Had the severity grown to crisis levels, Lucas McDonald, a former TV weatherman who leads the chain's emergency operations, might have called in dozens of workers to support the handful who are posted at the division's command center in 24/7 shifts. The full-house team--typically assembled only a few times a year--would help coordinate relief efforts, adjust supply routes and disseminate information to affected stores, a playbook the company has perfected through two exceptionally hectic hurricane seasons. "Right now, we're having conversations with some of our merchants on when the right time to ship more supplies into places like Florida and the Southeast would be ahead of any possible redevelopment from Dorian after it makes its way through Hispaniola," McDonald says. Meanwhile, in Dallas, meteorologists at Southwest Airlines mapped out contingency plans for rerouting and canceling flights given various possible hurricane scenarios. And in the Atlanta nerve center of IBM-owned Weather Company, forecasters relayed storm data and analysis to corporate clients like State Farm, which in turn used it to inform IBM Watson conversational ad units that spread safety information.

Artificial intelligence and machine learning may be ideal for picking up the day-to-day tasks of running enterprises, but still fall flat when it comes to innovation or reacting to unforeseen or one-off events. While enterprise-grade AI is still a ways off, it's incumbent on business and IT leaders to start piloting and exploring the advantages AI potentially offers. That's the word coming out of a recent report from the MIT Task Force on the Work of the Future, which looked at AI as part of a broad range of changes sweeping the employment scene and workplace. "We are a long way from AI systems that can read the news, re-plan supply chains in response to anticipated events like Brexit or trade disputes, and adapt production tasks to new sources of parts and materials," state the report's authors, David Autor of the National Bureau of Economic Research, along with David Mindell and Elisabeth Reynolds, both with MIT. For starters, data – the fuel that propels AI decision-making – is not ready for the leap. Most successful AI initiatives to date are based on machine learning (ML) systems, which depend on large data sets.

WAUKESHA, Wis.--(BUSINESS WIRE)--GE Healthcare today announced the Food and Drug Administration's 510(k) clearance of Critical Care Suite, an industry-first collection of artificial intelligence (AI) algorithms embedded on a mobile X-ray device. Built in collaboration with UC San Francisco (UCSF), using GE Healthcare's Edison platform, the AI algorithms help to reduce the turn-around time it can take for radiologists to review a suspected pneumothorax, a type of collapsed lung. "X-ray – the world's oldest form of medical imaging – just got a whole lot smarter, and soon, the rest of our offerings will too," says Kieran Murphy, President & CEO, GE Healthcare. "GE Healthcare is leading the way in the creation of AI applications for diagnostic imaging and taking what was once a promise and turning it into a reality. By integrating AI into every aspect of care, we will ultimately improve patient outcomes, reduce waste and inefficiencies, and eliminate costly errors. Critical Care Suite is just the beginning."

However, he argues, these are not enough to counter accelerating technological changes allowing greater intrusions of privacy and he calls for a worldwide protest movement, similar to those on climate change. He added: "You have to be ready to stand for something if you want it to change. "That is what I hope this book (Permanent Record) will help people come to decide for themselves." The revelation coincides with the GSMA's announcement that the AI market is projected to reach $70 billion by 2020.

The attack, which knocked out more than half of the Saudi oil output, may force the U.S. to tap into its own oil reserves to keep the markets well supplied. President Trump on Sunday suggested U.S. investigators had "reason to believe" they knew who launched crippling attacks against a key Saudi oil facility, and vowed that America was "locked and loaded depending on verification." While he did not specify who he believed was responsible for Saturday's drone attacks, U.S. investigators previously have pointed the finger at Iran. "Saudi Arabia oil supply was attacked. There is reason to believe that we know the culprit, are locked and loaded depending on verification, but are waiting to hear from the Kingdom as to who they believe was the cause of this attack, and under what terms we would proceed!" the president tweeted. Earlier Sunday, Trump authorized the use of emergency oil reserves in Texas and other states after Saudi oil processing facilities were attacked, sparking fears of a spike in oil prices when markets reopen Monday.

