Time series regression analysis in econometrics typically involves a framework relying on a set of mixing conditions to establish consistency and asymptotic normality of parameter estimates and HAC-type estimators of the residual long-run variances to conduct proper inference. This article introduces structured machine learning regressions for high-dimensional time series data using the aforementioned commonly used setting. To recognize the time series data structures we rely on the sparse-group LASSO estimator. We derive a new Fuk-Nagaev inequality for a class of $\tau$-dependent processes with heavier than Gaussian tails, nesting $\alpha$-mixing processes as a special case, and establish estimation, prediction, and inferential properties, including convergence rates of the HAC estimator for the long-run variance based on LASSO residuals. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that the text data can be a useful addition to more traditional numerical data.

In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.

A dry decade in the Navajo Nation has killed vegetation, dessicated soils, and released once-stable sand into the wind. This sand now covers one-third of the Nation's land, threatening roads, gardens and hundreds of homes. Many arid regions have similar problems: global warming has increased dune movement across farmland in Namibia and Angola, and the southwestern US. Current dune models, unfortunately, do not scale well enough to provide useful forecasts for the $\sim$5\% of land surfaces covered by mobile sand. We test the ability of two deep learning algorithms, a GAN and a CNN, to model the motion of sand dunes. The models are trained on simulated data from community-standard cellular automaton model of sand dunes. Preliminary results show the GAN producing reasonable forward predictions of dune migration at ten million times the speed of the existing model.

The need for reskilling and retraining due to the impact of artificial intelligence (AI) and automation technology will be massive, affecting more than 120 million workers across the world's 12 largest economies, according to IBM's Institute for Business Value. In a report entitled The enterprise guide to closing the skills gap, the institute indicated that while only 41% of employers have the required people, skills and resources in place to execute their business strategies effectively today, the situation will only get worse as demand for new – particularly soft – skills continues and expertise focused around repetitive, rules-based activities becomes progressively obsolete. "By 2030, the global talent shortage could reach more than 85 million people," the study says. "To be clear, the issue is not a shortage of workers, but a shortage of workers with the right skills." To make matters worse, although the so-called "half-life" of professional skills was formerly estimated at between 10 and 15 years, the half-life of a learned skill today is estimated to be a mere five years, and is potentially even less for technical expertise. So skills learned now will only be half as valuable in five years' time, which means that finding ways to continually update and refresh them will become an increasing imperative.

Twelve years later, even some of his former Carnegie Mellon University teammates have become business competitors of Salesky, who with CMU alumnus and faculty adviser Peter Rander founded Argo AI and went on to attract substantial investments from Ford Motor Co and Volkswagen AG (VOWG_p.DE). At the 2007 self-driving competition staged by DoD's Defense Advanced Research Projects Agency (DARPA) in remote Victorville, California, Salesky's CMU team and one from rival Stanford University included the future founders of at least four self-driving startups. Those competitors were Chris Urmson and Drew Bagnell of self-driving vehicle startup Aurora, Dave Ferguson of Nuro, Apex.ai's Jan Becker and Anthony Levandowski of Pronto.ai. Sebastian Thrun, who with Levandowski and Urmson helped build Google's self-driving business, also participated in the 2007 DARPA Urban Challenge, as did Dmitri Dolgov, who now heads engineering at Google's self-driving spinout Waymo.

That role, A.I. specialist, is the fastest growing U.S. job in terms of number of hires, at least according to LinkedIn, which published its annual emerging jobs report on Tuesday. Hirings for A.I. specialists on the career networking service have grown 74% annually over the past four years, LinkedIn said. But it didn't reveal how many jobs that represents, only that demand for that job role is growing faster than other emerging jobs. What's noteworthy about this year's survey is that last year's top job role, blockchain developer, is absent from the latest list. It highlights how the recent craze over cryptocurrencies and blockchain created a brief demand for blockchain-related jobs, but as the hype died down, so too did demand for people with blockchain skills.

While the Trump administration scrapped a proposed rule to use facial recognition to identify all people entering and leaving the United States, in other areas the federal government is embracing an expanded use of the technology despite privacy concerns. Let's face it – facial recognition isn't going anywhere. Whether we realize it or not, most of use some type of facial technology software every day. It's being widely used in shopping, home security, and law enforcement, and millions of us use it constantly to open our smart phones. But privacy advocates and civil libertarians are raising alarms about the growing use of facial recognition technology by the federal government under President Trump.

Humana Inc. has employed artificial intelligence to come up with persuasive language in emails sent to customers to encourage more of them to get flu shots--and it is seeing higher open and click-through rates. The Louisville, Ky.-based health insurer serves more than 16 million customers, including four million Medicare Advantage members. Medicare Advantage plans are administered by private insurers. These plans typically offer lower out-of-pocket costs than traditional government-run Medicare in exchange for members using...

Bank of America Corporation (NYSE: BAC) reported interesting statistics regarding its artificial intelligence (AI) – driven virtual financial assistant, Erica . According to the bank, the virtual assistant has surpassed 10 million since its nationwide launch earlier in summer 2018, and it is currently on track to complete 100 million client requests. Now, there are several updates to Erica's software. The latest enhancements include a new Refund Confirmation Insight through which clients are proactively notified when a merchant refund is posted and available to use in their checking, savings or credit card accounts. "Erica is ushering in a new era of personalized banking and providing our clients never-before-possible convenience," said David Tyrie, head of advanced solutions and digital banking at Bank of America.

Today's largest manufacturing country headed the output league table nearly two centuries ago before being ousted by Britain in the first industrial revolution. China accounts for 20 per cent of global output, followed by the United States with 18 per cent, Japan 10 per cent, Germany 7 per cent and South Korea with 4 per cent, according to the most recent (2015) data from the United Nations Conference on Trade and Development. The UK is ninth with 2 per cent. In the intervening centuries there have been sizeable shifts. China reclaimed its crown after 150 years by overtaking America during the past decade.