See part three, where we covered how data science fits in and why data scientists are so important. The impact of incorporating new technologies is huge. Across eight industrial sectors, the value at stake is upward of $17 trillion. When machine learning is applied to industrial data, it opens up new doors for today's businesses to improve their operations and save time and money. They can predict and prevent equipment failures before they happen, improve the availability and reliability of their critical assets, and cost-optimize their maintenance programs.

BEIRUT: The rise of Artificial Intelligence has grown and developed globally, owing to various advances in many fields. Although many people are still finding it hard to understand the virtual intelligence, others are taking in the challenge and bringing together ways to connect computers and humans, producing new AI material that somehow benefits many. With the inspiration of corralling several key ideas, Nicolas Zaatar and Charlie El Khoury have developed their own startup NAR – Next Automated Robot – whose mission was to transform drones from flying cameras to flying computers. "We thought, what if we use the drone as an inspector gadget?" The ideas they have combined are information, data, algorithm, uncertainty, computing, and finally, optimizing.

Artificial intelligence, or rather things like machine learning and automation, which are often wrongly called artificial intelligence, is a big thing in oil and gas right now. The hype around AI spreads a lot further than the oil and gas industry, but in it, the technology is making the first splashes and it looks like they are fast multiplying. While "AI"--or more accurately predictive and analytic algorithms, and automation--in the upstream segment of the industry has garnered some attention already, there is a somewhat surprising part of the oil and gas industry that may be as ripe as exploration and production for some software help: permitting and environmental assessment. Researchers from the Environmental Defense Fund are working on a system using Natural Language Processing that could streamline what is now a very complex process to the benefit of all stakeholders involved. Here's how one of the researchers, Evan Patrick, puts it: "Natural Language Processing pulls out information similar to how humans get information from reading.

"I will not sell the future for instant profit!" Werner von Siemens, 1884 In Atlas Shrugged (1957), by Ayn Rand, the system falls apart to the point that the remaining producers choose to simply withdraw rather than proliferate and disrupt the society from within. "In 1995, Fukuyama argued that only those societies with a high degree of social trust would be able to create the kind of flexible, large-scale business organizations that are needed for successful competition in the global economy." Carrying proudly the responsibility of its 170 years history and legacy, a Tech Giant, an Atlas of the modern era of turbulent markets and exponentially growing challenges, the largest industrial manufacturing company in Europe with its footprint in 180 countries around the globe, the German conglomerate company Siemens AG (German pronunciation: [ˈziːmɛns]) is shaping the future – the digital future. "With its Vision 2020, Siemens has recently once again clearly answered these questions: a company faces up to its responsibilities, furnishes lasting benefit and generates added value from a position of strength – for its shareholders, employees, customers, business partners and societies all over the world. Joe Kaeser, President and Chief Executive Officer of Siemens AG, puts it like this: "Only the strong can help the weak, take responsibility and then fulfill it.

In this paper, we present a general, multistage framework for graphical model approximation using a cascade of models such as trees. In particular, we look at the problem of covariance matrix approximation for Gaussian distributions as linear transformations of tree models. This is a new way to decompose the covariance matrix. Here, we propose an algorithm which incorporates the Cholesky factorization method to compute the decomposition matrix and thus can approximate a simple graphical model using a cascade of the Cholesky factorization of the tree approximation transformations. The Cholesky decomposition enables us to achieve a tree structure factor graph at each cascade stage of the algorithm which facilitates the use of the message passing algorithm since the approximated graph has less loops compared to the original graph. The overall graph is a cascade of factor graphs with each factor graph being a tree. This is a different perspective on the approximation model, and algorithms such as Gaussian belief propagation can be used on this overall graph. Here, we present theoretical result that guarantees the convergence of the proposed model approximation using the cascade of tree decompositions. In the simulations, we look at synthetic and real data and measure the performance of the proposed framework by comparing the KL divergences.

Humanity's future is being shaped by the technologies emerging today. These technologies include artificial intelligence and robotics, future transport and renewable power, data mining and data privacy. They are set to have a huge impact on the way we live, work and play. But these technologies also raise important questions: do we clearly understand the potential of artificial intelligence, how will robots change the nature of work and society more broadly, can we exploit personal information and keep it private at the same time, and are we doing enough to make energy renewable? New Scientist's "Exploring the Future" webinar, sponsored by BAE Systems, will challenge an expert panel of engineers and scientists to discuss these questions and the technologies behind them to better understand how they will change our lives by 2030.

