Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S\&E) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S\&E observations and model-simulations often follow heavily skewed distributions and are not well modeled with DL approaches, since they usually optimize a Gaussian, or Euclidean, likelihood loss. Recent developments in Bayesian Deep Learning (BDL), which attempts to capture uncertainties from noisy observations, aleatoric, and from unknown model parameters, epistemic, provide us a foundation. Here we present a discrete-continuous BDL model with Gaussian and lognormal likelihoods for uncertainty quantification (UQ). We demonstrate the approach by developing UQ estimates on "DeepSD", a super-resolution based DL model for Statistical Downscaling (SD) in climate applied to precipitation, which follows an extremely skewed distribution. We find that the discrete-continuous models outperform a basic Gaussian distribution in terms of predictive accuracy and uncertainty calibration. Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes. Such results may be important across S\&E, as well as other domains such as finance and economics, where extremes are often of significant interest. Furthermore, to our knowledge, this is the first UQ model in SD where both aleatoric and epistemic uncertainties are characterized.

Word evolution refers to the changing meanings and associations of words throughout time, as a byproduct of human language evolution. By studying word evolution, we can infer social trends and language constructs over different periods of human history. However, traditional techniques such as word representation learning do not adequately capture the evolving language structure and vocabulary. In this paper, we develop a dynamic statistical model to learn time-aware word vector representation. We propose a model that simultaneously learns time-aware embeddings and solves the resulting "alignment problem". This model is trained on a crawled NYTimes dataset. Additionally, we develop multiple intuitive evaluation strategies of temporal word embeddings. Our qualitative and quantitative tests indicate that our method not only reliably captures this evolution over time, but also consistently outperforms state-of-the-art temporal embedding approaches on both semantic accuracy and alignment quality.

Imagine a situation where a group of adversaries is preparing an attack on the United States or U.S. interests. An intelligence analyst has observed some signals, but the situation is rapidly changing. The analyst faces the decision to alert a principal decision maker that an attack is imminent, or to wait until more is known about the situation. This warning decision is based on the analyst's observation and evaluation of signals, independent or correlated, and on her updating of the prior probabilities of possible scenarios and their outcomes. The warning decision also depends on the analyst's assessment of the crisis' dynamics and perception of the preferences of the principal decision maker, as well as the lead time needed for an appropriate response. This article presents a model to support this analyst's dynamic warning decision. As with most problems involving warning, the key is to manage the tradeoffs between false positives and false negatives given the probabilities and the consequences of intelligence failures of both types. The model is illustrated by revisiting the case of the attack on Pearl Harbor in December 1941. It shows that the radio silence of the Japanese fleet carried considerable information (Sir Arthur Conan Doyle's "dog in the night" problem), which was misinterpreted at the time. Even though the probabilities of different attacks were relatively low, their consequences were such that the Bayesian dynamic reasoning described here may have provided valuable information to key decision makers.

RESTON, Va.--(BUSINESS WIRE)--NCI, Inc., a leading provider of information technology and professional services and solutions to U.S. Federal Government agencies, today announced Brad Mascho has joined the company as chief artificial intelligence officer (CAIO). In this role, he is responsible for leading a new corporate division focused around artificial intelligence (AI) initiatives and strategy to accelerate, automate and augment repeatable processes for NCI's customers, quickly turning data into actionable intelligence. "NCI's purpose is to bridge the gap between commercial innovation and missions of national importance," said NCI CEO Paul Dillahay. "Being one of the first solution providers to add a CAIO is just another example of our commitment to innovation. Brad is the ideal visionary to help our clients realize the transformational impact happening at the intersection of AI, machine learning and predictive analytics. We are excited to add Brad's entrepreneurial spirit to our executive leadership team."

This on-demand webinar covers the various ways in which artificial intelligence (AI) and machine learning (ML) are coming to dominate the cyber security landscape. This webinar provides you with an understanding of how the various types of machine learning techniques are being applied to cyber security and how those techniques are being tailored to solve particular problems in cyber security. It also covers why using multiple artificial intelligence or machine learning-based solutions enhances a defense-in-depth approach to security and how the fundamentals of cyber defense and offense are changing due to the greater adoption of these solutions.

The human brain is responsible for making us adaptable and widespread -- a singularly adept instrument to help humans survive and thrive. Even as artificial intelligence quickly progresses, when it comes to military conflicts, people still outpace robots in crucial split-second decision-making. Slowly but surely, though, the gap is lessening, and training robots' targeting capabilities using human brain responses may help close it. When humans make decisions or respond to specific stimuli, our brains emit what's known as a P300 response. We can measure that response, and the evaluation of those responses is used primarily with medical patients who have some form of neurodegenerative disease or disability.

For 40-year-old Mao Ya, the facial recognition camera that allows access to her apartment house is simply a useful convenience. "If I am carrying shopping bags in both hands, I just have to look ahead and the door swings open," she said. "And my 5-year-old daughter can just look up at the camera and get in. It's good for kids because they often lose their keys." But for the police, the cameras that replaced the residents' old entry cards serve quite a different purpose.

Data breaches continue to come fast and furious. The latest major incident, Equifax, was one for the record books. Hackers obtained highly sensitive personal data on 145 million Equifax customers, including credit card numbers, Social Security numbers and driver's license numbers. So why do these outrageous breaches continue to happen --- and continue to get worse? One of the key reasons is that the bad guys keep getting better and better at what they do.

Among the many, many Democrats who will seek the party's presidential nomination in 2020, most probably agree on a handful of core issues: protecting DACA, rejoining the Paris climate agreement, unraveling President Trump's tax breaks for the wealthy. Only one of them will be focused on the robot apocalypse. That candidate is Andrew Yang, a well-connected New York businessman who is mounting a longer-than-long-shot bid for the White House. Mr. Yang, a former tech executive who started the nonprofit organization Venture for America, believes that automation and advanced artificial intelligence will soon make millions of jobs obsolete -- yours, mine, those of our accountants and radiologists and grocery store cashiers. He says America needs to take radical steps to prevent Great Depression-level unemployment and a total societal meltdown, including handing out trillions of dollars in cash.

In the middle of this live stream image of the car, on the center screen, are the words'Don't Panic'. This a reference to The Hitchhiker's Guide to the Galaxy, the 1979 book that was first in a series by Douglas Adams about an accidental space traveler, Arthur Dent Two of the Falcon Heavy's reusable boosters - both recycled from previous launches - returned minutes after lift-off for on-the-mark touchdowns at Cape Canaveral. Sonic booms rumbled across the region with the synchronised vertical landings. However, the craft's third and final booster missed its target - a drone ship in the Atlantic Ocean - by about 328 feet (100 metres). In a press conference after the historic launch, Musk said early reports show the rocket's central core'hit the water at 300 miles per hour (480kph) and sprayed the drone ship with shrapnel'.