Tesla founder Elon Musk believes he can rebuild Puerto Rico's power grid. Tesla CEO Elon Musk speaks during a news conference at the Adelaide Oval in Adelaide, Australia on July 7, 2017. Tesla will partner with French renewable energy developer Neoen to build the world's biggest Lithium IIon Battery, a 100MW battery that will be built in James Town, the South Australian government announced on the day. SAN FRANCISCO -- Elon Musk has so many irons in the fire, you can't see the fire. The Tesla and SpaceX CEO tweeted Friday that he is delaying the unveiling of a self-driving truck in order to focus his attention on smoothing out Model 3 production issues and helping devastated Puerto Rico switch over to solar power. "Tesla Semi unveil now Nov 16," Musk tweeted, pushing it back around three weeks from the original Oct. 26 event.

Almost all of the work in graphical models for game theory has mirrored previous work in probabilistic graphical models. Our work considers the opposite direction: Taking advantage of recent advances in equilibrium computation for probabilistic inference. We present formulations of inference problems in Markov random fields (MRFs) as computation of equilibria in a certain class of game-theoretic graphical models. We concretely establishes the precise connection between variational probabilistic inference in MRFs and correlated equilibria. No previous work exploits recent theoretical and empirical results from the literature on algorithmic and computational game theory on the tractable, polynomial-time computation of exact or approximate correlated equilibria in graphical games with arbitrary, loopy graph structure. We discuss how to design new algorithms with equally tractable guarantees for the computation of approximate variational inference in MRFs. Also, inspired by a previously stated game-theoretic view of state-of-the-art tree-reweighed (TRW) message-passing techniques for belief inference as zero-sum game, we propose a different, general-sum potential game to design approximate fictitious-play techniques. We perform synthetic experiments evaluating our proposed approximation algorithms with standard methods and TRW on several classes of classical Ising models (i.e., with binary random variables). We also evaluate the algorithms using Ising models learned from the MNIST dataset. Our experiments show that our global approach is competitive, particularly shinning in a class of Ising models with constant, "highly attractive" edge-weights, in which it is often better than all other alternatives we evaluated. With a notable exception, our more local approach was not as effective. Yet, in fairness, almost all of the alternatives are often no better than a simple baseline: estimate 0.5.

Convenience store chain Lawson Inc. and Japanese cybermall operator said Friday that they will organize a demonstration test on Oct. 31 for drone delivery services in an area devastated by the March 2011 earthquake and the subsequent nuclear disaster in Fukushima Prefecture. The test will be conducted in Minamisoma, Fukushima Prefecture, utilizing a Lawson outlet, a mobile shop and Rakuten's drone. When the traveling shop visits areas some 2.7 km from the convenience store, it will take orders from local residents and deliver products from the outlet, including warm cooked food. The service will be available only on Thursdays during the test, and the experiment will be conducted over a period of six months. The drone can carry up to 2 kg of products and deliver them in about seven minutes.

Michigan's struggles have played out on the world's stage. Just after the turn of the century began what's referred to as the state's lost decade, the economy faltered, oil prices skyrocketed and the housing market crashed. Nearly a million jobs left the state between 2000 and 2013, many of them in manufacturing and the automotive industry. For a state of just under 10 million people, the impact was devastating: Unemployment was higher than the national average by more than four percent. Bailouts for Chrysler and General Motors were followed by Detroit's record-setting municipal bankruptcy, but through grit and determination, Michigan started clawing its way back from the brink. Now multimillion-dollar investments in the city from tech titans like Amazon, Facebook and LG make headlines with startling frequency, and a host of tech startups have begun to fill the gaps left by plant closures. On the state's west side lies Grand Rapids, where 20 percent of the area's residents are employed in manufacturing jobs, twice the national average, according to a recent report from the Los Angeles Times. About $30,000 per year -- enough for a single person but hardly enough to raise a family. A recent wage survey found machinists in the region averaged $41,710 per year, around half that of software and application developers. Where humans were once doing the physical labor themselves, they're now supervising several machines at a time or operating as quality control. As time goes on, more areas of low-skill labor will be taken over by artificial intelligence, machine learning and automation in what's being labeled the fourth industrial revolution. "When you walk through a plant, there are going to be very few humans on the floor, but there's going to be hundreds of people that are high-tech [trained], maintaining all those systems, because they're not stupid systems that are just stamping out metal parts," said Tom Kelly, executive director of the Michigan-based nonprofit Automation Alley.

The Omega Team was the soul of the company. Whereas the rest of the enterprise brought in the money to keep things going, by various commercial applications of narrow AI, the Omega Team pushed ahead in their quest for what had always been the CEO's dream: building general artificial intelligence. Most other employees viewed "the Omegas," as they affectionately called them, as a bunch of pie-in-the-sky dreamers, perpetually decades away from their goal. They happily indulged them, however, because they liked the prestige that the cutting-edge work of the Omegas gave their company, and they also appreciated the improved algorithms that the Omegas occasionally gave them. What they didn't realize was that the Omegas had carefully crafted their image to hide a secret: They were extremely close to pulling off the most audacious plan in human history. Their charismatic CEO had handpicked them not only for being brilliant researchers, but also for ambition, idealism, and a strong commitment to helping humanity. He reminded them that their plan was extremely dangerous, and that if powerful governments found out, they would do virtually anything--including kidnapping--to shut them down or, preferably, to steal their code. But they were all in, 100 percent, for much the same reason that many of the world's top physicists joined the Manhattan Project to develop nuclear weapons: They were convinced that if they didn't do it first, someone less idealistic would. The AI they had built, nicknamed Prometheus, kept getting more capable. Although its cognitive abilities still lagged far behind those of humans in many areas, for example, social skills, the Omegas had pushed hard to make it extraordinary at one particular task: programming AI systems. They'd deliberately chosen this strategy because they had bought the intelligence explosion argument made by the British mathematician Irving Good back in 1965: "Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man, however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an'intelligence explosion,' and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control."

