turing Machines (NDTMs) from axiomatic automata theory are criticized. The problem is also shown to be neither a problem in pure nor applied mathematics. The details of The MRAM model and the well known Hartmanis and Simon construction that shows how to code and simulate NDTMs on MRAM machines is described. Since the computation power of MRAMs is the same as NDTMs, P is equal to NP. The paper shows that the justification for the NDTM P?=NP problem using a letter from Kurt Godel to John Von Neumann is incorrect by showing Von Neumann explicitly rejected automata models of computation hardness and used his computer architecture for modeling computation that is exactly the MRAM model. The paper argues that Deolalikar's scientific solution showing P not equal to NP if assumptions from statistical physics are used, needs to be revisited.

cs.GL↗