As climate change escalates, the impact of natural disasters is likely to become less predictable. To encourage the use of machine learning for building damage assessment this week, Carnegie Mellon University's Software Engineering Institute and CrowdAI -- the U.S. Department of Defense's Joint AI Center (JAIC) and Defense Innovation Unit -- open-sourced a labeled data set of some of the largest natural disasters in the past decade. Called xBD, it covers the impact of disasters around the globe, like the 2010 earthquake that hit Haiti. "Although large-scale disasters bring catastrophic damage, they are relatively infrequent, so the availability of relevant satellite imagery is low. Furthermore, building design differs depending on where a structure is located in the world. As a result, damage of the same severity can look different from place to place, and data must exist to reflect this phenomenon," reads a research paper detailing the creation of xBD.

From a trader's point of view, there is one commodity that is worth infinitesimally more than any other. And it's not cutting-edge technology, advanced technical analysis, or profound macroeconomic insight – although these are undoubtedly hugely valuable – it's information. Not just any information – after all, the world is filled with more information than even the most powerful computers could hope to store, and the most intelligent brains could hope to begin to comprehend. No, there's one type of information that has the potential to give traders a bigger edge than any other, and that's the latest information. Information that the rest of the market has yet to factor into their equations.

Deepfakes have only been around for a few years; the first known videos, posted to Reddit in 2017, featured celebrities' faces swapped with those of porn stars. Shales got interested in making them himself early this year, and in February released his first deepfake: in it, he plastered the face of actor Nicolas Cage onto Elon Musk's body to make it appear as if Cage, rather than Musk, was smoking marijuana during a podcast interview with comedian Joe Rogan. Shales admits it isn't a great video; the resulting face is more of a morph than a swap, he said. And the voice is still unmistakably Musk's. But Shales kept going and quickly got better.

In a recent tweet, presidential candidate Bernie Sanders confused the basic economic concepts of revenue and profit. Sanders tweeted that "[t]he video game industry made $43 billion in revenue last year. The workers responsible for that profit deserve to collectively bargain as part of a union." Of course, the $43 billion was revenue, not profit - and there is a difference. Profit is what's left after a business pays the costs of generating revenue.

Measuring just 10.5 15.5 mm including antenna, the SparkFun Artemis module is intended to bridge the gap from "maker to market," and from prototype to product. The module has all of the support circuitry needed to make use of the Apollo 3 processor, but has been designed so that routing to the module can be done with lower-cost 2-layer PCBs with an 8 mil trace clearance. That means it can be easily integrated into maker projects, with a short run of circuit boards sourced from somewhere like OSH Park, or picked up in tape and reel quantities used in a production product. Today's release is the'engineering' version of the module and comes without FCC approval or a CE mark, however a fully FCC/CE approved version of the module with an RF shield is set to ship in tape and reel quantities as soon as next month. Traditionally known as a hobbyist supplier, the new Artemis Module is a big departure for SparkFun. This is the first time they've built and shipped an embedded module intended to scale to production of consumer product volumes.

LONDON – Following the launch of its AI inference chip last year, Habana Labs (Tel-Aviv, Israel) has unveiled an AI training chip built on the same architecture that can outpace the incumbent technology by a substantial margin, and features on-chip RoCE (remote direct memory access over Converged Ethernet) communications for scalability. While the company's inference chip, Goya, set records for ResNet-50 inference back in September 2018, the new training chip, Gaudi, offers similar high performance. Gaudi can process 1650 images per second at a batch size of 64 when training a ResNet-50 network, which Habana claims is a new world record for this benchmark. This throughput is delivered at 140W power consumption, also a substantial advantage versus competing solutions, according to the company. Impressive, but is Habana's architecture designed specifically to beat the ResNet-50 benchmark, or will it offer similar throughput advantages for other types of neural networks?

NASHIK: The city traffic branch has collected fine of Rs 5.10 lakh from 2,550 motorists in the past five months for violating the no-entry zone at the Indiranagar underpass on the Mumbai Agra highway. The traffic department had made mandatory for only Govind Nagar bound motorists to use the underpass while the Indiranagar bound motorists were told to take a detour to reach the place. But still many motorists didn't pay heed to the rules and were caught violating by the CCTV cameras put up inside the underpass. On January 26 this year, the city police installed an artificial intelligence (AI) system at the Indiranagar underpass to keep check on motorists violating the underpass norms. Senior police officials said that the new application installed at the underpass was not only helping streamline the vehicular traffic inside the structure but also helping in spotting the defaulters so that they can be fined.

When Ian Goodfellow dreamt up the idea of Generative Adversarial Networks (GANs) over a mug of beer back in 2014, he probably didn't expect to see the field advance so fast: In case you don't see where I'm going here, the images you just saw were utterly, undeniably, 100% … fake. Also, I don't mean these were photoshopped, CGI-ed, or (fill in the blanks with whatever Nvidia's calling their fancy new tech at the moment). I mean that these images are entirely generated through addition, multiplication, and splurging ludicrous amounts of cash on GPU computation. The algorithm that makes is stuff work is called a generative adversarial network (which is the long way of writing GAN, for those of you still stuck in machine learning acronym land), and over the last few years, there have been more innovations dedicated to making it work than there have been privacy scandals at Facebook. Summarizing every single improvement to the 2014 vanilla GANs is about as hard as watching season 8 of Game of Thrones on repeat. I'm not going to explain concepts like transposed convolutions and Wasserstein distance in detail. Instead, I'll provide links to some of the best resources you can use to quickly learn about these concepts so that you can see how they fit into the big picture. If you're still reading, I'm going to assume that you know the basics of deep learning and that you know how convolutional neural networks work.

Considered as the 4th Industrial Revolution, Artificial Intelligence (AI) has become a reality in today's world, especially in the military. Experts and academicians have emphasized the importance of AI for a long time. Furthermore, world leaders, including Obama, Trump, Xi, and Putin, have all made important statements that bring to the fore the significance of AI which can be summarized with what Putin stated on September 2017: whoever becomes the leader in AI, will rule the world. This analysis provides a short introduction on what AI is, how it has evolved until today and how it will change the nature of warfare. It then assesses why states invest in AI to later turn to the case of the U.S. and China. For both states, the main official documents and statements are analyzed, the bureaucratic structures that work on AI are presented and finally examples of how the U.S. and China are applying AI in the military are provided.

There exist, on the internet, any number of videos that show people doing things they never did. These videos are called deepfakes, and they're made using a particular kind of AI. Inevitably enough, they began in porn – there is a thriving online market for celebrity faces superimposed on porn actors' bodies – but the reason we're talking about them now is that people are worried about their impact on our already fervid political debate. Those worries are real enough to prompt the British government and the US Congress to look at ways of regulating them. The video that kicked off the sudden concern last month was, in fact, not a deepfake at all.