A company's plan to open a so-called sex robot brothel in Houston has been short circuited by city leaders. Houston's City Council on Wednesday updated one local ordinance to specifically ban individuals from having sex with an'anthropomorphic device,' a device that resembles a human being, at a sexually-oriented business. The company, Canada-based KinkySdollS, had wanted to open a'love dolls brothel' in Houston in which people would be able to use its human-like dolls - a concept that that has drawn comparisons to the science fiction series'Westworld.' KinkySdollS says its human-like dolls (above), which can speak and feel warm to the touch, are available for sale or rent. It would have been the company's second location. The first location opened in 2017 in Toronto.

Abstract-- Small unmanned aircraft can help firefighters combat wildfires by providing real-time surveillance of the growing fires. However, guiding the aircraft autonomously given only wildfire images is a challenging problem. This work models noisy images obtained from on-board cameras and proposes two approaches to filtering the wildfire images. The first approach uses a simple Kalman filter to reduce noise and update a belief map in observed areas. The second approach uses a particle filter to predict wildfire growth and uses observations to estimate uncertainties relating to wildfire expansion. The belief maps are used to train a deep reinforcement learning controller, which learns a policy to navigate the aircraft to survey the wildfire while avoiding flight directly over the fire. Simulation results show that the proposed controllers precisely guide the aircraft and accurately estimate wildfire growth, and a study of observation noise demonstrates the robustness of the particle filter approach.

Based on MacKay's information theoretic model of supervised machine learning [23], this article discusses how to practically estimate the maximum size of a neural network given a training data set. First, we present four easily applicable rules to analytically determine the capacity of neural network architectures. This allows the comparison of the efficiency of different network architectures independently of a task. Second, we introduce and experimentally validate a heuristic method to estimate the neural network capacity requirement for a given dataset and labeling. This allows an estimate of the required size of a neural network for a given problem. We conclude the article with a discussion on the consequences of sizing the network wrongly, which includes both increased computation effort for training as well as reduced generalization capability.

AT&T* and Dedrone, a San Francisco-based drone detection technology startup, are teaming up on a drone detection solution that helps protect military bases, venues, cities, and businesses, from malicious drones. As drones become more prominent, so does the misuse of the emerging technology. According to the Federal Aviation Administration¹, people purchased roughly 3 million drones worldwide in 2017. More than 1 million drones have been registered in the U.S. This creates a new avenue and threat vector for the community.

Brandon Alexander would like to introduce you to Angus, the farmer of the future. He's heavyset, weighing in at nearly 1,000 pounds, not to mention a bit slow. But he's strong enough to hoist 800-pound pallets of maturing vegetables and can move them from place to place on his own. Sure, Angus is a robot. But don't hold that against him, even if he looks more like a large tanning bed than C-3PO.

Brandon Alexander would like to introduce you to Angus, the farmer of the future. He's heavyset, weighing in at nearly 1,000 pounds, not to mention a bit slow. But he's strong enough to hoist 800-pound pallets of maturing vegetables and can move them from place to place on his own. Sure, Angus is a robot, but don't hold that against him, even if he looks more like a large tanning bed than C-3PO. Arobotic arm lifts plants being grown at Iron Ox, a robotic indoor farm, in San Carlos, Calif.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule-making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Because the paragraph is written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime targets.

With the 19th International Conference on Artificial Intelligence in Education (AIED) being held later this month, it might be a good time to start thinking about the very possible future of AI in schools and what it might mean for your children--both the good and the bad. If you're a parent or in the education field, you will no doubt be up to date on the ways that education in public schools and universities across the nation have already been steadily adapting towards a more technology-driven and data-producing form. Gone are the dusty chalkboards, the endless piles of handwritten essays, the class trips to the magical and always-too-warm computer lab, and those annoying moments when "that" student decides to spend 25 minutes staring out of the window while "using the pencil sharpener." Instead, students across the nation are utilizing Google's Chromebooks and collaborating on shared documents. What's more, they're learning to code as a basic element of their curriculum.

Donald Trump is texting everyone in the US the exact same message. "THIS IS A TEST of the National Wireless Emergency Alert System," the message will begin. Almost every phone in the country should receive the message – making a sound when they get it, even if they are on mute, and with notifications even showing on smart watches – and they should get it only once. It's possible some will get it later, since it is being broadcast for half an hour. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

For years, the Environmental Protection Agency's regulation of radiation, carcinogens, and other toxic chemicals has been based on the cautious scientific reasoning that considers even slight exposure to toxins potentially risky to public health. From that premise, the EPA has assessed a wide range of pollution, including lung-clogging particulate matter, Superfund cleanup, water treatment, radiation exposure, and as well as risk assessments for carcinogens like benzene. That time-honored approach may be changing because of easy-to-overlook phrasing within a paragraph buried in the proposed "Strengthening Transparency In Regulatory Science Rule," a regulation that will bar the EPA from considering a wide range of scientific studies in its rule making. With a few sentences buried in the seven-page Federal Register text, the EPA is opening the door to a new scientific approach that--in a worst-case scenario--could further relax regulations because of the assumption that a little pollution is actually beneficial. Some scientists have considered the implications of this paragraph and described a whole array of potential problems to Mother Jones. Written in incredibly vague language, most scientists were unable to explain which pollutants or regulations were the prime target.