Intelligence should be "distributed" where "knowledge" is created and "decisions" are made A few years ago, it was hard to find anyone to have a serious discussion about Artificial Intelligence (AI) outside academic institutions. Like any new major technology trend, the new wave of making AI and intelligent systems a reality is creating curiosity and enthusiasm. People are jumping on its bandwagon adding not only great ideas but also in many cases a lot of false promises and sometimes misleading opinions. Built by giant thinkers and academic researchers, AI adoption by industries and further development in academia around the globe is progressing at a faster rate than anyone had excepted. Accelerated by the strong belief that our biological limitations are increasingly becoming a major obstacle towards creating smart systems and machines that work with us to better use our biological cognitive capabilities to achieve higher goals. This is driving an overwhelming wave of demands and investments across industries to apply AI technologies to solve real-world problems and create smarter machines and new businesses.

Communication structure plays a key role in the learning capability of decentralized systems. Structural self-adaptation, by means of self-organization, changes the order as well as the input information of the agents' collective decision-making. This paper studies the role of agents' repositioning on the same communication structure, i.e. a tree, as the means to expand the learning capacity in complex combinatorial optimization problems, for instance, load-balancing power demand to prevent blackouts or efficient utilization of bike sharing stations. The optimality of structural self-adaptations is rigorously studied by constructing a novel large-scale benchmark that consists of 4000 agents with synthetic and real-world data performing 4 million structural self-adaptations during which almost 320 billion learning messages are exchanged. Based on this benchmark dataset, 124 deterministic structural criteria, applied as learning meta-features, are systematically evaluated as well as two online structural self-adaptation strategies designed to expand learning capacity. Experimental evaluation identifies metrics that capture agents with influential information and their optimal positioning. Significant gain in learning performance is observed for the two strategies especially under low-performing initialization. Strikingly, the strategy that triggers structural self-adaptation in a more exploratory fashion is the most cost-effective.

Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the state a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water droplets, we are able to observe an order of magnitude more variety of droplet behaviours than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the discovery of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplets motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how target free search can significantly increase the rate of unpredictable observations leading to new discoveries with potential applications in formulation chemistry.

Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short term memory layers with distributed attention, the present paper achieves state-of-the-art accuracy on temporal crop type classification at a 30x30m resolution with Sentinel 2 imagery.

Power system emergency control is generally regarded as the last safety net for grid security and resiliency. Existing emergency control schemes are usually designed off-line based on either the conceived "worst" case scenario or a few typical operation scenarios. These schemes are facing significant adaptiveness and robustness issues as increasing uncertainties and variations occur in modern electrical grids. To address these challenges, for the first time, this paper developed novel adaptive emergency control schemes using deep reinforcement learning (DRL), by leveraging the high-dimensional feature extraction and non-linear generalization capabilities of DRL for complex power systems. Furthermore, an open-source platform named RLGC has been designed for the first time to assist the development and benchmarking of DRL algorithms for power system control. Details of the platform and DRL-based emergency control schemes for generator dynamic braking and under-voltage load shedding are presented. Extensive case studies performed in both two-area four-machine system and IEEE 39-Bus system have demonstrated the excellent performance and robustness of the proposed schemes.

The headline above an essay in a magazine published by the Association of Computing Machinery (ACM) caught my eye. "Facial recognition is the plutonium of AI", it said. Since plutonium – a by-product of uranium-based nuclear power generation – is one of the most toxic materials known to humankind, this seemed like an alarmist metaphor, so I settled down to read. The article, by a Microsoft researcher, Luke Stark, argues that facial-recognition technology – one of the current obsessions of the tech industry – is potentially so toxic for the health of human society that it should be treated like plutonium and restricted accordingly. You could spend a lot of time in Silicon Valley before you heard sentiments like these about a technology that enables computers to recognise faces in a photograph or from a camera.

There's no denying that Japan, amid a severe labor crunch and a shrinking population, will need to rely more on foreign workers in the coming years, and that's especially true for small and midsize companies. Because of language issues and cultural differences, smaller firms often struggle to integrate foreign workers. But once they overcome those hurdles, many find that the addition of foreign perspectives can lead to new opportunities. Sakae Casting Co., a small aluminum cast manufacturer in Hachioji in western Tokyo, learned this the hard way. But its experience may be an example of what other firms will have to go through in the coming years.

IBM (NYSE: IBM) today announced a new portfolio of Internet of Things (IoT) solutions that team artificial intelligence (AI) and advanced analytics to help asset intensive organizations, such as the Metropolitan Atlanta Rapid Transit Authority (MARTA), to improve maintenance strategies. The solution is designed to help organizations to lower costs and reduce the risk of failure from physical assets such as vehicles, manufacturing robots, turbines, mining equipment, elevators, and electrical transformers. IBM Maximo Asset Performance Management (APM) solutions collect data from physical assets in near real-time and provide insights on current operating conditions, predict potential issues, identify problems and offer repair recommendations. Organizations in asset-intensive industries like energy and utilities, chemicals, oil and gas, manufacturing, and transportation, can have thousands of assets that are critical to operations. These assets are increasingly producing enormous amounts of data on their operating conditions.

Total SA plans to start a digital factory in the coming weeks to tap artificial intelligence in a bid to save hundreds of millions of dollars on exploration and production projects, according to an executive. The use of artificial intelligence to screen geological data will help identify new prospects, and shorten the time to acquire licenses, drill and make discoveries, Arnaud Breuillac, head of E&P, said at a conference organized by IFP Energies Nouvelles in Paris on Friday. It will also help optimize the use of equipment and reduce maintenance costs, he said. The digital factory will employ between 200 and 300 engineers and build on successful North Sea pilot projects, Chief Executive Officer Patrick Pouyanne said at the same event. It will also be a way to attract "young talent" to the industry.