Reservoir Computing in Materio : An Evaluation of Configuration through Evolution

Recent work has shown that computational substrates made from carbon nanotube/polymer mixtures can form trainable Reservoir Computers. This new reservoir computing platform uses computer based evolutionary algorithms to optimise a set of electrical control signals to induce reservoir properties within the substrate. In the training process, evolution decides the value of analogue control signals (voltages) and the location of inputs and outputs on the substrate that improve the performance of the subsequently trained reservoir readout. Here, we evaluate the performance of evolutionary search compared to randomly assigned electrical configurations. The substrate is trained and evaluated on time-series prediction using the Santa Fe Laser generated competition data (dataset A). In addition to the main investigation, we introduce two new features closely linked to the traditional reservoir computing architecture, adding an evolvable input-weighting mechanism and a reservoir time-scaling parameter. The experimental results show evolved configurations across all four test substrates consistently produce reservoirs with greater performance than randomly configured reservoirs. The results also show that applying both input-weighting and timescaling simultaneously can provide additional tuning to the task, improving performance. For one material, the evolved reservoir is shown to outperform – for this task – all other hardwarebased reservoir computers found in the literature. The same material also outperforms a simple evolved simulated Echo State Network of the same size. The performance of this material is reported to be both consistent after long time-periods and after reconfiguration to other tasks

Measurements, Models, Systems and Design

531 s.

Automated Telescience: Active Machine Learning Of Remote Dynamical Systems

Automated science is an emerging field of research and technology that aims to extend the role of computers in science from a tool that stores and analyzes data to one that generates hypotheses and designs experiments. Despite the tremendous discoveries and advancements brought forth by the scientific method, it is a process that is fundamentally driven by human insight and ingenuity. Automated science aims to develop algorithms, protocols and design philosophies that are capable of automating the scientific process. This work presents advances the field of automated science and the specific contributions of this work fall into three categories: coevolutionary search methods and applications, inferring the underlying structure of dynamical systems, and remote controlled automated science. First, a collection of coevolutionary search methods and applications are presented. These approaches include: a method to reduce the computational overhead of evolutionary algorithms via trainer selection strategies in a rank predictor framework, an approach for optimal experiment design for nonparametric models using Shannon information, and an application of coevolutionary algorithms to infer kinematic poses from RGBD images. Second, three algorithms are presented that infer the underlying structure of dynamical systems: a method to infer discrete-continuous hybrid dynamical systems from unlabeled data, an approach to discovering ordinary differential equations of arbitrary order, and a principle to uncover the existence and dynamics of hidden state variables that correspond to physical quantities from nonlinear differential equations. All of these algorithms are able to uncover structure in an unsupervised manner without any prior domain knowledge. Third, a remote controlled, distributed system is demonstrated to autonomously generate scientific models by perturbing and observing a system in an intelligent fashion. By automating the components of physical experimentation, scientific modeling and experimental design, models of luminescent chemical reactions and multi-compartmental pharmacokinetic systems were discovered without any human intervention, which illustrates how a set of distributed machines can contribute scientific knowledge while scaling beyond geographic constraints

What Makes Complex Systems Complex?

This paper explores some of the factors that make complex systems complex. We first examine the history of complex systems. It was Aristotle’s insight that how elements are joined together helps determine the properties of the resulting whole. We find (a) that scientific reductionism does not provide a sufficient explanation; (b) that to understand complex systems, one must identify and trace energy flows; and (c) that disproportionate causality, including global tipping points, are all around us. Disproportionate causality results from the wide availability of energy stores. We discuss three categories of emergent phenomena—static, dynamic, and adaptive—and recommend retiring the term emergent, except perhaps as a synonym for creative. Finally, we find that virtually all communication is stigmergic

Neo-Schumpeterian Simulation Models

The use of simulation modelling techniques by neo-Schumpeterian economists dates back to Nelson and Winter’s 1982 book “An Evolutionary Theory of Economic Change”, and has rapidly expanded ever since. This paper considers the way in which successive generations of models have extended the boundaries of research (both with respect to the range of phenomena considered and the different dimensions of innovation that are considered), and while simultaneously introducing novel modelling techniques. At the same time, the paper will highlight the distinct set of features that have emerged in these neo-Schumpeterian models, and which set them apart from the models developed by other schools. In particular, they share a distinct view about the type of world in which real economic agents operate, and a invariably contain a generic set of algorithms. In addition to reviewing past models, the paper considers a number of pressing issues that remain unresolved and which modellers will need to address in future. Notable amongst these are the methodological relationship between empirical studies and simulation (e.g. ‘history friendly modelling’), the development of common standards for sensitivity analysis, and the need to further extend the boundaries of research in order to consider important aspects of innovation and technical change.macroeconomics ;

What am I? Virtual Machines and the Mind/Body Problem

When your word processor or email program is running on your computer, this creates a "virtual machine” that manipulates windows, files, text, etc. What is this virtual machine, and what are the virtual objects it manipulates? Many standard arguments in the philosophy of mind have exact analogues for virtual machines and virtual objects, but we do not want to draw the wild metaphysical conclusions that have sometimes tempted philosophers in the philosophy of mind. A computer file is not made of epiphenomenal ectoplasm. I argue instead that virtual objects are "supervenient objects". The stereotypical example of supervenient objects is the statue and the lump of clay. To this end I propose a theory of supervenient objects. Then I turn to persons and mental states. I argue that my mental states are virtual states of a cognitive virtual machine implemented on my body, and a person is a supervenient object supervening on his cognitive virtual machine