Liquid Cybernetic Systems: The Fourth‐Order Cybernetics

Abstract

Technological development in robotics, computing architectures and devices, and information storage systems, in one single word: cybernetic systems, has progressed according to a jeopardized connection scheme, difficult if not impossible to track and picture in all its streams. Aim of this progress report is to critically introduce the most relevant limits and present a promising paradigm that might bring new momentum, offering features that naturally and elegantly overcome current challenges and introduce several other advantages: liquid cybernetic systems. The topic describing the four orders of cybernetic systems identified so far is introduced, evidencing the features of the fourth order that includes liquid systems. Then, current limitations to the development of conventional, von Neumann‐based cybernetic systems are briefly discussed: device integration, thermal design, data throughput, and energy consumption. In the following sections, liquid‐state machines are introduced, providing a computational paradigm (free from in materio considerations) that goes into the direction of solving such issues. Two original in materio implementation schemes are proposed: the COlloIdal demonsTratOR (COgITOR) autonomous robot, and a soft holonomic processor that is also proposed to realize an autolographic system.

Experimental Demonstration of In‐Memory Computing in a Ferrofluid System

“…In view of the reported features, we can conclude that further investigations using multiple ports and holonomic concepts [8] and further engineering steps toward a complex computer are possible, based on the spatial arrangement of the ports (see Section S15, Supporting Information). While the generation of DC voltage (at high impedance) can be easily implemented using low-complexity circuit solutions, read-out custom electronic circuits can take advantage of the demonstrated cumulative ultrawide band impedance variation of the colloid, without necessarily requiring accurate frequency synthesis.…”

“…Only recently, liquid and colloidal systems have been subject to attention for mimicking the ions moving in the human brain through embedding aqueous solutions in gel or solid-state scaffolds. [5] Being applicable to soft robotics, [6] energy harvesting, [7] and computation in general, [8] magnetic fluids are always of great research interest (Section S1, Supporting Information). Particularly, ferrofluids (FFs) are mixtures in which nanometricsize dispersed insoluble particles are suspended throughout a solvent, the particles being typically superparamagnetic, giving rise to interesting collective behavior.…”

Emergence of In‐Materio Intelligence from an Incidental Structure of a Single‐Walled Carbon Nanotube–Porphyrin Polyoxometalate Random Network

“…Thus, initially, 80% of the output datasets were used for training with the targets and then the remaining 20% were used for testing the classification performance of the device. For training, only the output weights W m,i out were optimized using a Python-encoded ridge regression [51] model with a ridge regularization coefficient (λ) value with which the identity matrix I was multiplied, as expressed by Equation (7). Here, T represents the transpose of the voltage output matrix V (bold represents matrix format) constituting all the 11 time-series readouts.…”

“…Thus, unlike their von Neumann counterparts, the hardware implementations can instead pave the way for efficient machine intelligence computing. Many such hardware‐implemented NN models are being researched [ 6,7 ] ; however, the one that has recently been attracting attention is directed towards the emulation of reservoir computing (RC) [ 8–11 ] (Figure S1, Supporting Information) because of its bio‐inspired and straightforward framework for processing time‐series data. Execution of such temporal RC machine has been accomplished in atomic switch networks (ASNs), [ 12 ] conductive polymer network, [ 13 ] carbon nanotube (CNT)/polymer composites, [ 14,15 ] optoelectrical systems, [ 16,17 ] soft bodies, [ 18,19 ] spintronics, [ 20,21 ] and water tank systems.…”

Learning in colloids: Synapse-like ZnO + DMSO colloid

“…Nanofluids, or colloidal dispersion of nanoparticles in a liquid solvent, have received a lot of attention in recent years because of their prospective applications [17], encompassing intrinsic plasticity, self-adaptability and fault-tolerance [18,19]. Even after long periods of storage, a stable colloid is expected to remain sediment-free.…”