Worries about deep fakes--machine-manipulated videos of celebrities and world leaders purportedly saying or doing things that they really didn't--are quaint compared to a new threat: doctored images of the Earth itself. China is the acknowledged leader in using an emerging technique called generative adversarial networks to trick computers into seeing objects in landscapes or in satellite images that aren't there, says Todd Myers, automation lead and Chief Information Officer in the Office of the Director of Technology at the National Geospatial-Intelligence Agency. "The Chinese are well ahead of us. This is not classified info," Myers said Thursday at the second annual Genius Machines summit, hosted by Defense One and Nextgov. "The Chinese have already designed; they're already doing it right now, using GANs--which are generative adversarial networks--to manipulate scenes and pixels to create things for nefarious reasons."

The rapid progress and development in artificial intelligence (AI) is prompting desperate speculation about its dual-use applications and security risks. From autonomous weapons systems (AWS) to facial recognition technology to decision-making algorithms, each emerging application of artificial intelligence brings with it both good and bad. It is this dual nature of artificial intelligence technology that is bringing enormous security risks to not only individuals and entities across nations: its government, industries, organizations, and academia (NGIOA) but also the future of humanity. The reality is that any new AI innovation might be used for both beneficial and harmful purposes: any single algorithm that may provide important economic applications might also lead to the production of unprecedented weapons of mass destruction on a scale that is difficult to fathom. As a result, the concerns about artificial intelligence-based automation are growing.

New robots are about to create a buzz in space. Three hovering robots, dubbed Astrobees, will be heading to the International Space Station to help astronauts conduct research, do maintenance and track inventory, NASA said Tuesday in a blog post. Our @Space_Station crew is gaining 3 robotic helpers -- Astrobees! These cube-shaped robots will stay busy, flying around the orbital lab assisting basic tasks & allowing our astronauts more time for science. But how will they do it?

The US Food and Drug Administration has announced that it is preparing to regulate AI systems that can update and improve themselves as they gorge on more training data. The announcement: The agency released a white paper proposing a regulatory framework to decide how medical products that use AI should seek approval before they can go on the market. It is the biggest step the FDA has taken to date toward formalizing oversight of products that use machine learning (ML). The challenge: Machine-learning systems are tricky to regulate because they can continuously update and improve their performance through new training data. In instances where the FDA has approved ML-based medical software before, it has required the algorithms to be "frozen" before commercial deployment and to go through a reapproval process when they are changed.

The future of'deep fake' technology could be much worse than doctored videos of celebrities and politicians. U.S. military experts are raising concerns about a new variant of deep fakes, or videos that use AI to make subjects appear to say or do something they really didn't, that could involve doctored satellite images of the Earth, according to Defense One. It comes as social media giants, researchers and other experts have been working to outsmart deep fake videos. U.S. military experts are raising concerns about a new variant of deep fakes, or videos that use AI to manipulate its subjects, that could involve doctored satellite images of the Earth Deepfakes are so named because they utilize deep learning, a form of artificial intelligence. They are made by feeding a computer an algorithm, or set of instructions, lots of images and audio of a certain person.

Silicon Valley's use of nontraditional employment arrangements, where workers typically aren't afforded the same privileges as employees, has grown faster than full-time jobs, even as tech giants come under fire for their treatment of Uber drivers, Google cafeteria workers, or Facebook content moderators. But after sustained protest from contractors and employees, Google said Tuesday it will require outside companies that supply it labor to offer better working conditions, including comprehensive health care, 12 weeks of parental leave, and a $15-per-hour minimum wage. Google's policy change arrives amid a wider reckoning over Silicon Valley's impact, from income inequality to workers rights. Last week, thousands of Uber drivers protested an abrupt move to cut per-mile fares by 25 percent, as the company prepares to go public; that was followed by an anonymous letter from an Uber engineer urging employees to support drivers. Days before Lyft's IPO, a California lawmaker declined to exempt Uber, Lyft, and other gig-economy tech companies from a bill that would limit employers' use of independent contractors and force them to classify more workers as employees, making them eligible for overtime, Social Security, unemployment, disability insurance, and other benefits.

Researchers discovered hundreds of millions of Facebook users' data was left unprotected once again, this time on Amazon's servers. The information exposed was stuff like names, passwords, comments, interests, and likes. The tl;dr: Facebook doesn't seem to have much control over what third parties do with your data, basically ever, so you might want to lock down those privacy settings. President Trump has hosted everyone from foreign dignitaries to sitting members of Congress at his home away from home--Mar-a-Lago. But after a woman was arrested for sneaking in this week, it raised the question: How safe is this place where Donald Trump conducts major presidential business?

The intelligence explosion hypothesis (for example, a technological singularity) is roughly the hypothesis that accelerating knowledge or technological growth radically changes humanity. While 20th-century figures are commonly credited as the first discoverers of the hypothesis, I assert that Nicolas de Condorcet, the 18th-century mathematician, is the earliest to (1) mathematically model an intelligence explosion, and (2) present an accelerating historical worldview, and (3) make intelligence explosion predictions that were restated centuries later. Condorcet provides insights on how ontology and social choice can help resolve value alignment.

The field of image reconstruction has undergone four waves of methods. The first wave was analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. The second wave was iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. The third wave of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. The fourth wave of methods replaces mathematically designed models of signals and processes with data-driven or adaptive models inspired by the field of machine learning. This paper reviews the progress in image reconstruction methods with focus on the two most recent trends: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.

Submissions for HCOMP-19 Are Due in June! The Seventh AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2019) will be held October 28-30 at Skamania Lodge in Washington State near the Columbia Gorge River, just 45 minutes from Portland, Oregon. This year is the 10-year anniversary of the very first HCOMP workshop in Paris, and to celebrate, there will be special events, talks, and panels throughout the conference. HCOMP is the premier venue for disseminating the latest research findings on crowdsourcing and human computation. While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in inviting, fostering, and promoting broad, interdisciplinary research.