Pornographic videos that used new software to replace the original face of an actress with that of a celebrity are being deleted by a service that hosted much of the content. San Francisco-based Gfycat has said it thinks the clips are "objectionable". The creation of such videos has become more common after the release of a free tool earlier this month that made the process relatively simple. The developer says FakeApp has been downloaded more than 100,000 times. It works by using a machine-learning algorithm to create a computer-generated version of the subject's face. To do this it requires several hundred photos of the celebrity in question for analysis and a video clip of the person whose features are to be replaced.

You might be using your drone (or thinking about getting a drone) for epic vacation shots and ultra-romantic wedding videos, but you should be thinking bigger. What if, instead of taking pictures of you, your drone could help you monitor hundreds of acres of crops? And what if it could fix those flaws or water those crops as soon as it spotted them? Just as self-driving cars could fundamentally rearchitect the way cities work, drones have a disruptive potential that's hard to overstate. They could change the way people and goods are transported (where we're going, we don't need roads!), eliminate some jobs and create others, and upend the way we think about distance. Drones could bring the internet to people who don't have it, deliver food and medicine to people who need it, and cast a watchful eye over anyone and everyone. Drones are even inspiring new sports!

According to an article published two days ago in the Chinese military's official journal, a team of researchers from the Chinese military university National University of Defense Technology (NUDT) successfully tested, in early December, a swarm of " several tens of fixed-wing drones. The text has given little detail – it is only learned that the test had the objective of evaluating autonomous flight technologies in training, and the ability of the swarm to conduct a reconnaissance mission to the above a certain area. The project and development work is led by Professor SHEN Lin Cheng (沈 林 成), PhD supervisor and Chair of the NUDT Institute of Artificial Intelligence Sciences. "The team has been working for nine months on the preparation of this swarm test, sometimes we have to do a hundred test flights a day," says the former director of the institute of electro-mechanical engineering and automation, "We have precise short, medium and long term objectives, which are consistent with those set by the government on the modernization of the Chinese armed forces by 2020, 2035 and 2050." Despite "rudimentary" testing site conditions, the NUDT research team of an average age of 30 years has achieved a breakthrough in the areas of parallel perception, behavioral intention prediction, and Autonomous flight control focused on the handling of random incidents, says the People's Liberation Army Daily article.

CAPE TOWN (Reuters) - South Africa's parliament said on Wednesday that President Jacob Zuma will deliver the state-of-the-nation address as planned on Feb 8 despite calls from within the ruling party and the opposition for the scandal-plagued leader.

Agents found Jorge Rivera in possession of 13 pounds of methamphetamine in Aug. 2017 when he was trying to use a drone to smuggle drugs across the border. A 25-year-old man who was previously arrested after using a drone to smuggle drugs across the U.S.-Mexico border has been sentenced to 12 years in prison, border officials announced. A jury sentenced Jorge Rivera on Wednesday after he was convicted last week of trying to traffic 13 pounds of methamphetamine into the United States during the summer, according to a release from the U.S. Customs and Border Protection. Rivera was detained on the night of Aug. 8, 2017 after a border patrol agent saw a drone flying across the border near the San Ysidro Port of Entry, the original arrest report said. An officer later reportedly located the suspect who was operating the drone and found him in possession of a bag containing "multiple plastic-wrapped packages containing methamphetamine."

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Computers can beat chess champions, simulate star explosions, and forecast global climate. They are also being trained as infallible problem-solvers and fast learners. And now, physicists at the U.S. Department of Energy's Lawrence Berkeley National Laboratory and their collaborators have demonstrated that computers are ready to tackle the universe's greatest mysteries. The team used thousands of images from simulated high-energy particle collisions to train computer networks to identify important features. The researchers programmed powerful arrays known as neural networks to serve as a sort of hive-like digital brain in analyzing and interpreting the images of the simulated particle debris left over from the collisions.

The basic fact is that technology eliminates jobs, not work. It is the continuous obligation of economic policy to match increases in productive potential with increases in purchasing power and demand. Otherwise the potential created by technical progress runs to waste in idle capacity, unemployment, and deprivation. The fear that machines will replace human labor is a durable one in the public mind, from the time of the Luddites in the early 19th century. Yet most economists have viewed "the end of humans in jobs" as a groundless fear, inconsistent with the evidence.

Microsoft reported encouraging growth Wednesday in what is becoming its most important business: cloud computing. The company, which has focused on the cloud to lead it into the post-PC era, had a lot to celebrate as it looks to catch up to Amazon.com Cloud growth drove the bulk of Microsoft's success in its latest quarter, solidifying its second-place position in the market. The unit containing its Azure cloud computing division made $7.8 billion in the company's latest quarter. While Microsoft doesn't provide dollar figures for each product, it said that revenue from Azure itself jumped 98 percent.

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then build a bayesian causal model using these extracted concepts as variables in order to explain image classification. Finally, we use this causal model to identify and visualize features with significant causal influence on final classification.