This week in Vienna kicks off an incredibly important global discussion happening at Electrify Europe; energy, electricity, and the transformation of our entire power structure. Have you thought about how we will power 100 billion connected things, as well as, support all the electric vehicles set to disrupt the combustion engine automotive industry? In an electricity sector undergoing rapid change and transition, it's vital for us to wrap our minds around the implications on the industry as a whole. Most of us are keenly aware of the conversations happening around Artificial Intelligence, Machine Learning, Blockchain, etc,, but I find it interesting that what powers our future is energy and I'm not hearing much discussion at the global events I have been keynoting this year on what's going to keep the lights on. That's when I found, Electrify Europe, a conference dedicated to bringing together thousands of innovators and thought leaders to discuss how the latest technologies will affect us, and how we can all benefit from evolving our businesses to position them for success in the future.

Not to be outdone by the "world smallest computer" IBM revealed in March, a team at the University of Michigan is calling IBM's bluff with an even smaller computer that's "dwarfed by a grain of rice," measuring just 0.33mm on each side. The university originally held the "world's smallest" trophy with its 2mm x 2mm x 4mm Michigan Micro Mote until IBM's smaller-than-salt 1mm x 1mm computer entered the scene earlier this year. Although the word "computer" brings an image of a miniaturized PC sitting on the tip of your finger, the Michigan team now questions the term. When you power off a desktop or laptop, all the programs and data still reside on the device's internal storage. That can't be said with these "computers" created by IBM and the Michigan team.

That new technologies could actually be bad for us, by sapping our attention or ruining our memories, is an argument that goes back to Socrates. It's tempting to summarily dismiss these concerns, but such tech-doomsaying is actually an important part of economic discovery. Our societies are organised by rules, embedded in our collective knowledge, about the proper way to behave and interact with each other. These rules are worked out over a long, often bitter process of debate and competition between rival ideas about society. Some of the most important rules we need to discover are about how to use technology and, just as importantly, how not to use it.

Russian scientists are developing an advanced automated submarine that will be powered by an external combustion engine, Igor Denisov, deputy director of the Foundation for Advanced Studies (FPI), revealed in an interview with Interfax, a Russian news agency. "We are planning to create an apparatus that will pass through the Northern Sea Route without floating up and without the use of nuclear power, including under the ice," Denisov said. "In order for this device to accomplish such a'feat,' its autonomy should be at least 90 days, which is already commensurate with the autonomy of modern submarines." The decision to forego the nuclear option to power the underwater vehicle was a conscious one, Denisov said, in order to make it increasingly safe. While a nuclear installation helps power submarines for uninterrupted movement throughout the world's oceans, it also puts its operational capabilities at risk.

This two-part work puts forth the idea of engaging power electronics to probe an electric grid to infer non-metered loads. Probing can be accomplished by commanding inverters to perturb their power injections and record the induced voltage response. Once a probing setup is deemed topologically observable by the tests of Part I, Part II provides a methodology for designing probing injections abiding by inverter and network constraints to improve load estimates. The task is challenging since system estimates depend on both probing injections and unknown loads in an implicit nonlinear fashion. The methodology first constructs a library of candidate probing vectors by sampling over the feasible set of inverter injections. Leveraging a linearized grid model and a robust approach, the candidate probing vectors violating voltage constraints for any anticipated load value are subsequently rejected. Among the qualified candidates, the design finally identifies the probing vectors yielding the most diverse system states. The probing task under noisy phasor and non-phasor data is tackled using a semidefinite-program (SDP) relaxation. Numerical tests using synthetic and real-world data on a benchmark feeder validate the conditions of Part I; the SDP-based solver; the importance of probing design; and the effects of probing duration and noise.

Artificial intelligence with machine learning is arguably the most significant of modern analytics techniques being exploited to extract value from the growing mass of modern utility data. A key use case is around asset management and maintenance, which E.ON north German regional grid operator subsidiary Schleswig-Holstein Netz AG has implemented on its medium-voltage grid. After nine months of use, the results, which by extension serve as a power failure warning indicator, have apparently even astonished the company. "The probability that we can predict a defect in the power grid has increased by a factor of two to three," says Thomas König, who is responsible for E.ON's German grid business. "And our customers benefit as well because possible sources of error that we identify in advance reduce the number of faults and make our grid more stable."

IAGON is the first decentralized Artificial Intelligence Blockchain-enabled Supercomputing Grid Technology for harnessing the storage capacities and processing power of multiple smart devices over a decentralized Blockchain/Tangle grid. The platform creates an AI decentralized architecture that manages and optimizes spare/idle distributed computing power and storage around the world, creating a truly decentralized Global Smart Computing Network solution for web-centric and decentralized applications. Utilizing a new consensus mechanism known as Proof-of Utilitarian (PoUW), it promotes decentralized cloud services, where multiple grid miners are rewarded by conducting decentralized parallel computing tasks and storing user's files. IAGON represents a new wave of High Performance Computing powered by Artificial Intelligence, Blockchain, BigData and wrapped in a sophisticated Encryption/Decryption philosophy catering for both individual and corporate clients. About IAGON IAGON is Global Supercomputing powered by Artificial Intelligence, BigData and Blockchain Technology that harnesses the storage capacities and processing power of multiple smart devices over a decentralized network Blockchain/Tangle grid. It's design philosophy is simple in that enables storing of BigData and repositories, as well as smaller scales of files, and carries out complex computational processes through a smart computation grid such as those needed for Artificial Intelligence and Machine Learning operations. IAGON operates a fully secure and encrypted platform that integrates Multiple Blockchain Support/Tangle Technologies, AI-Based Computational Processing, Smart Computational Grid and Secure Lake Technologies in an intuitive and user-friendly environment. Under IAGON's platform you can imagine a world where anyone can profit by joining a massive processing grid. IAGON will provide a fully automated platform for carrying out the storage and processing tasks of users on the basis of unutilized storage and processing capacities that are contributed by participating nodes or "miners".

We prove the existence of a semilinear representation of Cellular Automata (CA) with the introduction of multiple convolution kernels. Examples of the technique are presented for rules akin to the "edge-of-chaos" including the Turing universal rule 110 for further utilization in the area of reservoir computing. We also examine the significance of their dual representation on a frequency or wavelength domain as a superposition of plane waves for distributed computing applications including a new proposal for a "Hologrid" that could be realized with present Wi-Fi/Li-Fi technologies. Keywords: Cellular Automata, Distributed Computing, Holographic Representations Introduction Distributed computing has a long history running the full half of the previous century and its development went hand in hand with the rise of connectionist paradigm out of the study of both natural and artificial neural networks [1]. Perhaps one of the first application of holographic principles in general computation are to be found in the entirely original method of tearing by Kron [2], later termed "Diakoptics", which was invented for the efficient solution of large electrical networks by decomposition.

As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building. As a proof-of-concept, the team is working with staff at the Department of Energy's (DOE) Molecular Foundry, located at Berkeley Lab, to demonstrate the concepts of Science Search on the images captured by the facility's instruments. A beta version of the platform has been made available to Foundry researchers.

IMAGE: This is a screenshot of the Science Search interface. In this case, the user did an image search of nanoparticles. As scientific datasets increase in both size and complexity, the ability to label, filter and search this deluge of information has become a laborious, time-consuming and sometimes impossible task, without the help of automated tools. With this in mind, a team of researchers from Lawrence Berkeley National Laboratory (Berkeley Lab) and UC Berkeley are developing innovative machine learning tools to pull contextual information from scientific datasets and automatically generate metadata tags for each file. Scientists can then search these files via a web-based search engine for scientific data, called Science Search, that the Berkeley team is building.