Fast-food workers, cashiers, cooks, delivery people and their supporters held a rally outside New York City Hall on May 24, 2017.Erik Mcgregor/Pacific Press/Zuma From the window of his university office in Louvain-la-Neuve, Belgium, philosophy professor Philippe Van Parijs--considered by many to be Europe's most prominent advocate for the idea that the state should provide a regular income to every citizen--can see the mailbox where he sent off invitations to the first "basic income" conference more than 30 years ago. "I'm quite amazed by the seed we threw on the ground now," he says. After decades of obscurity, the idea is suddenly in fashion. Politicians around the world are interested and a handful of governments, such as Finland and the Canadian province of Ontario, are planning or considering basic-income pilot projects. But the idea of basic income has been around for more than 200 years, rising on waves of political and economic turmoil only to disappear in calmer times.

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.

Mark Johnson wants to beat the United States Department of Agriculture at its own game: predicting yields of America's crops. The USDA puts boots on the ground, deploying hundreds of workers to survey thousands of farms a month ahead of the October corn harvest, America's biggest crop. Johnson's startup, Descartes Labs, has just 20 employees, and they never leave the office in Los Alamos, New Mexico. Instead, Descartes relies on 4 petabytes of satellite imaging data and a machine learning algorithm to figure out how healthy the corn crop is from space. Corn yield prediction is big business in the US. Billions of dollars are at stake along the ag supply chain each year as corn starts to come out of the ground in August.

New forms of systems of intelligence are emerging through embedded artificial intelligence, machine learning, and analytics. These data-driven systems of intelligence are enabling digital disruption and new business models. Many companies don't know what steps to take to become digital, where to begin their journey to digital, or how to be sure they won't waste money on innovation they can't implement throughout their company to drive better business results. There is a massive opportunity to help companies take and complete this digital journey, not just to innovate, but to become scaled digital businesses. In this Data Science Central webinar join David Judge, Vice President, Chief Evangelist Leonardo at SAP, Bill Vorhies, Data Scientist, Editorial Director of Data Science Central, and Guilherme Rabello, Commercial and Market Intelligence Manager of InovaInCor, the Innovation department of the Heart Institute (InCor) in São Paulo as they discuss how new technologies are driving digital disruption and the need for innovation.

Artificial intelligence will have a dramatic impact on business by 2020, according to study released this week by IT services, consulting and business solutions provider Tata Consultancy Services (TCS). The firm's study, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies," shows that 84 percent of the 835 executives TCS surveyed from North America, Europe, Asia-Pacific and Latin America said their companies see the use of AI as "essential" to competitiveness. Artificial intelligence in the real world: What can it actually do? What are the limits of AI? And how do you go from managing data points to injecting AI in the enterprise?

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging.

Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have been using complex networks for data representation and analysis. However, no feature learning method has been proposed for such category of techniques. Here, we present an unsupervised feature learning mechanism that works on datasets with binary features. First, the dataset is mapped into a feature--sample network. Then, a multi-objective optimization process selects a set of new vertices to produce an enhanced version of the network. The new features depend on a nonlinear function of a combination of preexisting features. Effectively, the process projects the input data into a higher-dimensional space. To solve the optimization problem, we design two metaheuristics based on the lexicographic genetic algorithm and the improved strength Pareto evolutionary algorithm (SPEA2). We show that the enhanced network contains more information and can be exploited to improve the performance of machine learning methods. The advantages and disadvantages of each optimization strategy are discussed.

DETROIT – Shares of General Motors hit an all-time Tuesday as investors focused on a $2.5 billion third-quarter pretax profit and ignored a big accounting loss. The Detroit automaker's $3 billion net loss came from a $5.4 billion charge for selling Opel and Vauxhall to France's PSA Group, which closed in August. But with that backed out and before taxes, the company made $1.32 per share, trouncing Wall Street estimates. Analysts polled by FactSet expected $1.11 per share. Much of the accounting charge came from previous losses that GM can't use to offset future tax obligations.

The Linux Foundation on Monday introduced the Community Data License Agreement, a new framework for sharing large sets of data required for research, collaborative learning and other purposes. CDLAs will allow both individuals and groups to share data sets in the same way they share open source software code, the foundation said. "As systems require data to learn and evolve, no one organization can build, maintain and source all data required," noted Mike Dolan, VP of strategic programs at The Linux Foundation. "Data communities are forming around artificial intelligence and machine learning use cases, autonomous systems, and connected civil infrastructure," he told LinuxInsider. "The CDLA license agreements enable sharing data openly, embodying best practices learned over decades of sharing source code."

SEOUL – The constant missile and nuclear threats from South Korea's belligerent northern neighbor have racked regional tensions sky-high, but they are a boon for the country's burgeoning defense industry. South Korea has been one of the world's largest importers of military equipment and technology for decades -- mostly from the U.S. -- but in recent years its domestic sector has grown rapidly. Arms exports have soared tenfold in a decade, from just $253 million in 2006 to $2.5 billion last year, according to government data. The country's missiles, howitzers, submarines and warplanes are especially popular in Southeast Asia, Eastern Europe and South America. Once a largely agricultural backwater devastated by war, South Korea now has companies that have become world leaders in fields ranging from shipbuilding to smartphones, and its arms manufacturers are starting to follow suit.