For a long time, being out at sea meant being out of sight and out of reach. And all kinds of shenanigans went on as a result - countries secretly selling oil and other goods to countries they're not supposed to under international sanctions rules, for example, not to mention piracy and kidnapping. The problem is that captains can easily switch off the current way of tracking ships, called the Automatic Identification System (AIS), turning their vessels into "ghost ships". But now thousands of surveillance satellites have been launched into space, and artificial intelligence (AI) is being applied to the images they take. There's no longer anywhere to hide - even for ghost ships.

Embedded Computing Design recently conducted targeted industry research on the prevalence of machine learning and artificial intelligence in the development community. Much to our surprise, more than 75 percent of the respondents are already researching or using AI in one capacity or another. Now, AI and machine learning are not new, and the questions didn't specify whether respondents were indicating the use of time-honored techniques like principle component analysis, decision trees, etc. On the other hand, modern AI/ML is becoming synonymous with neural networking, so I took the results at face value. One important note, however, is that the respondents were evenly distributed geographically.

Rogue Drone Pilots Face A New Foe: Deep Learning AI In the wake of airspace-invading drones causing the precautionary shutdowns of three major airports--London's Gatwick in December and Heathrow in early January, along with Newark Liberty International, NJ last week--aviation authorities the world over are now considering the acquisition of commercial drone detection, tracking, and capture systems, in a bid to keep airliners and passengers safe. However, they could be jumping the gun. Experts are warning that today's counter-drone systems are no cure-all, as they cannot defend against some types of criminal drone threat--especially the most determined airspace saboteurs using home-built, customized drones that may not respond to conventional countermeasures. However, a number of ingenious ideas are now being investigated to shore up some of the gaps in functionality, and some of them are even based on the technology of the moment: deep learning. Aviation safety authorities worry about drones because they can penetrate cockpit windscreens, injuring the pilots, or risk starting inflight fires by breaching the fuel tanks in aircraft wings, or causing sudden engine failure during a critical part of flight like takeoff or landing, perhaps leading to a crash.

Winners Announced for the Zillow Prize For Spectrum's January issue, I wrote about the Zillow Prize competition, in which nearly 4,000 teams were pitted against one another in a quest to come up with a computerized algorithm or machine-learning system that could predict the future sale price of homes. Real-estate giant Zillow organized the competition in hopes of using what it learned from these teams to improve its own system of predicting home prices, something the company calls the "Zestimate." And today, Zillow has announced a winner: a team made up of Chahhou Mohamed of Morocco, Jordan Meyer of the United States, and Nima Shahbazi of Canada, whose predictions bettered the Zestimate by about 13 percent. Stan Humphries, chief analytics officer for the Zillow Group, in Seattle, says that he and his colleagues have learned an enormous amount from the winning team and others in the competition--thousands of people working for two years on the problem: "That's a huge help," says Humphries. Although he couldn't be too specific, Humphries shared that one area of insight was "how you combine various models in an ensemble approach."

Automation may cast a dark shadow over low-skill workers, but a new report suggests there's still hope for humans. The robots aren't taking over, yet: At least, that's the finding of a report by the World Bank that looked at data on global industrial jobs. The World Development Report 2019 says advanced economies have shed industrial jobs, but the rise of the industrial sector in East Asia has more than compensated for this loss, meaning overall numbers haven't changed. Jobs are relocating, not disappearing. The skills trends: What that does mean is that while the jobs may not be going away, if you don't live in the right place or have the right abilities, you could still be out of work.

It is 2019 and a good reason to write about robots! Robots and artificial intelligence machines are slowly entering the workplace, and your house. They are even more popular on social media and entertainment, you might even see an automated car being driven by … no one. Today you might do a double-take, but soon this might become the norm. Isaac Asimov's'Three Laws of Robotics' has been debated over and over.

It is known that the Langevin dynamics used in MCMC is the gradient flow of the KL divergence on the Wasserstein space, which helps convergence analysis and inspires recent particle-based variational inference methods (ParVIs). But no more MCMC dynamics is understood in this way. In this work, by developing novel concepts, we propose a theoretical framework that recognizes a general MCMC dynamics as the fiber-gradient Hamiltonian flow on the Wasserstein space of a fiber-Riemannian Poisson manifold. The "conservation + convergence" structure of the flow gives a clear picture on the behavior of general MCMC dynamics. We analyse existing MCMC instances under the framework. The framework also enables ParVI simulation of MCMC dynamics, which enriches the ParVI family with more efficient dynamics, and also adapts ParVI advantages to MCMCs. We develop two ParVI methods for a particular MCMC dynamics and demonstrate the benefits in experiments.

Generating explanation to explain its behavior is an essential capability for a robotic teammate. Explanations help human partners better understand the situation and maintain trust of their teammates. Prior work on robot generating explanations focuses on providing the reasoning behind its decision making. These approaches, however, fail to heed the cognitive requirement of understanding an explanation. In other words, while they provide the right explanations from the explainer's perspective, the explainee part of the equation is ignored. In this work, we address an important aspect along this direction that contributes to a better understanding of a given explanation, which we refer to as the progressiveness of explanations. A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation. As a result, such explanations are expected to be smoother and hence easier to understand. A general formulation of progressive explanation is presented. Algorithms are provided based on several alternative quantifications of cognitive effort as an explanation is being made, which are evaluated in a standard planning competition domain.

The combinations of joint atomic actions, which quickly becomes explosion in the number of discrete actions can intractable when M increases. However, a simple fix be efficiently addressed by a policy with factorized is to represent the joint distribution over discrete actions as distribution across action dimensions. We factorized across dimensions, so that the joint policy is still show that the discrete policy achieves significant tractable. As prior works have applied such discretization performance gains with state-of-the-art on-policy method in practice (OpenAI, 2018; Jaśkowski et al., 2018), optimization algorithms (PPO, TRPO, ACKTR) we aim to carry out a systemic study of such straightforward especially on high-dimensional tasks with complex discretization method in simulated environments, and show dynamics. Additionally, we show that an ordinal how they improve upon on-policy optimization baselines.

A question answering system (QA System) was developed that uses graph-pattern association rules on the YAGO knowledge base. The answer as output of the system is provided based on a user question as input. If the answer is missing or unavailable in the database, then graph-pattern association rules are used to get the answer. The architecture of this question answering system is as follows: question classification, graph component generation, query generation, and query processing. The question answering system uses association graph patterns in a waterfall model. In this paper, the architecture of the system is described, specifically discussing its reasoning and performance capabilities. The results of this research is that rules with high confidence and correct logic produce correct answers, and vice versa.