As intelligent systems are increasingly capable of performing their tasks without the n eed for continuous human input, direction, or supervision, new human - machine interaction concepts are needed. A promising approac h to this end is human - agent teaming, which envisions a novel interaction form where humans and machines behave as equal team partners . This paper presents an overview of the current state of the art in human - agent teaming, including the analysis of human - agent teams on five dimensions; a framework describing important teaming functionalities; a technical architecture, called SAIL, supporting social human - agent teaming through the modular implementation of the human - agent teaming functionalities; a technica l implementation of the architecture; and a proof - of - concept prototype created with the framework and architecture. We conclude this paper with a reflection on where we stand and a glance into the future showing the way forward .

In this paper, the problem of optimizing the deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs. Therefore, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem whose goal is to minimize the total transmit power while meeting the illumination and communication requirements of users. To solve this problem, an algorithm based on the machine learning framework of gated recurrent units (GRUs) is proposed. Using GRUs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. In order to reduce the complexity of the prediction algorithm while accurately predicting the illumination distribution, a Gaussian mixture model (GMM) is used to fit the illumination distribution of the target area at each time slot. Based on the predicted illumination distribution, the optimization problem is proved to be a convex optimization problem that can be solved by using duality. Simulations using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 22.1% reduction in transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution. The results also show that UAVs must hover at areas having strong illumination, thus providing useful guidelines on the deployment of VLC-enabled UAVs.

Complex networks are often either too large for full exploration, partially accessible or partially observed. Downstream learning tasks on incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks and given resource collection constraints are of great interest. In this paper we formulate the task-specific network discovery problem in an incomplete network setting as a sequential decision making problem. Our downstream task is vertex classification.We propose a framework, called Network Actor Critic (NAC), which learns concepts of policy and reward in an offline setting via a deep reinforcement learning algorithm. A quantitative study is presented on several synthetic and real benchmarks. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms.

As a result of social network popularity, in recent years, hate speech phenomenon has significantly increased. Due to its harmful effect on minority groups as well as on large communities, there is a pressing need for hate speech detection and filtering. However, automatic approaches shall not jeopardize free speech, so they shall accompany their decisions with explanations and assessment of uncertainty. Thus, there is a need for predictive machine learning models that not only detect hate speech but also help users understand when texts cross the line and become unacceptable. The reliability of predictions is usually not addressed in text classification. We fill this gap by proposing the adaptation of deep neural networks that can efficiently estimate prediction uncertainty. To reliably detect hate speech, we use Monte Carlo dropout regularization, which mimics Bayesian inference within neural networks. We evaluate our approach using different text embedding methods. We visualize the reliability of results with a novel technique that aids in understanding the classification reliability and errors.

ROBOT WARS AND SKYNET: IS SCI-FI BECOMING OUR REALITY? – PART 1 My Interest In Robotics And Nanotechnology, US Military UAVs/Drones And Robotic Vehicles, The Civilian Casualties Controversy, DARPA, The Darpa Urban Challenge, Roboticist William L. Whittaker, The Lunar X Prize Competition, The Positive And Negative Contributions Of Modern Technology, Wicked Heart Of Man, George W. Bush And Illegal Invasion Of Iraq, Destruction Wrought In Iraq, Americans And Body Bags, American Casualties From The Iraq War, DARPA's Aim: Protect People On The Battlefield, Remote Killing And Robots On The Battlefield Are The Wave Of The Future, Masters Of Science Fiction: Jerry Was A Man, Future Wars Fought with Automated Machines, Secret Robot Wars With No More Accountability To The Public, Callous American Public, America Was Founded On War, America Survives And Expands Her Empire Through War And Shrewd Economic Policies, Government Propaganda Machines And Malleable Gullible Public, Shock And Awe Wars, Are AI-Enabled Robot Wars In Our Future?, Current Advancements In Robotics, "Terminator" Movies And SkyNet, Will Artificial Intelligence Become A Threat?, Huge Investment in AI By The U.S. Military, The Joint Artificial Intelligence Center And Super Soldiers, Warnings From Stephen Hawking And Elon Musk, Go Down Fighting ROBOT WARS AND SKYNET: IS SCI-FI BECOMING OUR REALITY? – PART 2