My colleagues and I had the privilege of attending Tata Consultancy Services' (TCS) Analyst Day event held in Boston on September 21, 2017. There were several interesting and informative presentations covering topics such as the concept of Business 4.0 and how TCS is deploying Digital, deep domain expertise and its deep and vast portfolio of services and solutions to provide its customers with exponential value through mass customization, leveraging ecosystems to help its customers embrace and manage risk while maximizing business outcomes. As one of the ARC Analysts focused on upstream and midstream oil & gas it was great to learn more about how TCS is leveraging'cognitive automation' through its solutions such as Ignio, an product that provides some very powerful horsepower through its self-learning capability, empowered by machine learning and artificial intelligence (AI), that can move customers from predictive maintenance to prescriptive maintenance, thereby extending the life (and availability) of an asset such as a pump or compressor and also optimizing that asset's performance and the process for which it is being utilized. I know first-hand that TCS is successfully helping customers in Australia with pump optimization and increasing pump availability as well as, more importantly, helping to increase the customer's gas processing operations by saving over 100-man days per year, reducing pump downtime and increasing production. Harrick Vin, Global Head of Digitate, explained that he envisions advanced analytics platforms such as Ignio as being technology being augmented by people and one that is capable of learning over time.

Researchers have long been trying to make electronics that are safe to eat. These include edible transistors, sensors, batteries, electrodes, and capacitors, which (if you put them together) are most of an edible robot. What's been missing so far has been the thing that makes a robot distinct from a computing system, and that's an edible actuator that would allow an ingestible robot to actually do something useful once you've swallowed it. At IROS last week, researchers from EPFL's Laboratory of Intelligent Systems, headed by Dario Floreano, presented a prototype of a completely edible soft pneumatic actuator made of gelatin. It probably doesn't taste very good, but it's biodegradable, biocompatible, and environmentally sustainable, and could enable all kinds of novel applications, as the researchers explain in their paper: The components of such edible robots could be mixed with nutrient or pharmaceutical components for digestion and metabolization.

Green technology has been prolific in the news recently with the UK's new industrial strategy demonstrating a step in the right direction towards embracing new technology in the energy space. There are plans in place to majorly change the way electricity is produced, used and stored, as the government becomes more aware of the need for a smarter, more flexible energy system. With UK consumers overpaying a staggering £5.4 billion a year on standard tariffs, it is time that the energy system is redesigned to optimise electricity prices and modernise the grid. As part of their strategy, the government has outlined plans to ensure that all households and businesses are given the option to have a smart meter installed. While these meters give the consumer greater control and a better understanding of their energy usage, the data made available to them only scratches the surface of what is actually possible and what can have a real impact on changing consumer behaviour.

The methodology of community detection can be divided into two principles: imposing a network model on a given graph, or optimizing a designed objective function. The former provides guarantees on theoretical detectability but falls short when the graph is inconsistent with the underlying model. The latter is model-free but fails to provide quality assurance for the detected communities. In this paper, we propose a novel unified framework to combine the advantages of these two principles. The presented method, SGC-GEN, not only considers the detection error caused by the corresponding model mismatch to a given graph, but also yields a theoretical guarantee on community detectability by analyzing Spectral Graph Clustering (SGC) under GENerative community models (GCMs). SGC-GEN incorporates the predictability on correct community detection with a measure of community fitness to GCMs. It resembles the formulation of supervised learning problems by enabling various community detection loss functions and model mismatch metrics. We further establish a theoretical condition for correct community detection using the normalized graph Laplacian matrix under a GCM, which provides a novel data-driven loss function for SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN, and show that the computational complexity of SGC-GEN is comparable to the baseline methods. Our experiments on 18 real-world datasets demonstrate that SGC-GEN possesses superior and robust performance compared to 6 baseline methods under 7 representative clustering metrics.

Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps to several output tasks simultaneously with information sharing between them. We propose a novel multi-task learning framework for sparse linear regression, where a full task hierarchy is automatically inferred from the data, with the assumption that the task parameters follow a hierarchical tree structure. The leaves of the tree are the parameters for individual tasks, and the root is the global model that approximates all the tasks. We apply the proposed approach to develop and evaluate: (a) predictive models of plant traits using large-scale and automated remote sensing data, and (b) GWAS methodologies mapping such derived phenotypes in lieu of hand-measured traits. We demonstrate the superior performance of our approach compared to other methods, as well as the usefulness of discovering hierarchical groupings between tasks. Our results suggest that richer genetic mapping can indeed be obtained from the remote sensing data. In addition, our discovered groupings reveal interesting insights from a plant science perspective.