Fox News Flash top headlines for Sept. 12 are here. Check out what's clicking on Foxnews.com A bio-inspired robot can use water from the environment to launch itself into the air, British researchers revealed. The robot can travel 85 feet through the air after taking off and researchers believe it could be used to collect samples in hazardous or otherwise cluttered environments, such as during a major flood. Researchers from the Aerial Robotics Laboratory at Imperial College London devised a system that requires only 0.2 grams of calcium carbide powder in a combusion chamber, with the only moving part being a small pump that delivers water from the environment where the robot sits.

We study fact-checking in this paper, which aims to verify a textual claim given textual evidence (e.g., retrieved sentences from Wikipedia). Existing studies typically either concatenate retrieved sentences as a single string or use feature fusion on the top of features of sentences, while ignoring semantic-level information including participants, location, and temporality of an event occurred in a sentence and relationships among multiple events. Such semantic-level information is crucial for understanding the relational structure of evidence and the deep reasoning procedure over that. In this paper, we address this issue by proposing a graph-based reasoning framework, called the Dynamic REAsoning Machine (DREAM) framework. We first construct a semantic-level graph, where nodes are extracted by semantic role labeling toolkits and are connected by inner- and inter- sentence edges. After having the automatically constructed graph, we use XLNet as the backbone of our approach and propose a graph-based contextual word representation learning module and a graph-based reasoning module to leverage the information of graphs. The first module is designed by considering a claim as a sequence, in which case we use the graph structure to re-define the relative distance of words. On top of this, we propose the second module by considering both the claim and the evidence as graphs and use a graph neural network to capture the semantic relationship at a more abstract level. We conduct experiments on FEVER, a large-scale benchmark dataset for fact-checking. Results show that both of the graph-based modules improve performance. Our system is the state-of-the-art system on the public leaderboard in terms of both accuracy and FEVER score.

AI is constantly evolving and reaching new milestones all the time. Since nearly two-thirds of Americans rely on Twitter as their primary news source, we've decided to share the top influencers that Lionbridge AI follows to stay in the know. If you don't already, give us a follow us too @LionbridgeAI for daily updates on what's new, from Lionbridge and the AI and machine learning industry at large. AI For Everyone is now available on @Coursera! This course will help non-engineers and engineers work together to leverage AI capabilities and build an AI strategy. If you want your company to embrace AI, this is the course to get your CEO to take! https://t.co/bzpf1ed8DL

Chooch is rapidly becoming the new standard in AI training, labeling, hashtags, and monetization for archived and live digital assets. Developers can use Chooch's general APIs or train Chooch for a mission-critical activity involving video and image content. And Chooch inferences and interprets content and the environment based on Chooch's trained perceptions of the physical world. Chooch can be used by plugging it into a platform. And it can be trained by simply providing a concept, pointing the platform into a direction, or feeding your own data.

When Apple introduced its triple-camera iPhone this week, marketing chief Phil Schiller waxed on about the device's ability to create the perfect photograph by weaving it together with eight separate exposures captured before the main shot, a feat of "computational photography mad science." "When you press the shutter button it takes one long exposure, and then in just one second the neural engine analyzes the fused combination of long and short images, picking the best among them, selecting all the pixels, and pixel by pixel, going through 24 million pixels to optimize for detail and low noise," Schiller said, describing a feature called "Deep Fusion" that will ship later this fall. It was the kind of technical digression that, in years past, might have been reserved for design chief Jony Ive's narration of a precision aluminum milling process to produce the iPhone's clean lines. But in this case, Schiller, the company's most enthusiastic photographer, was heaping his highest praise on custom silicon and artificial intelligence software. The technology industry's battleground for smartphone cameras has moved inside the phone, where sophisticated artificial intelligence software and special chips play a major role in how a phone's photos look.

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Originally published at https://rsomani95.github.io.Visit the link for a better formatted, interactive version of the post with many more images. Analysing cinema is a time-consuming process. In the cinematography domain alone, there's a lot of factors to consider, such as shot scale, shot composition, camera movement, color, lighting, etc. Whatever you shoot is in some way influenced by what you've watched. There's only so much one can watch, and even lesser that one can analyse thoroughly.

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Did you know that half of today's jobs will become obsolete in the next 15 years? According to Kai-fu Lee, one of China's leading artificial intelligence (AI) experts, nearly 50% of all jobs today will be automated by 2034. As technology becomes more advanced many jobs traditionally performed by humans are being taken up by machines. Just a few years ago, self-driving vehicles were pure science fiction. Today, thanks to massive advancements in technology, there is a global movement to make autonomous vehicles a norm on our roads.

Is there more to AI than automation and data processing? And now that some of the more basic functions of AI have matured, we're going to see a sharp increase in the sophistication of AI, as it becomes more and more human. The following is a brief look at where the enterprise is when it comes to AI, and where we're about to start heading. As I noted above, there are some basic functions of AI that most companies have already adopted, and that we as users can feel confident about when it comes to functionality. Those include things like personalization (personalizing ads, email messaging, auto-texts, etc.) and recommendations (such as those you see on apps like Spotify, Netflix, or in your Amazon shopping card), and other martech, which is now widely available aaS for just about any size enterprise.