Google services such as its image search and translation tools use sophisticated machine learning which allow computers to see, listen and speak in much the same way as human do. Machine learning is the term for the current cutting-edge applications in artificial intelligence. Basically, the idea is that by teaching machines to "learn" by processing huge amounts of data they will become increasingly better at carrying out tasks that traditionally can only be completed by human brains. These techniques include "computer vision" – training computers to recognize images in a similar way we do. For example, an object with four legs and a tail has a high probability of being an animal.

Canada's rankings in innovation has lagged that of other peer nations for decades despite government efforts to address this issue. Considering its success in developing research programs at its universities, its mediocre rankings overall in technology development is disappointing. Those programs alone have not been enough to translate into entrepreneurial innovation. A 2017 C.D. Howe Institute study points out that, even though Canadians have been at the forefront of breakthroughs in emerging technologies, in many cases, the chief beneficiaries of those breakthroughs have been other nations' economies. Canada needs to take a stronger role in building an environment in which Canadian know-how spurs Canadian business growth. According to a 2017 PwC global survey, Canadian companies stand significantly ahead of their global counterparts in having a dedicated team for digital innovation, with 54% of Canadian respondents reporting that their company does, as opposed to 43% of global respondents. Looking deeper, though, shows a far less innovative spirit, as 47% of respondents said that their pursuit of digital innovation takes the form of seeking to copy others' innovations rather than pursuing their own. Already a decade ago, experts recognized factors that constrain Canadian innovation growth. A 2009 study by the Council of Canadian Academies pointed to two key issues that have held Canadian businesses back from prioritizing innovation in their business strategies. The first issue deals with what has been called "the resource curse." Canada is largely "upstream" in the international supply chain, providing raw materials for other businesses that create products that are in turn passed down the value chain until they reach the stage of finished products sold to end customers. That places Canada in a position far distant from end customers, whose evolving needs spur businesses at the downstream end of the supply chain to adapt, which, in turn, spurs innovation.

The Samsung conglomerate said it will invest more than $22 billion over the next three years to target such areas as artificial intelligence and auto-technology components, as it seeks out growth drivers beyond phones and memory chips. The bulk of the spending will be earmarked for Samsung Electronics Co., the conglomerate's crown jewel. The company is the world's No. 1 maker of smartphones, semiconductors and televisions and last year put more toward capital expenditures than any other publicly traded company, Samsung said it would invest heavily in four key areas through 2020. Auto tech, artificial intelligence and new fifth-generation, or 5G, cellular technology--all of which that fall under Samsung's umbrella--will draw funding, as will its nascent drug companies specializing in contract manufacturing and biosimilar medications. It didn't provide a specific breakdown of the new investments, but the spending represents Samsung's broadest investment in new business pursuits since 2010. When combined with previously announced investments, the company will spend 180 trillion won, or about $161 billion, over the three-year period, a total that includes commitments for semiconductors and displays.

We present a regression technique for data driven problems based on polynomial chaos expansion (PCE). PCE is a popular technique in the field of uncertainty quantification (UQ), where it is typically used to replace a runnable but expensive computational model subject to random inputs with an inexpensive-to-evaluate polynomial function. The metamodel obtained enables a reliable estimation of the statistics of the output, provided that a suitable probabilistic model of the input is available. In classical machine learning (ML) regression settings, however, the system is only known through observations of its inputs and output, and the interest lies in obtaining accurate pointwise predictions of the latter. Here, we show that a PCE metamodel purely trained on data can yield pointwise predictions whose accuracy is comparable to that of other ML regression models, such as neural networks and support vector machines. The comparisons are performed on benchmark datasets available from the literature. The methodology also enables the quantification of the output uncertainties and is robust to noise. Furthermore, it enjoys additional desirable properties, such as good performance for small training sets and simplicity of construction, with only little parameter tuning required. In the presence of statistically dependent inputs, we investigate two ways to build the PCE, and show through simulations that one approach is superior to the other in the stated settings.