Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.

Deep-sea mining would cause "extensive and irreversible" damage to sensitive habitats.NOAA This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. An investigation by conservationists has found evidence that deep-seabed mining of rare minerals could cause "extensive and irreversible" damage to the planet. The report, published on Monday by the international wildlife charity Fauna & Flora, adds to the growing controversy that surrounds proposals to sweep the ocean floor of rare minerals that include cobalt, manganese and nickel. Mining companies want to exploit these deposits--which are crucial to the alternative energy sector--because land supplies are running low, they say.

"These things are hard to tip over," geologist Wilson Bonner assures me as the four-wheeled all-terrain vehicle he's piloting tilts suddenly sideways, pitching me toward the churned up mud beneath our wheels. We're grinding up the side of a thickly forested hill in rural Ontario, Canada, on a chilly fall day, heading toward a spot that Bonner's employer, startup KoBold Metals, says represents the marriage of cutting-edge artificial intelligence with one of humanity's oldest industries. We do indeed complete the half-hour trek relatively unmuddied, finally breaking through a ring of broken trees and mangled brush into a swath of bulldozed mud. A black pipe about as wide around as my arm juts out of the ground--the top end of a hole nearly a kilometer deep that was punched into the ground by a truck-sized drilling rig that sits idly nearby. It's not much to look at, but this hole might mark a step into the future of mining, an industry crucial for the world's transition to renewable energy.

Clinical records frequently include assessments of the characteristics of patients, which may include the completion of various questionnaires. These questionnaires provide a variety of perspectives on a patient's current state of well-being. Not only is it critical to capture the heterogeneity given by these perspectives, but there is also a growing demand for developing cost-effective technologies for clinical phenotyping. Filling out many questionnaires may be a strain for the patients and therefore costly. In this work, we propose COBALT -- a cost-based layer selector model for detecting phenotypes using a community detection approach. Our goal is to minimize the number of features used to build these phenotypes while preserving its quality. We test our model using questionnaire data from chronic tinnitus patients and represent the data in a multi-layer network structure. The model is then evaluated by predicting post-treatment data using baseline features (age, gender, and pre-treatment data) as well as the identified phenotypes as a feature. For some post-treatment variables, predictors using phenotypes from COBALT as features outperformed those using phenotypes detected by traditional clustering methods. Moreover, using phenotype data to predict post-treatment data proved beneficial in comparison with predictors that were solely trained with baseline features.

Lithium-ion (Li-ion) batteries commonly used in electric vehicles, small appliances and electronic storage systems are rechargeable and energy-efficient. As the demand for Li-ion batteries escalates, the elements needed to create them, such as cobalt, nickel and lithium, are in short supply. Jodie Lutkenhaus, professor in the Texas A&M University Artie McFerrin Department of Chemical Engineering, and Daniel Tabor, assistant professor in the Department of Chemistry, are using machine learning techniques to optimize polymers needed for developing metal-free, recyclable, organic batteries. The research is funded by the National Science Foundation (NSF) and in collaboration with Juan De Pablo and Stuart Rowan from the University of Chicago. With the approaching Li-ion battery shortage, metal-free batteries offer great potential. In theory, organic batteries could be locally sourced, decreasing demands on supply chains.

The biggest energy transition in history is well and truly underway, and nowhere is the shift more readily apparent than in the transport industry. Wall Street is almost unanimous that electric vehicles are the future of the industry, with EV sales already outpacing ICE sales in markets such as Norway. That kind of exponential growth can only mean one thing: Explosive demand for the metals that go into those batteries. Demand for battery metals is projected to soar as the transport industry continues to electrify at a record pace. In fact, there's a real danger that current mining technologies might struggle to keep up with the demand for battery metals in the near future. Thankfully, Artificial intelligence (AI) can not only be deployed to help improve the way these crucial elements are mined but can replace them altogether.

Yet according to a new paper, it may be the secret sauce for an entirely new kind of computer--one that combines quantum mechanics with the brain's inner workings. The result isn't just a computer with the ability to learn. The mechanisms that allow it to learn are directly embedded in its hardware structure--no extra AI software required. The computer model also simulates how our brains process information, using the language of neuron activity and synapses, rather than the silicon-based churning CPUs in our current laptops. The main trick relies on the quantum spin properties of cobalt atoms.

Throughout the expansion of blockchain into enterprise usage, there has been a steady'arms' race developing between vehicle manufacturers looking to integrate the technology for better efficiencies. Automobile production has long been at the forefront of technological advances, and thus, it makes sense that brands like Mercedes, BMW, Daimler, GM, and a host of others would be driving adoption. The ability of the blockchain to be applied in so many different sectors and the reliance on automobile production on an array of niches means that this match has a lot of potentials that are just starting to be tapped. The likes of BMW and Ford are backing the blockchain to ensure responsible sourcing of cobalt for their manufacturing; Daimler is piloting machine-to-machine payments using a blockchain platform without any human interaction; GM has a patent out for a blockchain-powered solution to manage data from autonomous vehicles; the list goes on, and is very broad. The drive from car manufacturers into the blockchain space may not grab the same headlines as when Google, Facebook, IBM, and other tech giants delve into the new space, but this ongoing push to integrate the technology is essential and vital for growth.

Robotics-as-a-service (RaaS) is about to eat the world of work. While much of the attention in the world of automation technology has been focused on self-driving cars, many other markets traditionally dominated by human-in-the-loop solutions are reaching a point of inflection, enabling RaaS solutions to take over. Robotics companies historically have sold their customers -- you guessed it -- robots. In the enterprise, robots have often been leveraged to streamline manufacturing. Giant companies with ominous, global, megacorp-sounding names like FANUC and ABB provide solutions that require hundreds of thousands, if not millions, of investment dollars just to get started.

During the past year, there have been major implosions of robot startups, such as with Jibo, Anki and Rethink Robotics. They all raised substantial amounts of capital from top-tier investors and had strong teams. One of the main reasons is the extreme complexities of melding software and movable hardware. As a result, the technology often does not live up to expectations. Even with the strides in AI – such as deep learning -- there is still much to do.